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BT 227.982 767.476 Td /F1 9.8 Tf [(Emergence: Complexity and Organization. Emergence: Complexity and Organization.)] TJ ET
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BT 26.250 653.490 Td /F1 9.8 Tf [(Moldoveanu M, Moldoveanu M. Organizations as Universal Computing Machines: Rule systems, computational equivalence, )] TJ ET
BT 26.250 641.585 Td /F1 9.8 Tf [(and organizational complexity. Emergence: Complexity and Organization. 2008 Mar 31 [last modified: 2016 Nov 30]. Edition 1. )] TJ ET
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BT 26.250 600.578 Td /F4 12.0 Tf [(Abstract)] TJ ET
BT 26.250 580.623 Td /F1 9.8 Tf [(Organizations can be—and, have been—modeled as rule-based systems. On a reductive view, the resulting models depict )] TJ ET
BT 26.250 568.719 Td /F1 9.8 Tf [(organizations as cellular automata \(CA\) that carry out computations whose inputs are the initial and boundary conditions of a )] TJ ET
BT 26.250 556.814 Td /F1 9.8 Tf [(lattice of elements co-evolving according to deterministic interaction rules and whose outputs are the final states of the CA )] TJ ET
BT 26.250 544.909 Td /F1 9.8 Tf [(lattice. We use such models to refine the notion of the complexity of an organizational phenomenon and entertain the notion of )] TJ ET
BT 26.250 533.004 Td /F1 9.8 Tf [(an organization as a universal computer that can support a wide variety of CA to suggest ways in which CA-derived insights can )] TJ ET
BT 26.250 521.100 Td /F1 9.8 Tf [(inform organizational analysis. We examine the informational and computational properties of CA rules and the implications of )] TJ ET
BT 26.250 509.195 Td /F1 9.8 Tf [(the trade-off between their informational and computational complexity to the problem of ‘organizational design’ and show how )] TJ ET
BT 26.250 497.290 Td /F1 9.8 Tf [(the discovery of operational rules could proceed in the context of an empirical framework.)] TJ ET
BT 26.250 460.688 Td /F4 12.0 Tf [(Introduction: Organizations as rule-following systems)] TJ ET
BT 26.250 440.733 Td /F1 9.8 Tf [(Ever since the early work of Simon \(1947\), March and Simon \(1958\) and Cyert and March \(1963\) organizations have been )] TJ ET
BT 26.250 428.829 Td /F1 9.8 Tf [(frequently modeled as rule-following systems. Standard operating procedures \(Cyert & March, 1963\), routines \(Nelson & Winter, )] TJ ET
BT 26.250 416.924 Td /F1 9.8 Tf [(1982\), socio-cognitive and cognitive scripts \(Newell, 1990\) and algorithms \(Moldoveanu & Bauer, 2002\) have all been posited )] TJ ET
BT 26.250 405.019 Td /F1 9.8 Tf [(as ways of understanding the dynamics of organizations through the decomposition of macro-level phenomena that seem to )] TJ ET
BT 26.250 393.114 Td /F1 9.8 Tf [(follow a pattern or regularity into micro-level phenomena that follow simple rules, along with some aggregation mechanism for )] TJ ET
BT 26.250 381.210 Td /F1 9.8 Tf [(the combined activity of the micro-level phenomena in question. Rule-based thinking about organizations has been used to )] TJ ET
BT 26.250 369.305 Td /F1 9.8 Tf [(understand the micro-analytics of individual behavior \(March, 1994\), the phenomenon of organizational ‘culture’ and ‘common )] TJ ET
BT 26.250 357.400 Td /F1 9.8 Tf [(law’ \(March, and Simon, 1958\) and to study the problem of organizational design \(Simon, 1962\), as follows:)] TJ ET
BT 26.250 320.798 Td /F4 12.0 Tf [(The logic of using rule-based systems to study organizational phenomena)] TJ ET
BT 26.250 283.646 Td /F4 12.0 Tf [(Appropriateness-to-a-rule as a criterion of ‘correct’ behavior \(March, 1994\))] TJ ET
BT 26.250 263.691 Td /F1 9.8 Tf [(One can model individual rationality either in a maximization-based framework common to economic theorizing \(Elster, 1981; )] TJ ET
BT 26.250 251.787 Td /F1 9.8 Tf [(see also Sen, 1997\) as the pursuit of those actions and activities that maximize the present value of some utility metric, or as )] TJ ET
BT 26.250 239.882 Td /F1 9.8 Tf [(adherence to sets of rules for thinking and acting \(March, 1994\) or for processing information \(March & Simon, 1958\). As March )] TJ ET
BT 26.250 227.977 Td /F1 9.8 Tf [(points out, it is possible to understand maximization-based logic in a rule-following framework, by positing that individual agents )] TJ ET
BT 26.250 216.072 Td /F1 9.8 Tf [(simply follow the ‘rules of instrumental reason’ \(such as ‘consistency of preferences over time’ \(see Sen, 1993 for a critique\)\) )] TJ ET
BT 26.250 204.168 Td /F1 9.8 Tf [(when they act ‘rationally according to the maximization framework. It is also possible to argue that individuals follow a )] TJ ET
BT 26.250 192.263 Td /F1 9.8 Tf [(maximization-based logic in adhering to certain rules \(including rules for thinking and computation \(Moldoveanu, 1999\)\), as )] TJ ET
BT 26.250 180.358 Td /F1 9.8 Tf [(rules allow for the predictable pursuit of ends and rule-following allows individuals to justify their actions to each other as )] TJ ET
BT 26.250 168.453 Td /F1 9.8 Tf [(legitimate. Even though there is no established ‘matter-of-fact’ about rule following as a general modeling hypothesis, the rule-)] TJ ET
BT 26.250 156.549 Td /F1 9.8 Tf [(following framework has produced a deep and fruitful research tradition in organization studies \(which can be traced back to )] TJ ET
BT 26.250 144.644 Td /F1 9.8 Tf [(March and Simon, 1958; Cyert & March, 1962; Nelson & Winter, 1982\) and can be understood as a foundation of the mind-as-)] TJ ET
BT 26.250 132.739 Td /F1 9.8 Tf [(computer metaphor \(Gigerenzer, 1991\) that lies at the foundation of the ‘cognitive revolution’ in cognitive psychology \(Simon, )] TJ ET
BT 26.250 120.834 Td /F1 9.8 Tf [(1992\). It is to this tradition that this article contributes an understanding of the relationship between micro-level rules and macro-)] TJ ET
BT 26.250 108.930 Td /F1 9.8 Tf [(level rule dynamics.)] TJ ET
BT 26.250 72.327 Td /F4 12.0 Tf [(Appropriateness-to-a-rule as a modeling assumption for organizations: Explaining an observed )] TJ ET
BT 26.250 57.675 Td /F4 12.0 Tf [(phenomenon in terms of the instantiation of a particular rule or rule set \(March, 1994\))] TJ ET
BT 26.250 37.721 Td /F1 9.8 Tf [(Rules \(or norms\) have also been frequently used for understanding organizations of individual agents, even without direct )] TJ ET
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BT 26.250 653.490 Td /F1 9.8 Tf [(Moldoveanu M, Moldoveanu M. Organizations as Universal Computing Machines: Rule systems, computational equivalence, )] TJ ET
BT 26.250 641.585 Td /F1 9.8 Tf [(and organizational complexity. Emergence: Complexity and Organization. 2008 Mar 31 [last modified: 2016 Nov 30]. Edition 1. )] TJ ET
BT 26.250 629.680 Td /F1 9.8 Tf [(doi: 10.emerg/10.17357.66d2beeebdfba021dfd96f49a17d4bcc.)] TJ ET
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BT 26.250 600.578 Td /F4 12.0 Tf [(Abstract)] TJ ET
BT 26.250 580.623 Td /F1 9.8 Tf [(Organizations can be—and, have been—modeled as rule-based systems. On a reductive view, the resulting models depict )] TJ ET
BT 26.250 568.719 Td /F1 9.8 Tf [(organizations as cellular automata \(CA\) that carry out computations whose inputs are the initial and boundary conditions of a )] TJ ET
BT 26.250 556.814 Td /F1 9.8 Tf [(lattice of elements co-evolving according to deterministic interaction rules and whose outputs are the final states of the CA )] TJ ET
BT 26.250 544.909 Td /F1 9.8 Tf [(lattice. We use such models to refine the notion of the complexity of an organizational phenomenon and entertain the notion of )] TJ ET
BT 26.250 533.004 Td /F1 9.8 Tf [(an organization as a universal computer that can support a wide variety of CA to suggest ways in which CA-derived insights can )] TJ ET
BT 26.250 521.100 Td /F1 9.8 Tf [(inform organizational analysis. We examine the informational and computational properties of CA rules and the implications of )] TJ ET
BT 26.250 509.195 Td /F1 9.8 Tf [(the trade-off between their informational and computational complexity to the problem of ‘organizational design’ and show how )] TJ ET
BT 26.250 497.290 Td /F1 9.8 Tf [(the discovery of operational rules could proceed in the context of an empirical framework.)] TJ ET
BT 26.250 460.688 Td /F4 12.0 Tf [(Introduction: Organizations as rule-following systems)] TJ ET
BT 26.250 440.733 Td /F1 9.8 Tf [(Ever since the early work of Simon \(1947\), March and Simon \(1958\) and Cyert and March \(1963\) organizations have been )] TJ ET
BT 26.250 428.829 Td /F1 9.8 Tf [(frequently modeled as rule-following systems. Standard operating procedures \(Cyert & March, 1963\), routines \(Nelson & Winter, )] TJ ET
BT 26.250 416.924 Td /F1 9.8 Tf [(1982\), socio-cognitive and cognitive scripts \(Newell, 1990\) and algorithms \(Moldoveanu & Bauer, 2002\) have all been posited )] TJ ET
BT 26.250 405.019 Td /F1 9.8 Tf [(as ways of understanding the dynamics of organizations through the decomposition of macro-level phenomena that seem to )] TJ ET
BT 26.250 393.114 Td /F1 9.8 Tf [(follow a pattern or regularity into micro-level phenomena that follow simple rules, along with some aggregation mechanism for )] TJ ET
BT 26.250 381.210 Td /F1 9.8 Tf [(the combined activity of the micro-level phenomena in question. Rule-based thinking about organizations has been used to )] TJ ET
BT 26.250 369.305 Td /F1 9.8 Tf [(understand the micro-analytics of individual behavior \(March, 1994\), the phenomenon of organizational ‘culture’ and ‘common )] TJ ET
BT 26.250 357.400 Td /F1 9.8 Tf [(law’ \(March, and Simon, 1958\) and to study the problem of organizational design \(Simon, 1962\), as follows:)] TJ ET
BT 26.250 320.798 Td /F4 12.0 Tf [(The logic of using rule-based systems to study organizational phenomena)] TJ ET
BT 26.250 283.646 Td /F4 12.0 Tf [(Appropriateness-to-a-rule as a criterion of ‘correct’ behavior \(March, 1994\))] TJ ET
BT 26.250 263.691 Td /F1 9.8 Tf [(One can model individual rationality either in a maximization-based framework common to economic theorizing \(Elster, 1981; )] TJ ET
BT 26.250 251.787 Td /F1 9.8 Tf [(see also Sen, 1997\) as the pursuit of those actions and activities that maximize the present value of some utility metric, or as )] TJ ET
BT 26.250 239.882 Td /F1 9.8 Tf [(adherence to sets of rules for thinking and acting \(March, 1994\) or for processing information \(March & Simon, 1958\). As March )] TJ ET
BT 26.250 227.977 Td /F1 9.8 Tf [(points out, it is possible to understand maximization-based logic in a rule-following framework, by positing that individual agents )] TJ ET
BT 26.250 216.072 Td /F1 9.8 Tf [(simply follow the ‘rules of instrumental reason’ \(such as ‘consistency of preferences over time’ \(see Sen, 1993 for a critique\)\) )] TJ ET
BT 26.250 204.168 Td /F1 9.8 Tf [(when they act ‘rationally according to the maximization framework. It is also possible to argue that individuals follow a )] TJ ET
BT 26.250 192.263 Td /F1 9.8 Tf [(maximization-based logic in adhering to certain rules \(including rules for thinking and computation \(Moldoveanu, 1999\)\), as )] TJ ET
BT 26.250 180.358 Td /F1 9.8 Tf [(rules allow for the predictable pursuit of ends and rule-following allows individuals to justify their actions to each other as )] TJ ET
BT 26.250 168.453 Td /F1 9.8 Tf [(legitimate. Even though there is no established ‘matter-of-fact’ about rule following as a general modeling hypothesis, the rule-)] TJ ET
BT 26.250 156.549 Td /F1 9.8 Tf [(following framework has produced a deep and fruitful research tradition in organization studies \(which can be traced back to )] TJ ET
BT 26.250 144.644 Td /F1 9.8 Tf [(March and Simon, 1958; Cyert & March, 1962; Nelson & Winter, 1982\) and can be understood as a foundation of the mind-as-)] TJ ET
BT 26.250 132.739 Td /F1 9.8 Tf [(computer metaphor \(Gigerenzer, 1991\) that lies at the foundation of the ‘cognitive revolution’ in cognitive psychology \(Simon, )] TJ ET
BT 26.250 120.834 Td /F1 9.8 Tf [(1992\). It is to this tradition that this article contributes an understanding of the relationship between micro-level rules and macro-)] TJ ET
BT 26.250 108.930 Td /F1 9.8 Tf [(level rule dynamics.)] TJ ET
BT 26.250 72.327 Td /F4 12.0 Tf [(Appropriateness-to-a-rule as a modeling assumption for organizations: Explaining an observed )] TJ ET
BT 26.250 57.675 Td /F4 12.0 Tf [(phenomenon in terms of the instantiation of a particular rule or rule set \(March, 1994\))] TJ ET
BT 26.250 37.721 Td /F1 9.8 Tf [(Rules \(or norms\) have also been frequently used for understanding organizations of individual agents, even without direct )] TJ ET
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BT 15.000 720.189 Td /F2 21.0 Tf [(systems, computational equivalence, and organizational )] TJ ET
BT 15.000 695.241 Td /F2 21.0 Tf [(complexity)] TJ ET
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BT 119.460 665.394 Td /F1 9.8 Tf [(Mihnea Moldoveanu)] TJ ET
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BT 26.250 653.490 Td /F1 9.8 Tf [(Moldoveanu M, Moldoveanu M. Organizations as Universal Computing Machines: Rule systems, computational equivalence, )] TJ ET
BT 26.250 641.585 Td /F1 9.8 Tf [(and organizational complexity. Emergence: Complexity and Organization. 2008 Mar 31 [last modified: 2016 Nov 30]. Edition 1. )] TJ ET
BT 26.250 629.680 Td /F1 9.8 Tf [(doi: 10.emerg/10.17357.66d2beeebdfba021dfd96f49a17d4bcc.)] TJ ET
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BT 26.250 600.578 Td /F4 12.0 Tf [(Abstract)] TJ ET
BT 26.250 580.623 Td /F1 9.8 Tf [(Organizations can be—and, have been—modeled as rule-based systems. On a reductive view, the resulting models depict )] TJ ET
BT 26.250 568.719 Td /F1 9.8 Tf [(organizations as cellular automata \(CA\) that carry out computations whose inputs are the initial and boundary conditions of a )] TJ ET
BT 26.250 556.814 Td /F1 9.8 Tf [(lattice of elements co-evolving according to deterministic interaction rules and whose outputs are the final states of the CA )] TJ ET
BT 26.250 544.909 Td /F1 9.8 Tf [(lattice. We use such models to refine the notion of the complexity of an organizational phenomenon and entertain the notion of )] TJ ET
BT 26.250 533.004 Td /F1 9.8 Tf [(an organization as a universal computer that can support a wide variety of CA to suggest ways in which CA-derived insights can )] TJ ET
BT 26.250 521.100 Td /F1 9.8 Tf [(inform organizational analysis. We examine the informational and computational properties of CA rules and the implications of )] TJ ET
BT 26.250 509.195 Td /F1 9.8 Tf [(the trade-off between their informational and computational complexity to the problem of ‘organizational design’ and show how )] TJ ET
BT 26.250 497.290 Td /F1 9.8 Tf [(the discovery of operational rules could proceed in the context of an empirical framework.)] TJ ET
BT 26.250 460.688 Td /F4 12.0 Tf [(Introduction: Organizations as rule-following systems)] TJ ET
BT 26.250 440.733 Td /F1 9.8 Tf [(Ever since the early work of Simon \(1947\), March and Simon \(1958\) and Cyert and March \(1963\) organizations have been )] TJ ET
BT 26.250 428.829 Td /F1 9.8 Tf [(frequently modeled as rule-following systems. Standard operating procedures \(Cyert & March, 1963\), routines \(Nelson & Winter, )] TJ ET
BT 26.250 416.924 Td /F1 9.8 Tf [(1982\), socio-cognitive and cognitive scripts \(Newell, 1990\) and algorithms \(Moldoveanu & Bauer, 2002\) have all been posited )] TJ ET
BT 26.250 405.019 Td /F1 9.8 Tf [(as ways of understanding the dynamics of organizations through the decomposition of macro-level phenomena that seem to )] TJ ET
BT 26.250 393.114 Td /F1 9.8 Tf [(follow a pattern or regularity into micro-level phenomena that follow simple rules, along with some aggregation mechanism for )] TJ ET
BT 26.250 381.210 Td /F1 9.8 Tf [(the combined activity of the micro-level phenomena in question. Rule-based thinking about organizations has been used to )] TJ ET
BT 26.250 369.305 Td /F1 9.8 Tf [(understand the micro-analytics of individual behavior \(March, 1994\), the phenomenon of organizational ‘culture’ and ‘common )] TJ ET
BT 26.250 357.400 Td /F1 9.8 Tf [(law’ \(March, and Simon, 1958\) and to study the problem of organizational design \(Simon, 1962\), as follows:)] TJ ET
BT 26.250 320.798 Td /F4 12.0 Tf [(The logic of using rule-based systems to study organizational phenomena)] TJ ET
BT 26.250 283.646 Td /F4 12.0 Tf [(Appropriateness-to-a-rule as a criterion of ‘correct’ behavior \(March, 1994\))] TJ ET
BT 26.250 263.691 Td /F1 9.8 Tf [(One can model individual rationality either in a maximization-based framework common to economic theorizing \(Elster, 1981; )] TJ ET
BT 26.250 251.787 Td /F1 9.8 Tf [(see also Sen, 1997\) as the pursuit of those actions and activities that maximize the present value of some utility metric, or as )] TJ ET
BT 26.250 239.882 Td /F1 9.8 Tf [(adherence to sets of rules for thinking and acting \(March, 1994\) or for processing information \(March & Simon, 1958\). As March )] TJ ET
BT 26.250 227.977 Td /F1 9.8 Tf [(points out, it is possible to understand maximization-based logic in a rule-following framework, by positing that individual agents )] TJ ET
BT 26.250 216.072 Td /F1 9.8 Tf [(simply follow the ‘rules of instrumental reason’ \(such as ‘consistency of preferences over time’ \(see Sen, 1993 for a critique\)\) )] TJ ET
BT 26.250 204.168 Td /F1 9.8 Tf [(when they act ‘rationally according to the maximization framework. It is also possible to argue that individuals follow a )] TJ ET
BT 26.250 192.263 Td /F1 9.8 Tf [(maximization-based logic in adhering to certain rules \(including rules for thinking and computation \(Moldoveanu, 1999\)\), as )] TJ ET
BT 26.250 180.358 Td /F1 9.8 Tf [(rules allow for the predictable pursuit of ends and rule-following allows individuals to justify their actions to each other as )] TJ ET
BT 26.250 168.453 Td /F1 9.8 Tf [(legitimate. Even though there is no established ‘matter-of-fact’ about rule following as a general modeling hypothesis, the rule-)] TJ ET
BT 26.250 156.549 Td /F1 9.8 Tf [(following framework has produced a deep and fruitful research tradition in organization studies \(which can be traced back to )] TJ ET
BT 26.250 144.644 Td /F1 9.8 Tf [(March and Simon, 1958; Cyert & March, 1962; Nelson & Winter, 1982\) and can be understood as a foundation of the mind-as-)] TJ ET
BT 26.250 132.739 Td /F1 9.8 Tf [(computer metaphor \(Gigerenzer, 1991\) that lies at the foundation of the ‘cognitive revolution’ in cognitive psychology \(Simon, )] TJ ET
BT 26.250 120.834 Td /F1 9.8 Tf [(1992\). It is to this tradition that this article contributes an understanding of the relationship between micro-level rules and macro-)] TJ ET
BT 26.250 108.930 Td /F1 9.8 Tf [(level rule dynamics.)] TJ ET
BT 26.250 72.327 Td /F4 12.0 Tf [(Appropriateness-to-a-rule as a modeling assumption for organizations: Explaining an observed )] TJ ET
BT 26.250 57.675 Td /F4 12.0 Tf [(phenomenon in terms of the instantiation of a particular rule or rule set \(March, 1994\))] TJ ET
BT 26.250 37.721 Td /F1 9.8 Tf [(Rules \(or norms\) have also been frequently used for understanding organizations of individual agents, even without direct )] TJ ET
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BT 26.250 611.826 Td /F4 12.0 Tf [(Appropriateness-to-a-rule as a design assumption for organizations)] TJ ET
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BT 383.948 428.172 Td /F1 9.8 Tf [( kinds of rules and rule systems. It is )] TJ ET
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BT 26.250 361.149 Td /F1 9.8 Tf [(Rules of conduct, comportment and coordination supply scripts and algorithms that allow individuals in an organization to )] TJ ET
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BT 26.250 270.315 Td /F4 9.8 Tf [(Meta-rules)] TJ ET
BT 75.010 270.315 Td /F1 9.8 Tf [( govern the uses of rules and the ways in which rules are adapted in response to anomalies. The problem of )] TJ ET
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BT 26.250 246.506 Td /F1 9.8 Tf [(changing contexts and changing environmental conditions. The theory of adaptive algorithms \(including evolutionary algorithms )] TJ ET
BT 26.250 234.601 Td /F1 9.8 Tf [(\(Bruderer & Singh, 1996\)\) supplies a rich set of formalizable systems of meta-rules. Algorithms for search \(March, 1991\) can be )] TJ ET
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BT 26.250 210.792 Td /F1 9.8 Tf [(used to study the phenomena of organizational exploitation of a newly discovered opportunity or opportunity set.)] TJ ET
BT 26.250 191.387 Td /F4 9.8 Tf [(Para-rules)] TJ ET
BT 73.937 191.387 Td /F1 9.8 Tf [( are rules for adjudicating among unforeseen incongruencies or tensions between rules \(Cyert & March, 1963\). Even )] TJ ET
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BT 26.250 167.577 Td /F1 9.8 Tf [(special class of rules \(we will call them para-rules\) for adjudicating conflicts between different rule sets and thus resolving the )] TJ ET
BT 26.250 155.673 Td /F1 9.8 Tf [(important problem of prescribing action in situations when opposing rule systems claim equal cognitive or moral jurisdiction. )] TJ ET
BT 26.250 143.768 Td /F1 9.8 Tf [(Such para-rules are often ways of ascertaining truth or validity of empirical or moral rule systems, or of theories that )] TJ ET
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BT 26.250 119.958 Td /F1 9.8 Tf [(\(Moldoveanu, 2002\).)] TJ ET
BT 26.250 100.554 Td /F4 9.8 Tf [(Ortho-rules)] TJ ET
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BT 26.250 743.667 Td /F1 9.8 Tf [(certain rules, certain rule sets, or, certain rules sets in certain conditions. Meaning, for instance, is created through collective )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(action that embodies the rules and norms of organizations as a whole, and )] TJ ET
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BT 403.438 731.762 Td /F1 9.8 Tf [( is about the collective pursuit of such )] TJ ET
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BT 391.475 719.857 Td /F1 9.8 Tf [( upon “meaning”. As organizational rules, )] TJ ET
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BT 26.250 696.048 Td /F1 9.8 Tf [(a background of shared assumptions, categories and heuristics that are common knowledge \(Lewis, 1969\) among members of )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(the organization, thus enabling members of the organization to pursue collective goals efficiently and reliably \(Kreps, 1990\). The )] TJ ET
BT 26.250 672.238 Td /F1 9.8 Tf [(meaning-giving activity of rules and rule systems thereby recedes into unconsciousness \(and sometimes oblivion\), which does )] TJ ET
BT 26.250 660.333 Td /F1 9.8 Tf [(not, nonetheless weaken the basic argument that rule systems—applied to the organizational level—can be used to understand )] TJ ET
BT 26.250 648.429 Td /F1 9.8 Tf [(the production of collective patterns of behavior.)] TJ ET
BT 26.250 611.826 Td /F4 12.0 Tf [(Appropriateness-to-a-rule as a design assumption for organizations)] TJ ET
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BT 26.250 568.062 Td /F1 9.8 Tf [(phenomena and systems include organizations, institutions, and the technologies which are used by organizations and )] TJ ET
BT 26.250 556.158 Td /F1 9.8 Tf [(institutions to carry out their work. The problem of organizational and institutional design becomes the problem of selecting )] TJ ET
BT 26.250 544.253 Td /F1 9.8 Tf [(those rules and systems of rules that are most likely to produce a particular desired outcome. Examples of successful rules )] TJ ET
BT 26.250 532.348 Td /F1 9.8 Tf [(include the ‘modularization heuristic’ and the ‘separability heuristic’ \(Simon, 1962\) for creating more survivable evolutionary )] TJ ET
BT 26.250 520.443 Td /F1 9.8 Tf [(entities in the presence of external ‘noise’ that disrupts the smooth internal functioning of these systems. Organizational culture )] TJ ET
BT 26.250 508.539 Td /F1 9.8 Tf [(can be understood as a system of rules and meta-rules for selecting certain kinds of behaviors, beliefs, theories, models or )] TJ ET
BT 26.250 496.634 Td /F1 9.8 Tf [(cognitive maps over others \(Moldoveanu & Singh, 2003\), and thus the Simonesque framework can be applied to the problem of )] TJ ET
BT 26.250 484.729 Td /F1 9.8 Tf [(designing successful cultural systems \(once a criterion for organizational success has been agreed upon\).)] TJ ET
BT 26.250 448.127 Td /F4 12.0 Tf [(The uses of rules in organizations and institutions)] TJ ET
BT 26.250 428.172 Td /F1 9.8 Tf [(The fact that we can ask about ‘best’ rules depends on the fact that there are )] TJ ET
BT 360.110 428.172 Td /F5 9.8 Tf [(many)] TJ ET
BT 383.948 428.172 Td /F1 9.8 Tf [( kinds of rules and rule systems. It is )] TJ ET
BT 26.250 416.268 Td /F1 9.8 Tf [(important to distinguish between different kinds of organizational rule systems, and to ask: is it possible \(or even conceivable\) )] TJ ET
BT 26.250 404.363 Td /F1 9.8 Tf [(that they can be accommodated by a modeling framework? If so, what is the framework that can contain \(i.e., represent, allow )] TJ ET
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BT 26.250 361.149 Td /F1 9.8 Tf [(Rules of conduct, comportment and coordination supply scripts and algorithms that allow individuals in an organization to )] TJ ET
BT 26.250 349.244 Td /F1 9.8 Tf [(interact successfully in coordination games \(Schelling, 1960\), argumentation games \(Alexy, 1988; Toulmin, 1981\), or morality )] TJ ET
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BT 26.250 313.530 Td /F1 9.8 Tf [(informative to the discussion, on pain of being a non-contribution\), and the principles of discourse ethics \(Alexy, 1988\) )] TJ ET
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BT 26.250 289.720 Td /F1 9.8 Tf [(unintelligible collections of syllables, and thus outside of the realm of the dialogue being carried out\).)] TJ ET
BT 26.250 270.315 Td /F4 9.8 Tf [(Meta-rules)] TJ ET
BT 75.010 270.315 Td /F1 9.8 Tf [( govern the uses of rules and the ways in which rules are adapted in response to anomalies. The problem of )] TJ ET
BT 26.250 258.411 Td /F1 9.8 Tf [(organizational adaptation \(Levinthal & March, 1993\) is essentially the problem of adapting rule sets to different contexts, )] TJ ET
BT 26.250 246.506 Td /F1 9.8 Tf [(changing contexts and changing environmental conditions. The theory of adaptive algorithms \(including evolutionary algorithms )] TJ ET
BT 26.250 234.601 Td /F1 9.8 Tf [(\(Bruderer & Singh, 1996\)\) supplies a rich set of formalizable systems of meta-rules. Algorithms for search \(March, 1991\) can be )] TJ ET
BT 26.250 222.696 Td /F1 9.8 Tf [(used to study different patterns of organizational exploration in the same way as task-performance-simulating algorithms can be )] TJ ET
BT 26.250 210.792 Td /F1 9.8 Tf [(used to study the phenomena of organizational exploitation of a newly discovered opportunity or opportunity set.)] TJ ET
BT 26.250 191.387 Td /F4 9.8 Tf [(Para-rules)] TJ ET
BT 73.937 191.387 Td /F1 9.8 Tf [( are rules for adjudicating among unforeseen incongruencies or tensions between rules \(Cyert & March, 1963\). Even )] TJ ET
BT 26.250 179.482 Td /F1 9.8 Tf [(without changing contexts, varying contexts and changing environmental conditions, the rule-based paradigm needs to access a )] TJ ET
BT 26.250 167.577 Td /F1 9.8 Tf [(special class of rules \(we will call them para-rules\) for adjudicating conflicts between different rule sets and thus resolving the )] TJ ET
BT 26.250 155.673 Td /F1 9.8 Tf [(important problem of prescribing action in situations when opposing rule systems claim equal cognitive or moral jurisdiction. )] TJ ET
BT 26.250 143.768 Td /F1 9.8 Tf [(Such para-rules are often ways of ascertaining truth or validity of empirical or moral rule systems, or of theories that )] TJ ET
BT 26.250 131.863 Td /F1 9.8 Tf [(organizations might collectively hold about the environment. They can constitute the ‘epistemology of the organization )] TJ ET
BT 26.250 119.958 Td /F1 9.8 Tf [(\(Moldoveanu, 2002\).)] TJ ET
BT 26.250 100.554 Td /F4 9.8 Tf [(Ortho-rules)] TJ ET
BT 79.339 100.554 Td /F1 9.8 Tf [( are rules for resolving ambiguities and uncertainty about the correct application of a particular rule \(Cohen & )] TJ ET
BT 26.250 88.649 Td /F1 9.8 Tf [(March, 1972\). Classical epistemology turns up many indeterminacies and ambiguities in the establishment of the validity of the )] TJ ET
BT 26.250 76.744 Td /F1 9.8 Tf [(empirical claims of a theory or model: most of its core results are negative results: about the impossibility of a deductive basis )] TJ ET
BT 26.250 64.839 Td /F1 9.8 Tf [(for inductive logic \(Hume, 1949\), the ambiguity of confirmation \(Hempel, 1951\), the impossibility of constructing a rule system )] TJ ET
BT 26.250 52.935 Td /F1 9.8 Tf [(that can be used to prove its own validity \(Putnam, 1985; following Gödel, 1931\). Nevertheless, individual agents can and do )] TJ ET
BT 26.250 41.030 Td /F1 9.8 Tf [(bridge the logical \(and meta-logical\) divide between word and action \(or event or object\): they interpret events according to )] TJ ET
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BT 26.250 755.571 Td /F1 9.8 Tf [(systems, systems of meaning as well as systems of action and choice, all of which can be ‘reduced’ to proclivities to follow )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(certain rules, certain rule sets, or, certain rules sets in certain conditions. Meaning, for instance, is created through collective )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(action that embodies the rules and norms of organizations as a whole, and )] TJ ET
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BT 403.438 731.762 Td /F1 9.8 Tf [( is about the collective pursuit of such )] TJ ET
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BT 354.084 719.857 Td /F5 9.8 Tf [(meaning)] TJ ET
BT 391.475 719.857 Td /F1 9.8 Tf [( upon “meaning”. As organizational rules, )] TJ ET
BT 26.250 707.952 Td /F1 9.8 Tf [(individuated and exemplified by collective organizational action, become self-evident to members of the organization, they form )] TJ ET
BT 26.250 696.048 Td /F1 9.8 Tf [(a background of shared assumptions, categories and heuristics that are common knowledge \(Lewis, 1969\) among members of )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(the organization, thus enabling members of the organization to pursue collective goals efficiently and reliably \(Kreps, 1990\). The )] TJ ET
BT 26.250 672.238 Td /F1 9.8 Tf [(meaning-giving activity of rules and rule systems thereby recedes into unconsciousness \(and sometimes oblivion\), which does )] TJ ET
BT 26.250 660.333 Td /F1 9.8 Tf [(not, nonetheless weaken the basic argument that rule systems—applied to the organizational level—can be used to understand )] TJ ET
BT 26.250 648.429 Td /F1 9.8 Tf [(the production of collective patterns of behavior.)] TJ ET
BT 26.250 611.826 Td /F4 12.0 Tf [(Appropriateness-to-a-rule as a design assumption for organizations)] TJ ET
BT 26.250 591.872 Td /F1 9.8 Tf [(Which rules are “best” for designing rule-bound systems—and, why? Herbert Simon \(Simon, 1962, 1996\) argued for the )] TJ ET
BT 26.250 579.967 Td /F1 9.8 Tf [(development of the ‘sciences of the artificial’—of those phenomena and systems that are created by humans. These )] TJ ET
BT 26.250 568.062 Td /F1 9.8 Tf [(phenomena and systems include organizations, institutions, and the technologies which are used by organizations and )] TJ ET
BT 26.250 556.158 Td /F1 9.8 Tf [(institutions to carry out their work. The problem of organizational and institutional design becomes the problem of selecting )] TJ ET
BT 26.250 544.253 Td /F1 9.8 Tf [(those rules and systems of rules that are most likely to produce a particular desired outcome. Examples of successful rules )] TJ ET
BT 26.250 532.348 Td /F1 9.8 Tf [(include the ‘modularization heuristic’ and the ‘separability heuristic’ \(Simon, 1962\) for creating more survivable evolutionary )] TJ ET
BT 26.250 520.443 Td /F1 9.8 Tf [(entities in the presence of external ‘noise’ that disrupts the smooth internal functioning of these systems. Organizational culture )] TJ ET
BT 26.250 508.539 Td /F1 9.8 Tf [(can be understood as a system of rules and meta-rules for selecting certain kinds of behaviors, beliefs, theories, models or )] TJ ET
BT 26.250 496.634 Td /F1 9.8 Tf [(cognitive maps over others \(Moldoveanu & Singh, 2003\), and thus the Simonesque framework can be applied to the problem of )] TJ ET
BT 26.250 484.729 Td /F1 9.8 Tf [(designing successful cultural systems \(once a criterion for organizational success has been agreed upon\).)] TJ ET
BT 26.250 448.127 Td /F4 12.0 Tf [(The uses of rules in organizations and institutions)] TJ ET
BT 26.250 428.172 Td /F1 9.8 Tf [(The fact that we can ask about ‘best’ rules depends on the fact that there are )] TJ ET
BT 360.110 428.172 Td /F5 9.8 Tf [(many)] TJ ET
BT 383.948 428.172 Td /F1 9.8 Tf [( kinds of rules and rule systems. It is )] TJ ET
BT 26.250 416.268 Td /F1 9.8 Tf [(important to distinguish between different kinds of organizational rule systems, and to ask: is it possible \(or even conceivable\) )] TJ ET
BT 26.250 404.363 Td /F1 9.8 Tf [(that they can be accommodated by a modeling framework? If so, what is the framework that can contain \(i.e., represent, allow )] TJ ET
BT 26.250 392.458 Td /F1 9.8 Tf [(us to do computational and thought experiments with\) the different kinds of rules and rule systems there are? And there are )] TJ ET
BT 26.250 380.553 Td /F1 9.8 Tf [(many. For instance:)] TJ ET
BT 26.250 361.149 Td /F1 9.8 Tf [(Rules of conduct, comportment and coordination supply scripts and algorithms that allow individuals in an organization to )] TJ ET
BT 26.250 349.244 Td /F1 9.8 Tf [(interact successfully in coordination games \(Schelling, 1960\), argumentation games \(Alexy, 1988; Toulmin, 1981\), or morality )] TJ ET
BT 26.250 337.339 Td /F1 9.8 Tf [(games \(Ruth Lakoff, 2001\). An example of rules in this class include the cooperation principle \(Grice, 1975\) that is a pre-)] TJ ET
BT 26.250 325.434 Td /F1 9.8 Tf [(condition for participation in a dialogue \(demanding that we understand the speaker’s contributions as being relevant and )] TJ ET
BT 26.250 313.530 Td /F1 9.8 Tf [(informative to the discussion, on pain of being a non-contribution\), and the principles of discourse ethics \(Alexy, 1988\) )] TJ ET
BT 26.250 301.625 Td /F1 9.8 Tf [(demanding consistency and setting standards for minimal responsiveness for speech acts \(on pain of the latter becoming )] TJ ET
BT 26.250 289.720 Td /F1 9.8 Tf [(unintelligible collections of syllables, and thus outside of the realm of the dialogue being carried out\).)] TJ ET
BT 26.250 270.315 Td /F4 9.8 Tf [(Meta-rules)] TJ ET
BT 75.010 270.315 Td /F1 9.8 Tf [( govern the uses of rules and the ways in which rules are adapted in response to anomalies. The problem of )] TJ ET
BT 26.250 258.411 Td /F1 9.8 Tf [(organizational adaptation \(Levinthal & March, 1993\) is essentially the problem of adapting rule sets to different contexts, )] TJ ET
BT 26.250 246.506 Td /F1 9.8 Tf [(changing contexts and changing environmental conditions. The theory of adaptive algorithms \(including evolutionary algorithms )] TJ ET
BT 26.250 234.601 Td /F1 9.8 Tf [(\(Bruderer & Singh, 1996\)\) supplies a rich set of formalizable systems of meta-rules. Algorithms for search \(March, 1991\) can be )] TJ ET
BT 26.250 222.696 Td /F1 9.8 Tf [(used to study different patterns of organizational exploration in the same way as task-performance-simulating algorithms can be )] TJ ET
BT 26.250 210.792 Td /F1 9.8 Tf [(used to study the phenomena of organizational exploitation of a newly discovered opportunity or opportunity set.)] TJ ET
BT 26.250 191.387 Td /F4 9.8 Tf [(Para-rules)] TJ ET
BT 73.937 191.387 Td /F1 9.8 Tf [( are rules for adjudicating among unforeseen incongruencies or tensions between rules \(Cyert & March, 1963\). Even )] TJ ET
BT 26.250 179.482 Td /F1 9.8 Tf [(without changing contexts, varying contexts and changing environmental conditions, the rule-based paradigm needs to access a )] TJ ET
BT 26.250 167.577 Td /F1 9.8 Tf [(special class of rules \(we will call them para-rules\) for adjudicating conflicts between different rule sets and thus resolving the )] TJ ET
BT 26.250 155.673 Td /F1 9.8 Tf [(important problem of prescribing action in situations when opposing rule systems claim equal cognitive or moral jurisdiction. )] TJ ET
BT 26.250 143.768 Td /F1 9.8 Tf [(Such para-rules are often ways of ascertaining truth or validity of empirical or moral rule systems, or of theories that )] TJ ET
BT 26.250 131.863 Td /F1 9.8 Tf [(organizations might collectively hold about the environment. They can constitute the ‘epistemology of the organization )] TJ ET
BT 26.250 119.958 Td /F1 9.8 Tf [(\(Moldoveanu, 2002\).)] TJ ET
BT 26.250 100.554 Td /F4 9.8 Tf [(Ortho-rules)] TJ ET
BT 79.339 100.554 Td /F1 9.8 Tf [( are rules for resolving ambiguities and uncertainty about the correct application of a particular rule \(Cohen & )] TJ ET
BT 26.250 88.649 Td /F1 9.8 Tf [(March, 1972\). Classical epistemology turns up many indeterminacies and ambiguities in the establishment of the validity of the )] TJ ET
BT 26.250 76.744 Td /F1 9.8 Tf [(empirical claims of a theory or model: most of its core results are negative results: about the impossibility of a deductive basis )] TJ ET
BT 26.250 64.839 Td /F1 9.8 Tf [(for inductive logic \(Hume, 1949\), the ambiguity of confirmation \(Hempel, 1951\), the impossibility of constructing a rule system )] TJ ET
BT 26.250 52.935 Td /F1 9.8 Tf [(that can be used to prove its own validity \(Putnam, 1985; following Gödel, 1931\). Nevertheless, individual agents can and do )] TJ ET
BT 26.250 41.030 Td /F1 9.8 Tf [(bridge the logical \(and meta-logical\) divide between word and action \(or event or object\): they interpret events according to )] TJ ET
Q
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BT 291.710 19.825 Td /F1 11.0 Tf [(2)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
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BT 26.250 767.476 Td /F1 9.8 Tf [(particular rules, and act according to the reconstructed reality that they have created \(Weick, 1995\). What makes this process )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(intelligible is the pursuit of certain rules \(or habits\) of cognition and interpretation, which supplies the impetus for the study of )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(implicit cognition and perception.)] TJ ET
BT 26.250 724.262 Td /F1 9.8 Tf [(There is, then, a way of studying organizations and organizing in which )] TJ ET
BT 334.603 724.262 Td /F5 9.8 Tf [(rules rule)] TJ ET
BT 374.695 724.262 Td /F1 9.8 Tf [(. They matter so deeply that they provide the )] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(very foundation for the meaning of speech acts \(Wittgenstein, 1952\), the recognition of patterns of organization, the successful )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(coordination of communicative and strategic action and the design, planning and enactment of organizational tasks )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(\(Moldoveanu & Bauer, 2004\). It may then, interest one, as a student of human action and organizational behavior, to come up )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(with a representation of rules and rule-bound behavior that is at once general enough to accommodate the rich variety of rules )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(and rule-based patterns of behavior and precise enough to allow us to consider detailed questions concerning rule design and )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(its effects on macro-level organizational behavior.)] TJ ET
BT 26.250 616.231 Td /F4 12.0 Tf [(The rule-based study of rule-bound systems)] TJ ET
BT 26.250 596.277 Td /F1 9.8 Tf [(Stephen Wolfram \(1985, 1993, 2002\) suggested that )] TJ ET
BT 256.048 596.277 Td /F5 9.8 Tf [(computation)] TJ ET
BT 309.156 596.277 Td /F1 9.8 Tf [( can provide a powerful model for physical processes, and )] TJ ET
BT 26.250 584.372 Td /F1 9.8 Tf [(that )] TJ ET
BT 45.224 584.372 Td /F5 9.8 Tf [(this)] TJ ET
BT 60.395 584.372 Td /F1 9.8 Tf [( fact has important consequences for the predictability and tractability of problems in physics. He points to the following )] TJ ET
BT 26.250 572.467 Td /F1 9.8 Tf [(interesting relationship between computation and physical dynamics: “On one hand, theoretical models describe physical )] TJ ET
BT 26.250 560.562 Td /F1 9.8 Tf [(processes by computations that transform initial data according to algorithms representing physical laws. And, on the other )] TJ ET
BT 26.250 548.658 Td /F1 9.8 Tf [(hand, computers themselves \(which we use to run the said computations—author’s note\) are physical systems obeying physical )] TJ ET
BT 26.250 536.753 Td /F1 9.8 Tf [(laws” \(Wolfram, 1985.\) He explores the implications of this duality for the frequency of intractable or not-decidable-in-a-finite-)] TJ ET
BT 26.250 524.848 Td /F1 9.8 Tf [(time problems in physics, as follows:)] TJ ET
0.965 0.965 0.965 rg
26.250 353.075 555.000 161.893 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 514.967 m 581.250 514.967 l 581.250 514.217 l 26.250 514.217 l f
26.250 353.075 m 581.250 353.075 l 581.250 353.825 l 26.250 353.825 l f
0.271 0.267 0.267 rg
BT 41.206 495.711 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 495.693 Td /F1 9.8 Tf [(First, he shows that computations can be 1. modeled by the long-run evolution of cellular automata \(CA\), and therefore )] TJ ET
BT 54.750 483.789 Td /F1 9.8 Tf [(that predicting the evolution of a CA is equivalent to predicting the evolution of the dynamical system that the rule set )] TJ ET
BT 54.750 471.884 Td /F1 9.8 Tf [(embodied in the CA models;)] TJ ET
BT 41.206 448.747 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 448.729 Td /F1 9.8 Tf [(Next, he argues that some calculations 2. may be computationally irreducible, in the sense that the only way in which )] TJ ET
BT 54.750 436.824 Td /F1 9.8 Tf [(one can predict the evolution of the CA that corresponds to them is to follow the long-run evolution of that CA: there )] TJ ET
BT 54.750 424.920 Td /F1 9.8 Tf [(are, in these cases, no short cuts;)] TJ ET
BT 41.206 401.783 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 401.765 Td /F1 9.8 Tf [(Finally, he shows that there are large classes 3. of CAs \(‘universal computers’, capable of simulating any other finite )] TJ ET
BT 54.750 389.860 Td /F1 9.8 Tf [(computational device of the same dimension\) for which the problem of predicting the long-run evolution is intractable; )] TJ ET
BT 54.750 377.955 Td /F1 9.8 Tf [(moreover, such CAs are far more frequent than are CAs allowing ‘computational shortcuts.)] TJ ET
BT 26.250 336.051 Td /F1 9.8 Tf [(The ‘surprising insight’ is that, in the physical world, there are many more intractable problems than there are tractable ones. It )] TJ ET
BT 26.250 324.146 Td /F1 9.8 Tf [(seems at least prima facie interesting to ask: to what extent does any of this make a difference to the problems faced by )] TJ ET
BT 26.250 312.241 Td /F1 9.8 Tf [(organizational modelers? )] TJ ET
BT 137.878 312.241 Td /F5 9.8 Tf [(This)] TJ ET
BT 156.296 312.241 Td /F1 9.8 Tf [( in the previous sentence has, to be sure, )] TJ ET
BT 337.314 312.241 Td /F5 9.8 Tf [(two)] TJ ET
BT 352.485 312.241 Td /F1 9.8 Tf [( parts: the )] TJ ET
BT 398.554 312.241 Td /F5 9.8 Tf [(first)] TJ ET
BT 414.261 312.241 Td /F1 9.8 Tf [( has to do with the basic modeling )] TJ ET
BT 26.250 300.336 Td /F1 9.8 Tf [(move that Wolfram makes \(physical systems as general purpose computers, their evolutions as computations, computations as )] TJ ET
BT 26.250 288.432 Td /F1 9.8 Tf [(states of a CA, CA as a generalized model for the evolution of a physical system\). The )] TJ ET
BT 400.709 288.432 Td /F5 9.8 Tf [(second)] TJ ET
BT 432.143 288.432 Td /F1 9.8 Tf [( has to do with the issue of )] TJ ET
BT 26.250 276.527 Td /F5 9.8 Tf [(complexity)] TJ ET
BT 72.299 276.527 Td /F1 9.8 Tf [(: suppose organizations )] TJ ET
BT 177.980 276.527 Td /F5 9.8 Tf [(could)] TJ ET
BT 201.282 276.527 Td /F1 9.8 Tf [( be thought of as universal computers, what—if anything—could we say about the )] TJ ET
BT 26.250 264.622 Td /F1 9.8 Tf [(complexity of organizational phenomena understood as intermediate and final steps in \(sometimes very lengthy\) computations?)] TJ ET
BT 26.250 245.217 Td /F1 9.8 Tf [(Let us take seriously the basic model of organizational states as states of a CA—with \(potentially very complex\) rule systems in )] TJ ET
BT 26.250 233.313 Td /F1 9.8 Tf [(place. In the ‘worst case’—one in which we do not agree on just what the ‘right’ rules that govern interactions should be—we )] TJ ET
BT 26.250 221.408 Td /F1 9.8 Tf [(can simply assume that the CA which models the organization is the CA corresponding to the total set of elementary particles in )] TJ ET
BT 26.250 209.503 Td /F1 9.8 Tf [(the organization, interacting according to a rule system that corresponds to the fundamental laws of physics. \(As Wolfram is )] TJ ET
BT 26.250 197.598 Td /F1 9.8 Tf [(quick to point out \(Wolfram, 1985\), the modeler’s basic choice of ontology \(‘particles’ versus ‘fields’ should not matter from the )] TJ ET
BT 26.250 185.694 Td /F1 9.8 Tf [(point of view of the validity of a CA model, as CAs can be used to model discrete-form versions of the partial differential )] TJ ET
BT 26.250 173.789 Td /F1 9.8 Tf [(equations that are used to model the space-time distribution and evolution of a field\). It is not difficult to see that the problem of )] TJ ET
BT 26.250 161.884 Td /F1 9.8 Tf [(predicting the evolution of an organization over a significantly long period of—in general—intractable: even the two-body )] TJ ET
BT 26.250 149.979 Td /F1 9.8 Tf [(problem is intractable for certain kinetic energy levels \(Casti, 1991\). So, we have good reason to ask: why—and, when could we )] TJ ET
BT 26.250 138.075 Td /F1 9.8 Tf [(reasonably—harbor any confidence that we can )] TJ ET
BT 235.436 138.075 Td /F5 9.8 Tf [(predict)] TJ ET
BT 264.696 138.075 Td /F1 9.8 Tf [( anything at all about the evolution of an organization over periods of )] TJ ET
BT 26.250 126.170 Td /F1 9.8 Tf [(time that matter?)] TJ ET
BT 26.250 106.765 Td /F1 9.8 Tf [(Wolfram’s CA-based approach offers both a sobering answer to such questions and the prospect of a framework that allows us )] TJ ET
BT 26.250 94.860 Td /F1 9.8 Tf [(to ask fundamental questions about predictability of organizational phenomena in the first place. In particular, if very large )] TJ ET
BT 26.250 82.956 Td /F1 9.8 Tf [(classes of formal CAs represent ‘universal computers’ and there is no reason to )] TJ ET
BT 371.966 82.956 Td /F5 9.8 Tf [(ex ante)] TJ ET
BT 403.946 82.956 Td /F1 9.8 Tf [( exclude them as valid models for )] TJ ET
BT 26.250 71.051 Td /F1 9.8 Tf [(organizations, then, the probability that a particular organizational phenomenon is computationally irreducible will )] TJ ET
BT 514.501 71.051 Td /F5 9.8 Tf [(ex ante)] TJ ET
BT 546.481 71.051 Td /F1 9.8 Tf [( be )] TJ ET
BT 26.250 59.146 Td /F1 9.8 Tf [(high. This means that, for a large class of organizational phenomena, )] TJ ET
BT 327.564 59.146 Td /F5 9.8 Tf [(the best predictive model of the phenomenon is the )] TJ ET
BT 26.250 47.241 Td /F5 9.8 Tf [(phenomenon itself)] TJ ET
BT 105.917 47.241 Td /F1 9.8 Tf [(. To figure out where a computationally irreducible rule set is going to ‘take’ a particular patterns, one has to )] TJ ET
Q
q
15.000 32.956 577.500 744.044 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(particular rules, and act according to the reconstructed reality that they have created \(Weick, 1995\). What makes this process )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(intelligible is the pursuit of certain rules \(or habits\) of cognition and interpretation, which supplies the impetus for the study of )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(implicit cognition and perception.)] TJ ET
BT 26.250 724.262 Td /F1 9.8 Tf [(There is, then, a way of studying organizations and organizing in which )] TJ ET
BT 334.603 724.262 Td /F5 9.8 Tf [(rules rule)] TJ ET
BT 374.695 724.262 Td /F1 9.8 Tf [(. They matter so deeply that they provide the )] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(very foundation for the meaning of speech acts \(Wittgenstein, 1952\), the recognition of patterns of organization, the successful )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(coordination of communicative and strategic action and the design, planning and enactment of organizational tasks )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(\(Moldoveanu & Bauer, 2004\). It may then, interest one, as a student of human action and organizational behavior, to come up )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(with a representation of rules and rule-bound behavior that is at once general enough to accommodate the rich variety of rules )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(and rule-based patterns of behavior and precise enough to allow us to consider detailed questions concerning rule design and )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(its effects on macro-level organizational behavior.)] TJ ET
BT 26.250 616.231 Td /F4 12.0 Tf [(The rule-based study of rule-bound systems)] TJ ET
BT 26.250 596.277 Td /F1 9.8 Tf [(Stephen Wolfram \(1985, 1993, 2002\) suggested that )] TJ ET
BT 256.048 596.277 Td /F5 9.8 Tf [(computation)] TJ ET
BT 309.156 596.277 Td /F1 9.8 Tf [( can provide a powerful model for physical processes, and )] TJ ET
BT 26.250 584.372 Td /F1 9.8 Tf [(that )] TJ ET
BT 45.224 584.372 Td /F5 9.8 Tf [(this)] TJ ET
BT 60.395 584.372 Td /F1 9.8 Tf [( fact has important consequences for the predictability and tractability of problems in physics. He points to the following )] TJ ET
BT 26.250 572.467 Td /F1 9.8 Tf [(interesting relationship between computation and physical dynamics: “On one hand, theoretical models describe physical )] TJ ET
BT 26.250 560.562 Td /F1 9.8 Tf [(processes by computations that transform initial data according to algorithms representing physical laws. And, on the other )] TJ ET
BT 26.250 548.658 Td /F1 9.8 Tf [(hand, computers themselves \(which we use to run the said computations—author’s note\) are physical systems obeying physical )] TJ ET
BT 26.250 536.753 Td /F1 9.8 Tf [(laws” \(Wolfram, 1985.\) He explores the implications of this duality for the frequency of intractable or not-decidable-in-a-finite-)] TJ ET
BT 26.250 524.848 Td /F1 9.8 Tf [(time problems in physics, as follows:)] TJ ET
0.965 0.965 0.965 rg
26.250 353.075 555.000 161.893 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 514.967 m 581.250 514.967 l 581.250 514.217 l 26.250 514.217 l f
26.250 353.075 m 581.250 353.075 l 581.250 353.825 l 26.250 353.825 l f
0.271 0.267 0.267 rg
BT 41.206 495.711 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 495.693 Td /F1 9.8 Tf [(First, he shows that computations can be 1. modeled by the long-run evolution of cellular automata \(CA\), and therefore )] TJ ET
BT 54.750 483.789 Td /F1 9.8 Tf [(that predicting the evolution of a CA is equivalent to predicting the evolution of the dynamical system that the rule set )] TJ ET
BT 54.750 471.884 Td /F1 9.8 Tf [(embodied in the CA models;)] TJ ET
BT 41.206 448.747 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 448.729 Td /F1 9.8 Tf [(Next, he argues that some calculations 2. may be computationally irreducible, in the sense that the only way in which )] TJ ET
BT 54.750 436.824 Td /F1 9.8 Tf [(one can predict the evolution of the CA that corresponds to them is to follow the long-run evolution of that CA: there )] TJ ET
BT 54.750 424.920 Td /F1 9.8 Tf [(are, in these cases, no short cuts;)] TJ ET
BT 41.206 401.783 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 401.765 Td /F1 9.8 Tf [(Finally, he shows that there are large classes 3. of CAs \(‘universal computers’, capable of simulating any other finite )] TJ ET
BT 54.750 389.860 Td /F1 9.8 Tf [(computational device of the same dimension\) for which the problem of predicting the long-run evolution is intractable; )] TJ ET
BT 54.750 377.955 Td /F1 9.8 Tf [(moreover, such CAs are far more frequent than are CAs allowing ‘computational shortcuts.)] TJ ET
BT 26.250 336.051 Td /F1 9.8 Tf [(The ‘surprising insight’ is that, in the physical world, there are many more intractable problems than there are tractable ones. It )] TJ ET
BT 26.250 324.146 Td /F1 9.8 Tf [(seems at least prima facie interesting to ask: to what extent does any of this make a difference to the problems faced by )] TJ ET
BT 26.250 312.241 Td /F1 9.8 Tf [(organizational modelers? )] TJ ET
BT 137.878 312.241 Td /F5 9.8 Tf [(This)] TJ ET
BT 156.296 312.241 Td /F1 9.8 Tf [( in the previous sentence has, to be sure, )] TJ ET
BT 337.314 312.241 Td /F5 9.8 Tf [(two)] TJ ET
BT 352.485 312.241 Td /F1 9.8 Tf [( parts: the )] TJ ET
BT 398.554 312.241 Td /F5 9.8 Tf [(first)] TJ ET
BT 414.261 312.241 Td /F1 9.8 Tf [( has to do with the basic modeling )] TJ ET
BT 26.250 300.336 Td /F1 9.8 Tf [(move that Wolfram makes \(physical systems as general purpose computers, their evolutions as computations, computations as )] TJ ET
BT 26.250 288.432 Td /F1 9.8 Tf [(states of a CA, CA as a generalized model for the evolution of a physical system\). The )] TJ ET
BT 400.709 288.432 Td /F5 9.8 Tf [(second)] TJ ET
BT 432.143 288.432 Td /F1 9.8 Tf [( has to do with the issue of )] TJ ET
BT 26.250 276.527 Td /F5 9.8 Tf [(complexity)] TJ ET
BT 72.299 276.527 Td /F1 9.8 Tf [(: suppose organizations )] TJ ET
BT 177.980 276.527 Td /F5 9.8 Tf [(could)] TJ ET
BT 201.282 276.527 Td /F1 9.8 Tf [( be thought of as universal computers, what—if anything—could we say about the )] TJ ET
BT 26.250 264.622 Td /F1 9.8 Tf [(complexity of organizational phenomena understood as intermediate and final steps in \(sometimes very lengthy\) computations?)] TJ ET
BT 26.250 245.217 Td /F1 9.8 Tf [(Let us take seriously the basic model of organizational states as states of a CA—with \(potentially very complex\) rule systems in )] TJ ET
BT 26.250 233.313 Td /F1 9.8 Tf [(place. In the ‘worst case’—one in which we do not agree on just what the ‘right’ rules that govern interactions should be—we )] TJ ET
BT 26.250 221.408 Td /F1 9.8 Tf [(can simply assume that the CA which models the organization is the CA corresponding to the total set of elementary particles in )] TJ ET
BT 26.250 209.503 Td /F1 9.8 Tf [(the organization, interacting according to a rule system that corresponds to the fundamental laws of physics. \(As Wolfram is )] TJ ET
BT 26.250 197.598 Td /F1 9.8 Tf [(quick to point out \(Wolfram, 1985\), the modeler’s basic choice of ontology \(‘particles’ versus ‘fields’ should not matter from the )] TJ ET
BT 26.250 185.694 Td /F1 9.8 Tf [(point of view of the validity of a CA model, as CAs can be used to model discrete-form versions of the partial differential )] TJ ET
BT 26.250 173.789 Td /F1 9.8 Tf [(equations that are used to model the space-time distribution and evolution of a field\). It is not difficult to see that the problem of )] TJ ET
BT 26.250 161.884 Td /F1 9.8 Tf [(predicting the evolution of an organization over a significantly long period of—in general—intractable: even the two-body )] TJ ET
BT 26.250 149.979 Td /F1 9.8 Tf [(problem is intractable for certain kinetic energy levels \(Casti, 1991\). So, we have good reason to ask: why—and, when could we )] TJ ET
BT 26.250 138.075 Td /F1 9.8 Tf [(reasonably—harbor any confidence that we can )] TJ ET
BT 235.436 138.075 Td /F5 9.8 Tf [(predict)] TJ ET
BT 264.696 138.075 Td /F1 9.8 Tf [( anything at all about the evolution of an organization over periods of )] TJ ET
BT 26.250 126.170 Td /F1 9.8 Tf [(time that matter?)] TJ ET
BT 26.250 106.765 Td /F1 9.8 Tf [(Wolfram’s CA-based approach offers both a sobering answer to such questions and the prospect of a framework that allows us )] TJ ET
BT 26.250 94.860 Td /F1 9.8 Tf [(to ask fundamental questions about predictability of organizational phenomena in the first place. In particular, if very large )] TJ ET
BT 26.250 82.956 Td /F1 9.8 Tf [(classes of formal CAs represent ‘universal computers’ and there is no reason to )] TJ ET
BT 371.966 82.956 Td /F5 9.8 Tf [(ex ante)] TJ ET
BT 403.946 82.956 Td /F1 9.8 Tf [( exclude them as valid models for )] TJ ET
BT 26.250 71.051 Td /F1 9.8 Tf [(organizations, then, the probability that a particular organizational phenomenon is computationally irreducible will )] TJ ET
BT 514.501 71.051 Td /F5 9.8 Tf [(ex ante)] TJ ET
BT 546.481 71.051 Td /F1 9.8 Tf [( be )] TJ ET
BT 26.250 59.146 Td /F1 9.8 Tf [(high. This means that, for a large class of organizational phenomena, )] TJ ET
BT 327.564 59.146 Td /F5 9.8 Tf [(the best predictive model of the phenomenon is the )] TJ ET
BT 26.250 47.241 Td /F5 9.8 Tf [(phenomenon itself)] TJ ET
BT 105.917 47.241 Td /F1 9.8 Tf [(. To figure out where a computationally irreducible rule set is going to ‘take’ a particular patterns, one has to )] TJ ET
Q
q
15.000 32.956 577.500 744.044 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(particular rules, and act according to the reconstructed reality that they have created \(Weick, 1995\). What makes this process )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(intelligible is the pursuit of certain rules \(or habits\) of cognition and interpretation, which supplies the impetus for the study of )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(implicit cognition and perception.)] TJ ET
BT 26.250 724.262 Td /F1 9.8 Tf [(There is, then, a way of studying organizations and organizing in which )] TJ ET
BT 334.603 724.262 Td /F5 9.8 Tf [(rules rule)] TJ ET
BT 374.695 724.262 Td /F1 9.8 Tf [(. They matter so deeply that they provide the )] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(very foundation for the meaning of speech acts \(Wittgenstein, 1952\), the recognition of patterns of organization, the successful )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(coordination of communicative and strategic action and the design, planning and enactment of organizational tasks )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(\(Moldoveanu & Bauer, 2004\). It may then, interest one, as a student of human action and organizational behavior, to come up )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(with a representation of rules and rule-bound behavior that is at once general enough to accommodate the rich variety of rules )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(and rule-based patterns of behavior and precise enough to allow us to consider detailed questions concerning rule design and )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(its effects on macro-level organizational behavior.)] TJ ET
BT 26.250 616.231 Td /F4 12.0 Tf [(The rule-based study of rule-bound systems)] TJ ET
BT 26.250 596.277 Td /F1 9.8 Tf [(Stephen Wolfram \(1985, 1993, 2002\) suggested that )] TJ ET
BT 256.048 596.277 Td /F5 9.8 Tf [(computation)] TJ ET
BT 309.156 596.277 Td /F1 9.8 Tf [( can provide a powerful model for physical processes, and )] TJ ET
BT 26.250 584.372 Td /F1 9.8 Tf [(that )] TJ ET
BT 45.224 584.372 Td /F5 9.8 Tf [(this)] TJ ET
BT 60.395 584.372 Td /F1 9.8 Tf [( fact has important consequences for the predictability and tractability of problems in physics. He points to the following )] TJ ET
BT 26.250 572.467 Td /F1 9.8 Tf [(interesting relationship between computation and physical dynamics: “On one hand, theoretical models describe physical )] TJ ET
BT 26.250 560.562 Td /F1 9.8 Tf [(processes by computations that transform initial data according to algorithms representing physical laws. And, on the other )] TJ ET
BT 26.250 548.658 Td /F1 9.8 Tf [(hand, computers themselves \(which we use to run the said computations—author’s note\) are physical systems obeying physical )] TJ ET
BT 26.250 536.753 Td /F1 9.8 Tf [(laws” \(Wolfram, 1985.\) He explores the implications of this duality for the frequency of intractable or not-decidable-in-a-finite-)] TJ ET
BT 26.250 524.848 Td /F1 9.8 Tf [(time problems in physics, as follows:)] TJ ET
0.965 0.965 0.965 rg
26.250 353.075 555.000 161.893 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 514.967 m 581.250 514.967 l 581.250 514.217 l 26.250 514.217 l f
26.250 353.075 m 581.250 353.075 l 581.250 353.825 l 26.250 353.825 l f
0.271 0.267 0.267 rg
BT 41.206 495.711 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 495.693 Td /F1 9.8 Tf [(First, he shows that computations can be 1. modeled by the long-run evolution of cellular automata \(CA\), and therefore )] TJ ET
BT 54.750 483.789 Td /F1 9.8 Tf [(that predicting the evolution of a CA is equivalent to predicting the evolution of the dynamical system that the rule set )] TJ ET
BT 54.750 471.884 Td /F1 9.8 Tf [(embodied in the CA models;)] TJ ET
BT 41.206 448.747 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 448.729 Td /F1 9.8 Tf [(Next, he argues that some calculations 2. may be computationally irreducible, in the sense that the only way in which )] TJ ET
BT 54.750 436.824 Td /F1 9.8 Tf [(one can predict the evolution of the CA that corresponds to them is to follow the long-run evolution of that CA: there )] TJ ET
BT 54.750 424.920 Td /F1 9.8 Tf [(are, in these cases, no short cuts;)] TJ ET
BT 41.206 401.783 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 401.765 Td /F1 9.8 Tf [(Finally, he shows that there are large classes 3. of CAs \(‘universal computers’, capable of simulating any other finite )] TJ ET
BT 54.750 389.860 Td /F1 9.8 Tf [(computational device of the same dimension\) for which the problem of predicting the long-run evolution is intractable; )] TJ ET
BT 54.750 377.955 Td /F1 9.8 Tf [(moreover, such CAs are far more frequent than are CAs allowing ‘computational shortcuts.)] TJ ET
BT 26.250 336.051 Td /F1 9.8 Tf [(The ‘surprising insight’ is that, in the physical world, there are many more intractable problems than there are tractable ones. It )] TJ ET
BT 26.250 324.146 Td /F1 9.8 Tf [(seems at least prima facie interesting to ask: to what extent does any of this make a difference to the problems faced by )] TJ ET
BT 26.250 312.241 Td /F1 9.8 Tf [(organizational modelers? )] TJ ET
BT 137.878 312.241 Td /F5 9.8 Tf [(This)] TJ ET
BT 156.296 312.241 Td /F1 9.8 Tf [( in the previous sentence has, to be sure, )] TJ ET
BT 337.314 312.241 Td /F5 9.8 Tf [(two)] TJ ET
BT 352.485 312.241 Td /F1 9.8 Tf [( parts: the )] TJ ET
BT 398.554 312.241 Td /F5 9.8 Tf [(first)] TJ ET
BT 414.261 312.241 Td /F1 9.8 Tf [( has to do with the basic modeling )] TJ ET
BT 26.250 300.336 Td /F1 9.8 Tf [(move that Wolfram makes \(physical systems as general purpose computers, their evolutions as computations, computations as )] TJ ET
BT 26.250 288.432 Td /F1 9.8 Tf [(states of a CA, CA as a generalized model for the evolution of a physical system\). The )] TJ ET
BT 400.709 288.432 Td /F5 9.8 Tf [(second)] TJ ET
BT 432.143 288.432 Td /F1 9.8 Tf [( has to do with the issue of )] TJ ET
BT 26.250 276.527 Td /F5 9.8 Tf [(complexity)] TJ ET
BT 72.299 276.527 Td /F1 9.8 Tf [(: suppose organizations )] TJ ET
BT 177.980 276.527 Td /F5 9.8 Tf [(could)] TJ ET
BT 201.282 276.527 Td /F1 9.8 Tf [( be thought of as universal computers, what—if anything—could we say about the )] TJ ET
BT 26.250 264.622 Td /F1 9.8 Tf [(complexity of organizational phenomena understood as intermediate and final steps in \(sometimes very lengthy\) computations?)] TJ ET
BT 26.250 245.217 Td /F1 9.8 Tf [(Let us take seriously the basic model of organizational states as states of a CA—with \(potentially very complex\) rule systems in )] TJ ET
BT 26.250 233.313 Td /F1 9.8 Tf [(place. In the ‘worst case’—one in which we do not agree on just what the ‘right’ rules that govern interactions should be—we )] TJ ET
BT 26.250 221.408 Td /F1 9.8 Tf [(can simply assume that the CA which models the organization is the CA corresponding to the total set of elementary particles in )] TJ ET
BT 26.250 209.503 Td /F1 9.8 Tf [(the organization, interacting according to a rule system that corresponds to the fundamental laws of physics. \(As Wolfram is )] TJ ET
BT 26.250 197.598 Td /F1 9.8 Tf [(quick to point out \(Wolfram, 1985\), the modeler’s basic choice of ontology \(‘particles’ versus ‘fields’ should not matter from the )] TJ ET
BT 26.250 185.694 Td /F1 9.8 Tf [(point of view of the validity of a CA model, as CAs can be used to model discrete-form versions of the partial differential )] TJ ET
BT 26.250 173.789 Td /F1 9.8 Tf [(equations that are used to model the space-time distribution and evolution of a field\). It is not difficult to see that the problem of )] TJ ET
BT 26.250 161.884 Td /F1 9.8 Tf [(predicting the evolution of an organization over a significantly long period of—in general—intractable: even the two-body )] TJ ET
BT 26.250 149.979 Td /F1 9.8 Tf [(problem is intractable for certain kinetic energy levels \(Casti, 1991\). So, we have good reason to ask: why—and, when could we )] TJ ET
BT 26.250 138.075 Td /F1 9.8 Tf [(reasonably—harbor any confidence that we can )] TJ ET
BT 235.436 138.075 Td /F5 9.8 Tf [(predict)] TJ ET
BT 264.696 138.075 Td /F1 9.8 Tf [( anything at all about the evolution of an organization over periods of )] TJ ET
BT 26.250 126.170 Td /F1 9.8 Tf [(time that matter?)] TJ ET
BT 26.250 106.765 Td /F1 9.8 Tf [(Wolfram’s CA-based approach offers both a sobering answer to such questions and the prospect of a framework that allows us )] TJ ET
BT 26.250 94.860 Td /F1 9.8 Tf [(to ask fundamental questions about predictability of organizational phenomena in the first place. In particular, if very large )] TJ ET
BT 26.250 82.956 Td /F1 9.8 Tf [(classes of formal CAs represent ‘universal computers’ and there is no reason to )] TJ ET
BT 371.966 82.956 Td /F5 9.8 Tf [(ex ante)] TJ ET
BT 403.946 82.956 Td /F1 9.8 Tf [( exclude them as valid models for )] TJ ET
BT 26.250 71.051 Td /F1 9.8 Tf [(organizations, then, the probability that a particular organizational phenomenon is computationally irreducible will )] TJ ET
BT 514.501 71.051 Td /F5 9.8 Tf [(ex ante)] TJ ET
BT 546.481 71.051 Td /F1 9.8 Tf [( be )] TJ ET
BT 26.250 59.146 Td /F1 9.8 Tf [(high. This means that, for a large class of organizational phenomena, )] TJ ET
BT 327.564 59.146 Td /F5 9.8 Tf [(the best predictive model of the phenomenon is the )] TJ ET
BT 26.250 47.241 Td /F5 9.8 Tf [(phenomenon itself)] TJ ET
BT 105.917 47.241 Td /F1 9.8 Tf [(. To figure out where a computationally irreducible rule set is going to ‘take’ a particular patterns, one has to )] TJ ET
Q
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0.000 0.000 0.000 rg
BT 291.710 19.825 Td /F1 11.0 Tf [(3)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
Q
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BT 26.250 767.476 Td /F1 9.8 Tf [(allow the evolution of that CA to ‘play out’ over whatever length of time one is interested in making predictions over. One might )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(argue, however, that to do so means to abandon any predictive effort in the first place, because as )] TJ ET
BT 452.774 755.571 Td /F5 9.8 Tf [(pre)] TJ ET
BT 466.862 755.571 Td /F1 9.8 Tf [(-dicting a behavior can at )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(best an event that is is )] TJ ET
BT 125.973 743.667 Td /F5 9.8 Tf [(simultaneous)] TJ ET
BT 183.410 743.667 Td /F1 9.8 Tf [( to the behavior being predicted. One might also argue that most of the predictive )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(‘successes’ of social sciences are illusory, which is not an untenable argument given the false conflation of prediction with )] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(explanation that has colored the epistemology of the social sciences \(see Friedman, 1953\).)] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(To all this one might object with a recitation of the many predictive successes that we experience everyday, relating to )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(macroscopically observable but microscopically rule-bound patters. For instance, ‘wanting’ to flip on a light knob can be )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(understood as a desire coupled to a successful prediction \( “if I extend my arm, catch the knob between my thumb and index )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(finger, twist the know, etc… then I will have turned on the light”\) which relates to a potentially very complex pattern of behavior )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(\(all of the elementary particles making up the parts of my body engaged in the activity, all of the elementary particles that )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(constitute the light and the knob, etc\). )] TJ ET
BT 191.015 640.929 Td /F5 9.8 Tf [(Countless)] TJ ET
BT 234.364 640.929 Td /F1 9.8 Tf [( other examples can be constructed, and it is this countlessness that one )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(usually rests arguments about the simplicity and ubiquity of predictive behavior on. However, as we have learned from Georg )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(Cantor and his followers, )] TJ ET
BT 136.250 617.119 Td /F5 9.8 Tf [(uncountability)] TJ ET
BT 195.861 617.119 Td /F1 9.8 Tf [( comes in different classes, that can be distinguished according to their relative densities )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(or measures. There are )] TJ ET
BT 130.829 605.214 Td /F5 9.8 Tf [(uncountably many)] TJ ET
BT 209.950 605.214 Td /F1 9.8 Tf [( natural numbers, for instance, in the sense that one could count them only by an )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(endless counting process; whereas there are uncountably many real numbers in the sense that no mapping of these entities to )] TJ ET
BT 26.250 581.405 Td /F1 9.8 Tf [(a natural number counter is possible. Not surprisingly—and, relevantly—the )] TJ ET
BT 355.166 581.405 Td /F5 9.8 Tf [(size)] TJ ET
BT 372.502 581.405 Td /F1 9.8 Tf [( of the former class is )] TJ ET
BT 467.867 581.405 Td /F5 9.8 Tf [(infinitesimally small)] TJ ET
BT 26.250 569.500 Td /F1 9.8 Tf [(compared to the size of the latter. By a similar argument, it could well be that the class of predictable macroscopic patterns of )] TJ ET
BT 26.250 557.595 Td /F1 9.8 Tf [(behavior is infinitesimally small compared to the class of fundamentally unpredictable patterns of behavior \(those modeled by )] TJ ET
BT 26.250 545.691 Td /F1 9.8 Tf [(computationally irreducible CAs\). Given such an argument, the predictive successes of everyday life could be understood either )] TJ ET
BT 26.250 533.786 Td /F1 9.8 Tf [(as )] TJ ET
BT 39.257 533.786 Td /F5 9.8 Tf [(very low probability accidents)] TJ ET
BT 166.046 533.786 Td /F1 9.8 Tf [( —not likely, because of the very high reliability with which we can produce them—or— more )] TJ ET
BT 26.250 521.881 Td /F1 9.8 Tf [(likely—as carefully )] TJ ET
BT 109.681 521.881 Td /F5 9.8 Tf [(contrived)] TJ ET
BT 149.237 521.881 Td /F1 9.8 Tf [( scenarios in which we have learned to design predictive )] TJ ET
BT 395.258 521.881 Td /F5 9.8 Tf [(short cuts)] TJ ET
BT 437.524 521.881 Td /F1 9.8 Tf [(. It could be, for instance, that )] TJ ET
BT 26.250 509.976 Td /F1 9.8 Tf [(there are )] TJ ET
BT 67.980 509.976 Td /F5 9.8 Tf [(many)] TJ ET
BT 91.819 509.976 Td /F1 9.8 Tf [( possible paths by which all of the elementary particles in one’s body can interact with one another to produce )] TJ ET
BT 26.250 498.072 Td /F1 9.8 Tf [(the complex macroscopic behavior ‘turning on the light knob’, but, by a long process of design and feedback, the mind-body )] TJ ET
BT 26.250 486.167 Td /F1 9.8 Tf [(system has learned to constrain the set of initial conditions in a way that drives the CA which describes the entire system )] TJ ET
BT 26.250 474.262 Td /F1 9.8 Tf [(repeatedly to the same outcome. If this argument is even remotely plausible \(and we have good reason to believe that it is\), )] TJ ET
BT 26.250 462.357 Td /F1 9.8 Tf [(then, it makes more sense to be perplexed \(and, as researchers, intrigued\) by our predictive successes \(how are they possible )] TJ ET
BT 26.250 450.453 Td /F1 9.8 Tf [(and when are they likely?\) than by our predictive failures \(if the world is described by law-like relationships between events, why )] TJ ET
BT 26.250 438.548 Td /F1 9.8 Tf [(can we not make more accurate and reliable predictions?\). The science of ‘organizational complexity’, then, is turned upside )] TJ ET
BT 26.250 426.643 Td /F1 9.8 Tf [(down as its ‘organizing question’ changes from ‘whither complexity?’ to ‘whither simplicity?: Why, when and how is it possible to )] TJ ET
BT 26.250 414.738 Td /F1 9.8 Tf [(make accurate predictions reliably, given that many the systems we make predictions about are likely to be computationally )] TJ ET
BT 26.250 402.834 Td /F1 9.8 Tf [(irreducible and thus the problem of predicting their evolution is likely to be uncomputable?’)] TJ ET
BT 26.250 366.231 Td /F4 12.0 Tf [(The nature and structure of bounds to predictive intelligence)] TJ ET
BT 26.250 346.277 Td /F1 9.8 Tf [(Of course, that is not how organization theorists usually think about the problem of complexity—just the opposite, in fact. They )] TJ ET
BT 26.250 334.372 Td /F1 9.8 Tf [(start from a \(hypothetical\) state in which the researcher or observer can make ‘perfect’ predictions about the evolution of the )] TJ ET
BT 26.250 322.467 Td /F1 9.8 Tf [(system under consideration and posit ‘sources of difficulties’ that he will encounter when trying to predict more accurately or )] TJ ET
BT 26.250 310.563 Td /F1 9.8 Tf [(more reliably; such as:)] TJ ET
0.965 0.965 0.965 rg
26.250 54.801 555.000 245.881 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 300.682 m 581.250 300.682 l 581.250 299.932 l 26.250 299.932 l f
26.250 54.801 m 581.250 54.801 l 581.250 55.551 l 26.250 55.551 l f
0.271 0.267 0.267 rg
0.271 0.267 0.267 RG
49.337 284.376 m
49.337 284.822 49.154 285.266 48.838 285.582 c
48.522 285.898 48.078 286.082 47.631 286.082 c
47.185 286.082 46.741 285.898 46.425 285.582 c
46.109 285.266 45.925 284.822 45.925 284.376 c
45.925 283.929 46.109 283.485 46.425 283.169 c
46.741 282.853 47.185 282.669 47.631 282.669 c
48.078 282.669 48.522 282.853 48.838 283.169 c
49.154 283.485 49.337 283.929 49.337 284.376 c f
BT 54.750 281.408 Td /F5 9.8 Tf [(Bounded computational capabilities)] TJ ET
BT 208.118 281.408 Td /F1 9.8 Tf [(. The principle of bounded computational capabilities has been a foundational )] TJ ET
BT 54.750 269.503 Td /F1 9.8 Tf [(principle of organizational research since Herbert Simon’s early work on bounded rationality. An agent \(individual or )] TJ ET
BT 54.750 257.598 Td /F1 9.8 Tf [(organizational\) is bounded in his or its ability to predict the future by the number of computations per unit time it can )] TJ ET
BT 54.750 245.694 Td /F1 9.8 Tf [(perform, and by the time available to perform these computations. In the case of computationally irreducible rule-bound )] TJ ET
BT 54.750 233.789 Td /F1 9.8 Tf [(systems, computational problems are particularly relevant, as the ideal predictor has to essentially build a system that )] TJ ET
BT 54.750 221.884 Td /F1 9.8 Tf [(has at least the computational complexity of the phenomenon that he is trying to make predictions about, and then \(in )] TJ ET
BT 54.750 209.979 Td /F1 9.8 Tf [(order to predict\), he has to ‘run the clock that measures ‘real time’’ faster than the clock that clocks the phenomenon of )] TJ ET
BT 54.750 198.075 Td /F1 9.8 Tf [(interest;)] TJ ET
49.337 177.888 m
49.337 178.334 49.154 178.778 48.838 179.094 c
48.522 179.410 48.078 179.594 47.631 179.594 c
47.185 179.594 46.741 179.410 46.425 179.094 c
46.109 178.778 45.925 178.334 45.925 177.888 c
45.925 177.441 46.109 176.997 46.425 176.681 c
46.741 176.365 47.185 176.181 47.631 176.181 c
48.078 176.181 48.522 176.365 48.838 176.681 c
49.154 176.997 49.337 177.441 49.337 177.888 c f
BT 54.750 174.920 Td /F5 9.8 Tf [(Bounded informational capabilities)] TJ ET
BT 203.233 174.920 Td /F1 9.8 Tf [(. Bounded rationality also has a second, )] TJ ET
BT 378.830 174.920 Td /F5 9.8 Tf [(informational)] TJ ET
BT 434.639 174.920 Td /F1 9.8 Tf [( component. Simon argues )] TJ ET
BT 54.750 163.015 Td /F1 9.8 Tf [(that upper bounds on short term memory \(or short term memory utilization\) are fundamental invariants of human )] TJ ET
BT 54.750 151.110 Td /F1 9.8 Tf [(behavior—and, by extension, of organizational behavior as well \(Simon, 1990\). Even if )] TJ ET
BT 429.755 151.110 Td /F5 9.8 Tf [(local)] TJ ET
BT 449.800 151.110 Td /F1 9.8 Tf [( rule systems could be )] TJ ET
BT 54.750 139.206 Td /F1 9.8 Tf [(designed with arbitrarily high complexity to alleviate the computational costs of using these rules to simulate macro-)] TJ ET
BT 54.750 127.301 Td /F1 9.8 Tf [(behavior, it is still the case that short-term memory bounds would limit the space in which these rules could be )] TJ ET
BT 54.750 115.396 Td /F1 9.8 Tf [(searched for. These ‘difficulties’—in the classical formulations of the problem of prediction—are what accounts for the )] TJ ET
BT 54.750 103.491 Td /F1 9.8 Tf [(predictive failures of the researcher or observer to instantiate a perfect simulation of Laplace’s demon—that figment of )] TJ ET
BT 54.750 91.587 Td /F1 9.8 Tf [(the 19)] TJ ET
BT 81.855 95.475 Td /F1 8.7 Tf [(th)] TJ ET
BT 89.083 91.587 Td /F1 9.8 Tf [( century imagination of \(some\) positivists who thought that knowledge of microscopic laws seamlessly )] TJ ET
BT 54.750 79.682 Td /F1 9.8 Tf [(translated into knowledge of the macroscopic patterns these laws give rise to.)] TJ ET
BT 26.250 37.777 Td /F1 9.8 Tf [(By contrast, the ‘new science’ that Wolfram \(2002\) points to is called upon to explain predictive successes—rather than )] TJ ET
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15.000 23.491 577.500 753.509 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(allow the evolution of that CA to ‘play out’ over whatever length of time one is interested in making predictions over. One might )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(argue, however, that to do so means to abandon any predictive effort in the first place, because as )] TJ ET
BT 452.774 755.571 Td /F5 9.8 Tf [(pre)] TJ ET
BT 466.862 755.571 Td /F1 9.8 Tf [(-dicting a behavior can at )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(best an event that is is )] TJ ET
BT 125.973 743.667 Td /F5 9.8 Tf [(simultaneous)] TJ ET
BT 183.410 743.667 Td /F1 9.8 Tf [( to the behavior being predicted. One might also argue that most of the predictive )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(‘successes’ of social sciences are illusory, which is not an untenable argument given the false conflation of prediction with )] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(explanation that has colored the epistemology of the social sciences \(see Friedman, 1953\).)] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(To all this one might object with a recitation of the many predictive successes that we experience everyday, relating to )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(macroscopically observable but microscopically rule-bound patters. For instance, ‘wanting’ to flip on a light knob can be )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(understood as a desire coupled to a successful prediction \( “if I extend my arm, catch the knob between my thumb and index )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(finger, twist the know, etc… then I will have turned on the light”\) which relates to a potentially very complex pattern of behavior )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(\(all of the elementary particles making up the parts of my body engaged in the activity, all of the elementary particles that )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(constitute the light and the knob, etc\). )] TJ ET
BT 191.015 640.929 Td /F5 9.8 Tf [(Countless)] TJ ET
BT 234.364 640.929 Td /F1 9.8 Tf [( other examples can be constructed, and it is this countlessness that one )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(usually rests arguments about the simplicity and ubiquity of predictive behavior on. However, as we have learned from Georg )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(Cantor and his followers, )] TJ ET
BT 136.250 617.119 Td /F5 9.8 Tf [(uncountability)] TJ ET
BT 195.861 617.119 Td /F1 9.8 Tf [( comes in different classes, that can be distinguished according to their relative densities )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(or measures. There are )] TJ ET
BT 130.829 605.214 Td /F5 9.8 Tf [(uncountably many)] TJ ET
BT 209.950 605.214 Td /F1 9.8 Tf [( natural numbers, for instance, in the sense that one could count them only by an )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(endless counting process; whereas there are uncountably many real numbers in the sense that no mapping of these entities to )] TJ ET
BT 26.250 581.405 Td /F1 9.8 Tf [(a natural number counter is possible. Not surprisingly—and, relevantly—the )] TJ ET
BT 355.166 581.405 Td /F5 9.8 Tf [(size)] TJ ET
BT 372.502 581.405 Td /F1 9.8 Tf [( of the former class is )] TJ ET
BT 467.867 581.405 Td /F5 9.8 Tf [(infinitesimally small)] TJ ET
BT 26.250 569.500 Td /F1 9.8 Tf [(compared to the size of the latter. By a similar argument, it could well be that the class of predictable macroscopic patterns of )] TJ ET
BT 26.250 557.595 Td /F1 9.8 Tf [(behavior is infinitesimally small compared to the class of fundamentally unpredictable patterns of behavior \(those modeled by )] TJ ET
BT 26.250 545.691 Td /F1 9.8 Tf [(computationally irreducible CAs\). Given such an argument, the predictive successes of everyday life could be understood either )] TJ ET
BT 26.250 533.786 Td /F1 9.8 Tf [(as )] TJ ET
BT 39.257 533.786 Td /F5 9.8 Tf [(very low probability accidents)] TJ ET
BT 166.046 533.786 Td /F1 9.8 Tf [( —not likely, because of the very high reliability with which we can produce them—or— more )] TJ ET
BT 26.250 521.881 Td /F1 9.8 Tf [(likely—as carefully )] TJ ET
BT 109.681 521.881 Td /F5 9.8 Tf [(contrived)] TJ ET
BT 149.237 521.881 Td /F1 9.8 Tf [( scenarios in which we have learned to design predictive )] TJ ET
BT 395.258 521.881 Td /F5 9.8 Tf [(short cuts)] TJ ET
BT 437.524 521.881 Td /F1 9.8 Tf [(. It could be, for instance, that )] TJ ET
BT 26.250 509.976 Td /F1 9.8 Tf [(there are )] TJ ET
BT 67.980 509.976 Td /F5 9.8 Tf [(many)] TJ ET
BT 91.819 509.976 Td /F1 9.8 Tf [( possible paths by which all of the elementary particles in one’s body can interact with one another to produce )] TJ ET
BT 26.250 498.072 Td /F1 9.8 Tf [(the complex macroscopic behavior ‘turning on the light knob’, but, by a long process of design and feedback, the mind-body )] TJ ET
BT 26.250 486.167 Td /F1 9.8 Tf [(system has learned to constrain the set of initial conditions in a way that drives the CA which describes the entire system )] TJ ET
BT 26.250 474.262 Td /F1 9.8 Tf [(repeatedly to the same outcome. If this argument is even remotely plausible \(and we have good reason to believe that it is\), )] TJ ET
BT 26.250 462.357 Td /F1 9.8 Tf [(then, it makes more sense to be perplexed \(and, as researchers, intrigued\) by our predictive successes \(how are they possible )] TJ ET
BT 26.250 450.453 Td /F1 9.8 Tf [(and when are they likely?\) than by our predictive failures \(if the world is described by law-like relationships between events, why )] TJ ET
BT 26.250 438.548 Td /F1 9.8 Tf [(can we not make more accurate and reliable predictions?\). The science of ‘organizational complexity’, then, is turned upside )] TJ ET
BT 26.250 426.643 Td /F1 9.8 Tf [(down as its ‘organizing question’ changes from ‘whither complexity?’ to ‘whither simplicity?: Why, when and how is it possible to )] TJ ET
BT 26.250 414.738 Td /F1 9.8 Tf [(make accurate predictions reliably, given that many the systems we make predictions about are likely to be computationally )] TJ ET
BT 26.250 402.834 Td /F1 9.8 Tf [(irreducible and thus the problem of predicting their evolution is likely to be uncomputable?’)] TJ ET
BT 26.250 366.231 Td /F4 12.0 Tf [(The nature and structure of bounds to predictive intelligence)] TJ ET
BT 26.250 346.277 Td /F1 9.8 Tf [(Of course, that is not how organization theorists usually think about the problem of complexity—just the opposite, in fact. They )] TJ ET
BT 26.250 334.372 Td /F1 9.8 Tf [(start from a \(hypothetical\) state in which the researcher or observer can make ‘perfect’ predictions about the evolution of the )] TJ ET
BT 26.250 322.467 Td /F1 9.8 Tf [(system under consideration and posit ‘sources of difficulties’ that he will encounter when trying to predict more accurately or )] TJ ET
BT 26.250 310.563 Td /F1 9.8 Tf [(more reliably; such as:)] TJ ET
0.965 0.965 0.965 rg
26.250 54.801 555.000 245.881 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 300.682 m 581.250 300.682 l 581.250 299.932 l 26.250 299.932 l f
26.250 54.801 m 581.250 54.801 l 581.250 55.551 l 26.250 55.551 l f
0.271 0.267 0.267 rg
0.271 0.267 0.267 RG
49.337 284.376 m
49.337 284.822 49.154 285.266 48.838 285.582 c
48.522 285.898 48.078 286.082 47.631 286.082 c
47.185 286.082 46.741 285.898 46.425 285.582 c
46.109 285.266 45.925 284.822 45.925 284.376 c
45.925 283.929 46.109 283.485 46.425 283.169 c
46.741 282.853 47.185 282.669 47.631 282.669 c
48.078 282.669 48.522 282.853 48.838 283.169 c
49.154 283.485 49.337 283.929 49.337 284.376 c f
BT 54.750 281.408 Td /F5 9.8 Tf [(Bounded computational capabilities)] TJ ET
BT 208.118 281.408 Td /F1 9.8 Tf [(. The principle of bounded computational capabilities has been a foundational )] TJ ET
BT 54.750 269.503 Td /F1 9.8 Tf [(principle of organizational research since Herbert Simon’s early work on bounded rationality. An agent \(individual or )] TJ ET
BT 54.750 257.598 Td /F1 9.8 Tf [(organizational\) is bounded in his or its ability to predict the future by the number of computations per unit time it can )] TJ ET
BT 54.750 245.694 Td /F1 9.8 Tf [(perform, and by the time available to perform these computations. In the case of computationally irreducible rule-bound )] TJ ET
BT 54.750 233.789 Td /F1 9.8 Tf [(systems, computational problems are particularly relevant, as the ideal predictor has to essentially build a system that )] TJ ET
BT 54.750 221.884 Td /F1 9.8 Tf [(has at least the computational complexity of the phenomenon that he is trying to make predictions about, and then \(in )] TJ ET
BT 54.750 209.979 Td /F1 9.8 Tf [(order to predict\), he has to ‘run the clock that measures ‘real time’’ faster than the clock that clocks the phenomenon of )] TJ ET
BT 54.750 198.075 Td /F1 9.8 Tf [(interest;)] TJ ET
49.337 177.888 m
49.337 178.334 49.154 178.778 48.838 179.094 c
48.522 179.410 48.078 179.594 47.631 179.594 c
47.185 179.594 46.741 179.410 46.425 179.094 c
46.109 178.778 45.925 178.334 45.925 177.888 c
45.925 177.441 46.109 176.997 46.425 176.681 c
46.741 176.365 47.185 176.181 47.631 176.181 c
48.078 176.181 48.522 176.365 48.838 176.681 c
49.154 176.997 49.337 177.441 49.337 177.888 c f
BT 54.750 174.920 Td /F5 9.8 Tf [(Bounded informational capabilities)] TJ ET
BT 203.233 174.920 Td /F1 9.8 Tf [(. Bounded rationality also has a second, )] TJ ET
BT 378.830 174.920 Td /F5 9.8 Tf [(informational)] TJ ET
BT 434.639 174.920 Td /F1 9.8 Tf [( component. Simon argues )] TJ ET
BT 54.750 163.015 Td /F1 9.8 Tf [(that upper bounds on short term memory \(or short term memory utilization\) are fundamental invariants of human )] TJ ET
BT 54.750 151.110 Td /F1 9.8 Tf [(behavior—and, by extension, of organizational behavior as well \(Simon, 1990\). Even if )] TJ ET
BT 429.755 151.110 Td /F5 9.8 Tf [(local)] TJ ET
BT 449.800 151.110 Td /F1 9.8 Tf [( rule systems could be )] TJ ET
BT 54.750 139.206 Td /F1 9.8 Tf [(designed with arbitrarily high complexity to alleviate the computational costs of using these rules to simulate macro-)] TJ ET
BT 54.750 127.301 Td /F1 9.8 Tf [(behavior, it is still the case that short-term memory bounds would limit the space in which these rules could be )] TJ ET
BT 54.750 115.396 Td /F1 9.8 Tf [(searched for. These ‘difficulties’—in the classical formulations of the problem of prediction—are what accounts for the )] TJ ET
BT 54.750 103.491 Td /F1 9.8 Tf [(predictive failures of the researcher or observer to instantiate a perfect simulation of Laplace’s demon—that figment of )] TJ ET
BT 54.750 91.587 Td /F1 9.8 Tf [(the 19)] TJ ET
BT 81.855 95.475 Td /F1 8.7 Tf [(th)] TJ ET
BT 89.083 91.587 Td /F1 9.8 Tf [( century imagination of \(some\) positivists who thought that knowledge of microscopic laws seamlessly )] TJ ET
BT 54.750 79.682 Td /F1 9.8 Tf [(translated into knowledge of the macroscopic patterns these laws give rise to.)] TJ ET
BT 26.250 37.777 Td /F1 9.8 Tf [(By contrast, the ‘new science’ that Wolfram \(2002\) points to is called upon to explain predictive successes—rather than )] TJ ET
Q
q
15.000 23.491 577.500 753.509 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(allow the evolution of that CA to ‘play out’ over whatever length of time one is interested in making predictions over. One might )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(argue, however, that to do so means to abandon any predictive effort in the first place, because as )] TJ ET
BT 452.774 755.571 Td /F5 9.8 Tf [(pre)] TJ ET
BT 466.862 755.571 Td /F1 9.8 Tf [(-dicting a behavior can at )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(best an event that is is )] TJ ET
BT 125.973 743.667 Td /F5 9.8 Tf [(simultaneous)] TJ ET
BT 183.410 743.667 Td /F1 9.8 Tf [( to the behavior being predicted. One might also argue that most of the predictive )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(‘successes’ of social sciences are illusory, which is not an untenable argument given the false conflation of prediction with )] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(explanation that has colored the epistemology of the social sciences \(see Friedman, 1953\).)] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(To all this one might object with a recitation of the many predictive successes that we experience everyday, relating to )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(macroscopically observable but microscopically rule-bound patters. For instance, ‘wanting’ to flip on a light knob can be )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(understood as a desire coupled to a successful prediction \( “if I extend my arm, catch the knob between my thumb and index )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(finger, twist the know, etc… then I will have turned on the light”\) which relates to a potentially very complex pattern of behavior )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(\(all of the elementary particles making up the parts of my body engaged in the activity, all of the elementary particles that )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(constitute the light and the knob, etc\). )] TJ ET
BT 191.015 640.929 Td /F5 9.8 Tf [(Countless)] TJ ET
BT 234.364 640.929 Td /F1 9.8 Tf [( other examples can be constructed, and it is this countlessness that one )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(usually rests arguments about the simplicity and ubiquity of predictive behavior on. However, as we have learned from Georg )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(Cantor and his followers, )] TJ ET
BT 136.250 617.119 Td /F5 9.8 Tf [(uncountability)] TJ ET
BT 195.861 617.119 Td /F1 9.8 Tf [( comes in different classes, that can be distinguished according to their relative densities )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(or measures. There are )] TJ ET
BT 130.829 605.214 Td /F5 9.8 Tf [(uncountably many)] TJ ET
BT 209.950 605.214 Td /F1 9.8 Tf [( natural numbers, for instance, in the sense that one could count them only by an )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(endless counting process; whereas there are uncountably many real numbers in the sense that no mapping of these entities to )] TJ ET
BT 26.250 581.405 Td /F1 9.8 Tf [(a natural number counter is possible. Not surprisingly—and, relevantly—the )] TJ ET
BT 355.166 581.405 Td /F5 9.8 Tf [(size)] TJ ET
BT 372.502 581.405 Td /F1 9.8 Tf [( of the former class is )] TJ ET
BT 467.867 581.405 Td /F5 9.8 Tf [(infinitesimally small)] TJ ET
BT 26.250 569.500 Td /F1 9.8 Tf [(compared to the size of the latter. By a similar argument, it could well be that the class of predictable macroscopic patterns of )] TJ ET
BT 26.250 557.595 Td /F1 9.8 Tf [(behavior is infinitesimally small compared to the class of fundamentally unpredictable patterns of behavior \(those modeled by )] TJ ET
BT 26.250 545.691 Td /F1 9.8 Tf [(computationally irreducible CAs\). Given such an argument, the predictive successes of everyday life could be understood either )] TJ ET
BT 26.250 533.786 Td /F1 9.8 Tf [(as )] TJ ET
BT 39.257 533.786 Td /F5 9.8 Tf [(very low probability accidents)] TJ ET
BT 166.046 533.786 Td /F1 9.8 Tf [( —not likely, because of the very high reliability with which we can produce them—or— more )] TJ ET
BT 26.250 521.881 Td /F1 9.8 Tf [(likely—as carefully )] TJ ET
BT 109.681 521.881 Td /F5 9.8 Tf [(contrived)] TJ ET
BT 149.237 521.881 Td /F1 9.8 Tf [( scenarios in which we have learned to design predictive )] TJ ET
BT 395.258 521.881 Td /F5 9.8 Tf [(short cuts)] TJ ET
BT 437.524 521.881 Td /F1 9.8 Tf [(. It could be, for instance, that )] TJ ET
BT 26.250 509.976 Td /F1 9.8 Tf [(there are )] TJ ET
BT 67.980 509.976 Td /F5 9.8 Tf [(many)] TJ ET
BT 91.819 509.976 Td /F1 9.8 Tf [( possible paths by which all of the elementary particles in one’s body can interact with one another to produce )] TJ ET
BT 26.250 498.072 Td /F1 9.8 Tf [(the complex macroscopic behavior ‘turning on the light knob’, but, by a long process of design and feedback, the mind-body )] TJ ET
BT 26.250 486.167 Td /F1 9.8 Tf [(system has learned to constrain the set of initial conditions in a way that drives the CA which describes the entire system )] TJ ET
BT 26.250 474.262 Td /F1 9.8 Tf [(repeatedly to the same outcome. If this argument is even remotely plausible \(and we have good reason to believe that it is\), )] TJ ET
BT 26.250 462.357 Td /F1 9.8 Tf [(then, it makes more sense to be perplexed \(and, as researchers, intrigued\) by our predictive successes \(how are they possible )] TJ ET
BT 26.250 450.453 Td /F1 9.8 Tf [(and when are they likely?\) than by our predictive failures \(if the world is described by law-like relationships between events, why )] TJ ET
BT 26.250 438.548 Td /F1 9.8 Tf [(can we not make more accurate and reliable predictions?\). The science of ‘organizational complexity’, then, is turned upside )] TJ ET
BT 26.250 426.643 Td /F1 9.8 Tf [(down as its ‘organizing question’ changes from ‘whither complexity?’ to ‘whither simplicity?: Why, when and how is it possible to )] TJ ET
BT 26.250 414.738 Td /F1 9.8 Tf [(make accurate predictions reliably, given that many the systems we make predictions about are likely to be computationally )] TJ ET
BT 26.250 402.834 Td /F1 9.8 Tf [(irreducible and thus the problem of predicting their evolution is likely to be uncomputable?’)] TJ ET
BT 26.250 366.231 Td /F4 12.0 Tf [(The nature and structure of bounds to predictive intelligence)] TJ ET
BT 26.250 346.277 Td /F1 9.8 Tf [(Of course, that is not how organization theorists usually think about the problem of complexity—just the opposite, in fact. They )] TJ ET
BT 26.250 334.372 Td /F1 9.8 Tf [(start from a \(hypothetical\) state in which the researcher or observer can make ‘perfect’ predictions about the evolution of the )] TJ ET
BT 26.250 322.467 Td /F1 9.8 Tf [(system under consideration and posit ‘sources of difficulties’ that he will encounter when trying to predict more accurately or )] TJ ET
BT 26.250 310.563 Td /F1 9.8 Tf [(more reliably; such as:)] TJ ET
0.965 0.965 0.965 rg
26.250 54.801 555.000 245.881 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 300.682 m 581.250 300.682 l 581.250 299.932 l 26.250 299.932 l f
26.250 54.801 m 581.250 54.801 l 581.250 55.551 l 26.250 55.551 l f
0.271 0.267 0.267 rg
0.271 0.267 0.267 RG
49.337 284.376 m
49.337 284.822 49.154 285.266 48.838 285.582 c
48.522 285.898 48.078 286.082 47.631 286.082 c
47.185 286.082 46.741 285.898 46.425 285.582 c
46.109 285.266 45.925 284.822 45.925 284.376 c
45.925 283.929 46.109 283.485 46.425 283.169 c
46.741 282.853 47.185 282.669 47.631 282.669 c
48.078 282.669 48.522 282.853 48.838 283.169 c
49.154 283.485 49.337 283.929 49.337 284.376 c f
BT 54.750 281.408 Td /F5 9.8 Tf [(Bounded computational capabilities)] TJ ET
BT 208.118 281.408 Td /F1 9.8 Tf [(. The principle of bounded computational capabilities has been a foundational )] TJ ET
BT 54.750 269.503 Td /F1 9.8 Tf [(principle of organizational research since Herbert Simon’s early work on bounded rationality. An agent \(individual or )] TJ ET
BT 54.750 257.598 Td /F1 9.8 Tf [(organizational\) is bounded in his or its ability to predict the future by the number of computations per unit time it can )] TJ ET
BT 54.750 245.694 Td /F1 9.8 Tf [(perform, and by the time available to perform these computations. In the case of computationally irreducible rule-bound )] TJ ET
BT 54.750 233.789 Td /F1 9.8 Tf [(systems, computational problems are particularly relevant, as the ideal predictor has to essentially build a system that )] TJ ET
BT 54.750 221.884 Td /F1 9.8 Tf [(has at least the computational complexity of the phenomenon that he is trying to make predictions about, and then \(in )] TJ ET
BT 54.750 209.979 Td /F1 9.8 Tf [(order to predict\), he has to ‘run the clock that measures ‘real time’’ faster than the clock that clocks the phenomenon of )] TJ ET
BT 54.750 198.075 Td /F1 9.8 Tf [(interest;)] TJ ET
49.337 177.888 m
49.337 178.334 49.154 178.778 48.838 179.094 c
48.522 179.410 48.078 179.594 47.631 179.594 c
47.185 179.594 46.741 179.410 46.425 179.094 c
46.109 178.778 45.925 178.334 45.925 177.888 c
45.925 177.441 46.109 176.997 46.425 176.681 c
46.741 176.365 47.185 176.181 47.631 176.181 c
48.078 176.181 48.522 176.365 48.838 176.681 c
49.154 176.997 49.337 177.441 49.337 177.888 c f
BT 54.750 174.920 Td /F5 9.8 Tf [(Bounded informational capabilities)] TJ ET
BT 203.233 174.920 Td /F1 9.8 Tf [(. Bounded rationality also has a second, )] TJ ET
BT 378.830 174.920 Td /F5 9.8 Tf [(informational)] TJ ET
BT 434.639 174.920 Td /F1 9.8 Tf [( component. Simon argues )] TJ ET
BT 54.750 163.015 Td /F1 9.8 Tf [(that upper bounds on short term memory \(or short term memory utilization\) are fundamental invariants of human )] TJ ET
BT 54.750 151.110 Td /F1 9.8 Tf [(behavior—and, by extension, of organizational behavior as well \(Simon, 1990\). Even if )] TJ ET
BT 429.755 151.110 Td /F5 9.8 Tf [(local)] TJ ET
BT 449.800 151.110 Td /F1 9.8 Tf [( rule systems could be )] TJ ET
BT 54.750 139.206 Td /F1 9.8 Tf [(designed with arbitrarily high complexity to alleviate the computational costs of using these rules to simulate macro-)] TJ ET
BT 54.750 127.301 Td /F1 9.8 Tf [(behavior, it is still the case that short-term memory bounds would limit the space in which these rules could be )] TJ ET
BT 54.750 115.396 Td /F1 9.8 Tf [(searched for. These ‘difficulties’—in the classical formulations of the problem of prediction—are what accounts for the )] TJ ET
BT 54.750 103.491 Td /F1 9.8 Tf [(predictive failures of the researcher or observer to instantiate a perfect simulation of Laplace’s demon—that figment of )] TJ ET
BT 54.750 91.587 Td /F1 9.8 Tf [(the 19)] TJ ET
BT 81.855 95.475 Td /F1 8.7 Tf [(th)] TJ ET
BT 89.083 91.587 Td /F1 9.8 Tf [( century imagination of \(some\) positivists who thought that knowledge of microscopic laws seamlessly )] TJ ET
BT 54.750 79.682 Td /F1 9.8 Tf [(translated into knowledge of the macroscopic patterns these laws give rise to.)] TJ ET
BT 26.250 37.777 Td /F1 9.8 Tf [(By contrast, the ‘new science’ that Wolfram \(2002\) points to is called upon to explain predictive successes—rather than )] TJ ET
Q
q
0.000 0.000 0.000 rg
BT 291.710 19.825 Td /F1 11.0 Tf [(4)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
Q
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BT 26.250 767.476 Td /F1 9.8 Tf [(predictive failures. To do so, however, we need a different foundational model of both ‘organizations’ and of \(the process of\) )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(‘organization’. Moldoveanu and Bauer \(2004\) have proposed that a useful model for the organization from the point of view of )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(studying ‘organizational complexity’ is a generalized computational device, such as a Universal Turing Machine \(UTM\). )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(Organizational tasks are conceptualized as computational processes, with ‘software’ \(or, the algorithms that ‘organize’ )] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(organizations\) provided by the plans and strategies of managers or the models used by researchers to represent organizational )] TJ ET
BT 26.250 707.952 Td /F1 9.8 Tf [(phenomena, and the hardware provided by the physical embodiment of the organization. Tasks can be understood as the )] TJ ET
BT 26.250 696.048 Td /F1 9.8 Tf [(processes by which algorithms run on the underlying computational structure. They are states of the computational device that )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(models organizations.)] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(Now, by Wolfram’s argument, the states of this computational device can be modeled by the instantaneous states of a CA, and )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(thus, a CA can be used to model what organizations \()] TJ ET
BT 257.656 652.833 Td /F5 9.8 Tf [(tout court\) do)] TJ ET
BT 315.103 652.833 Td /F1 9.8 Tf [(—and, ask: under what conditions can we make valid )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(predictions about the long-run dynamics of organizational processes? Because there is a straightforward type-type reduction of )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(organizational tasks to computational processes and of computational processes to the evolution of CAs, we can ask: what )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(sorts of organizational tasks lend themselves to predictable CA instantiations, and, under what conditions can we expect to be )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(able to make competent \(accurate and reliable\) predictions? Such a question can take many different forms. One can ask, for )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(instance, a parallel question about the evolution of language \(what are the attributes of words and concepts that ‘cut the world at )] TJ ET
BT 26.250 581.405 Td /F1 9.8 Tf [(the joints?’ \(Nozick, 2002\)—and model the rules by which we assign concepts to representations as the rules used to program )] TJ ET
BT 26.250 569.500 Td /F1 9.8 Tf [(the evolution of a CA; or, about the evolution of ‘ways of knowing’ \(what are ‘good discovery rules’ that account for predictive )] TJ ET
BT 26.250 557.595 Td /F1 9.8 Tf [(success in a particular science?\); or, about the evolution of reasoning more generally \(‘what is it in the nature of the received )] TJ ET
BT 26.250 545.691 Td /F1 9.8 Tf [(‘laws of thought’ that makes logical inference, for instance, a valuable building block for the causal inferences that underscore )] TJ ET
BT 26.250 533.786 Td /F1 9.8 Tf [(predictive successes?\). These problem statements can then be interpreted in the language of CAs in order to come up with )] TJ ET
BT 26.250 521.881 Td /F1 9.8 Tf [(representable—albeit potentially uncomputable—CA states that model the evolution of organizational patterns of behavior.)] TJ ET
BT 26.250 485.279 Td /F4 12.0 Tf [(Cellular automata \(CA\), universality and computational equivalence)] TJ ET
BT 26.250 465.324 Td /F1 9.8 Tf [(The computational modeling of organizational processes relies on the success of a series of reductions: of rule-based )] TJ ET
BT 26.250 453.420 Td /F1 9.8 Tf [(organizational phenomena to rule-based computational processes running on universal computational machines \(such as )] TJ ET
BT 26.250 441.515 Td /F1 9.8 Tf [(Universal Turing Machines\), of universal computational devices to CAs, and, quite often, of complex rule sets to simple rule )] TJ ET
BT 26.250 429.610 Td /F1 9.8 Tf [(sets. These reductions, in turn, allow us to reduce the ‘complexity’ of an organizational phenomenon to the rule-complexity, run-)] TJ ET
BT 26.250 417.705 Td /F1 9.8 Tf [(time-complexity and relative compressibility of the cellular automaton patterns that simulate that phenomenon, ‘computation’ writ )] TJ ET
BT 26.250 405.801 Td /F1 9.8 Tf [(large to the evolution of a cellular automaton embodying a particular rule set, the analysis of complexity to a typology of rule )] TJ ET
BT 26.250 393.896 Td /F1 9.8 Tf [(sets and of the evolved CA patterns associated with them, of macro-organizational processes to large scale cellular automaton )] TJ ET
BT 26.250 381.991 Td /F1 9.8 Tf [(patterns and of micro-organizational processes to local rule sets. As with any reductive strategy, we can ask:)] TJ ET
BT 26.250 362.586 Td /F5 9.8 Tf [(Is the strategy general enough to capture phenomena of interest?)] TJ ET
BT 309.166 362.586 Td /F1 9.8 Tf [( The principle of computational representability \(of in-principle )] TJ ET
BT 26.250 350.682 Td /F1 9.8 Tf [(computable functions\) or allows us to represent any organizational process or phenomenon that can in principle be represented. )] TJ ET
BT 26.250 338.777 Td /F1 9.8 Tf [(It states that natural processes can be understood as embodiments of computational processes. It is reasonable to ask if any )] TJ ET
BT 26.250 326.872 Td /F1 9.8 Tf [(phenomenon can properly be understood as a computational process, and, of course, an answer is not easily forthcoming: like )] TJ ET
BT 26.250 314.967 Td /F1 9.8 Tf [(any modeling device, the principle of computational representability is not refutable, because it is a )] TJ ET
BT 453.281 314.967 Td /F5 9.8 Tf [(paradigm)] TJ ET
BT 493.918 314.967 Td /F1 9.8 Tf [( \(Kuhn, 1962; )] TJ ET
BT 26.250 303.063 Td /F1 9.8 Tf [(1990\). It is, however, possible to inquire about the limits of the applicability of the move in particular cases.)] TJ ET
BT 26.250 283.658 Td /F1 9.8 Tf [(A fundamental theorem of computation \(Boolos & Jeffrey, 1993\) states that, if a process is simulable at all, then it is simulable )] TJ ET
BT 26.250 271.753 Td /F1 9.8 Tf [(on a Universal Turing Machine. A representable phenomenon is one that can be modeled using functions mapping independent )] TJ ET
BT 26.250 259.848 Td /F1 9.8 Tf [(to dependent variables. These functions are computable: algorithms that compute them on a computational device converge to )] TJ ET
BT 26.250 247.944 Td /F1 9.8 Tf [(an answer after a \(potentially large but finite\) number of iterations—which can be thought of as point computations. Moreover, if )] TJ ET
BT 26.250 236.039 Td /F1 9.8 Tf [(a function is computable, then it is Turing-computable: it can be implemented on a Universal Turing Machine \(Boolos & Jeffrey, )] TJ ET
BT 26.250 224.134 Td /F1 9.8 Tf [(1993\). Wolfram \(2002\) shows how to build CA representations of Turing machines, and thus how to reduce general-purpose )] TJ ET
BT 26.250 212.229 Td /F1 9.8 Tf [(computational processes to CA processes. Thus, by using the basic strategy shown in Figure 1, we can use this approach to )] TJ ET
BT 26.250 200.325 Td /F1 9.8 Tf [(reduce any organizational phenomenon that can in principle be symbolically represented to a set of CA processes.)] TJ ET
BT 26.250 180.920 Td /F5 9.8 Tf [(Are the resulting findings objective, or, at least, inter-subjective)] TJ ET
BT 296.656 180.920 Td /F1 9.8 Tf [(, i.e., do they depend on a particular embodiment of the )] TJ ET
BT 26.250 169.015 Td /F1 9.8 Tf [(computational device used for simulation? The significant point about Universal Turing Machines is that they are the most )] TJ ET
BT 26.250 157.110 Td /F1 9.8 Tf [(general computational devices available. They can be used to simulate the operation of )] TJ ET
BT 405.067 157.110 Td /F5 9.8 Tf [(any other computational device)] TJ ET
BT 540.007 157.110 Td /F1 9.8 Tf [(—such )] TJ ET
BT 26.250 145.206 Td /F1 9.8 Tf [(as a cellular automaton. Thus, if a process is simulable or representable on a particular CPU architecture \(a Pentium, say\) using )] TJ ET
BT 26.250 133.301 Td /F1 9.8 Tf [(the local set of rules associated with programming it, then it can be simulated using the \(irreducibly simple\) instruction set of a )] TJ ET
BT 26.250 121.396 Td /F1 9.8 Tf [(Turing machine. Our strategy thus allows modelers using different computational devices and different simulation languages )] TJ ET
BT 26.250 109.491 Td /F1 9.8 Tf [(\(such as ‘theories of the organization’, or ‘modeling heuristics’ or ‘models’ simpliciter\) to reach agreement about important )] TJ ET
BT 26.250 97.587 Td /F1 9.8 Tf [(quantities \(such as ‘complexity’\) by translating their models into a more universal rule set and then comparing their findings.)] TJ ET
BT 26.250 78.182 Td /F5 9.8 Tf [(Are the resulting distinctions, models and findings useful and relevant to the study of organizations?)] TJ ET
BT 455.464 78.182 Td /F1 9.8 Tf [( We argue that using the )] TJ ET
BT 26.250 66.277 Td /F1 9.8 Tf [(basic computational strategy outlined in this paper will allow us to draw important new distinctions in the study of organizations, )] TJ ET
BT 26.250 54.372 Td /F1 9.8 Tf [(such as those between the local complexity of the rules of interactions between individual agents and the rules of evolution of )] TJ ET
BT 26.250 42.468 Td /F1 9.8 Tf [(macro-organizational processes and the informational complexity of)] TJ ET
0.965 0.965 0.965 rg
26.250 -31.163 555.000 63.750 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 32.587 m 581.250 32.587 l 581.250 31.837 l 26.250 31.837 l f
Q
q
15.000 -31.163 577.500 808.163 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(predictive failures. To do so, however, we need a different foundational model of both ‘organizations’ and of \(the process of\) )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(‘organization’. Moldoveanu and Bauer \(2004\) have proposed that a useful model for the organization from the point of view of )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(studying ‘organizational complexity’ is a generalized computational device, such as a Universal Turing Machine \(UTM\). )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(Organizational tasks are conceptualized as computational processes, with ‘software’ \(or, the algorithms that ‘organize’ )] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(organizations\) provided by the plans and strategies of managers or the models used by researchers to represent organizational )] TJ ET
BT 26.250 707.952 Td /F1 9.8 Tf [(phenomena, and the hardware provided by the physical embodiment of the organization. Tasks can be understood as the )] TJ ET
BT 26.250 696.048 Td /F1 9.8 Tf [(processes by which algorithms run on the underlying computational structure. They are states of the computational device that )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(models organizations.)] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(Now, by Wolfram’s argument, the states of this computational device can be modeled by the instantaneous states of a CA, and )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(thus, a CA can be used to model what organizations \()] TJ ET
BT 257.656 652.833 Td /F5 9.8 Tf [(tout court\) do)] TJ ET
BT 315.103 652.833 Td /F1 9.8 Tf [(—and, ask: under what conditions can we make valid )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(predictions about the long-run dynamics of organizational processes? Because there is a straightforward type-type reduction of )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(organizational tasks to computational processes and of computational processes to the evolution of CAs, we can ask: what )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(sorts of organizational tasks lend themselves to predictable CA instantiations, and, under what conditions can we expect to be )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(able to make competent \(accurate and reliable\) predictions? Such a question can take many different forms. One can ask, for )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(instance, a parallel question about the evolution of language \(what are the attributes of words and concepts that ‘cut the world at )] TJ ET
BT 26.250 581.405 Td /F1 9.8 Tf [(the joints?’ \(Nozick, 2002\)—and model the rules by which we assign concepts to representations as the rules used to program )] TJ ET
BT 26.250 569.500 Td /F1 9.8 Tf [(the evolution of a CA; or, about the evolution of ‘ways of knowing’ \(what are ‘good discovery rules’ that account for predictive )] TJ ET
BT 26.250 557.595 Td /F1 9.8 Tf [(success in a particular science?\); or, about the evolution of reasoning more generally \(‘what is it in the nature of the received )] TJ ET
BT 26.250 545.691 Td /F1 9.8 Tf [(‘laws of thought’ that makes logical inference, for instance, a valuable building block for the causal inferences that underscore )] TJ ET
BT 26.250 533.786 Td /F1 9.8 Tf [(predictive successes?\). These problem statements can then be interpreted in the language of CAs in order to come up with )] TJ ET
BT 26.250 521.881 Td /F1 9.8 Tf [(representable—albeit potentially uncomputable—CA states that model the evolution of organizational patterns of behavior.)] TJ ET
BT 26.250 485.279 Td /F4 12.0 Tf [(Cellular automata \(CA\), universality and computational equivalence)] TJ ET
BT 26.250 465.324 Td /F1 9.8 Tf [(The computational modeling of organizational processes relies on the success of a series of reductions: of rule-based )] TJ ET
BT 26.250 453.420 Td /F1 9.8 Tf [(organizational phenomena to rule-based computational processes running on universal computational machines \(such as )] TJ ET
BT 26.250 441.515 Td /F1 9.8 Tf [(Universal Turing Machines\), of universal computational devices to CAs, and, quite often, of complex rule sets to simple rule )] TJ ET
BT 26.250 429.610 Td /F1 9.8 Tf [(sets. These reductions, in turn, allow us to reduce the ‘complexity’ of an organizational phenomenon to the rule-complexity, run-)] TJ ET
BT 26.250 417.705 Td /F1 9.8 Tf [(time-complexity and relative compressibility of the cellular automaton patterns that simulate that phenomenon, ‘computation’ writ )] TJ ET
BT 26.250 405.801 Td /F1 9.8 Tf [(large to the evolution of a cellular automaton embodying a particular rule set, the analysis of complexity to a typology of rule )] TJ ET
BT 26.250 393.896 Td /F1 9.8 Tf [(sets and of the evolved CA patterns associated with them, of macro-organizational processes to large scale cellular automaton )] TJ ET
BT 26.250 381.991 Td /F1 9.8 Tf [(patterns and of micro-organizational processes to local rule sets. As with any reductive strategy, we can ask:)] TJ ET
BT 26.250 362.586 Td /F5 9.8 Tf [(Is the strategy general enough to capture phenomena of interest?)] TJ ET
BT 309.166 362.586 Td /F1 9.8 Tf [( The principle of computational representability \(of in-principle )] TJ ET
BT 26.250 350.682 Td /F1 9.8 Tf [(computable functions\) or allows us to represent any organizational process or phenomenon that can in principle be represented. )] TJ ET
BT 26.250 338.777 Td /F1 9.8 Tf [(It states that natural processes can be understood as embodiments of computational processes. It is reasonable to ask if any )] TJ ET
BT 26.250 326.872 Td /F1 9.8 Tf [(phenomenon can properly be understood as a computational process, and, of course, an answer is not easily forthcoming: like )] TJ ET
BT 26.250 314.967 Td /F1 9.8 Tf [(any modeling device, the principle of computational representability is not refutable, because it is a )] TJ ET
BT 453.281 314.967 Td /F5 9.8 Tf [(paradigm)] TJ ET
BT 493.918 314.967 Td /F1 9.8 Tf [( \(Kuhn, 1962; )] TJ ET
BT 26.250 303.063 Td /F1 9.8 Tf [(1990\). It is, however, possible to inquire about the limits of the applicability of the move in particular cases.)] TJ ET
BT 26.250 283.658 Td /F1 9.8 Tf [(A fundamental theorem of computation \(Boolos & Jeffrey, 1993\) states that, if a process is simulable at all, then it is simulable )] TJ ET
BT 26.250 271.753 Td /F1 9.8 Tf [(on a Universal Turing Machine. A representable phenomenon is one that can be modeled using functions mapping independent )] TJ ET
BT 26.250 259.848 Td /F1 9.8 Tf [(to dependent variables. These functions are computable: algorithms that compute them on a computational device converge to )] TJ ET
BT 26.250 247.944 Td /F1 9.8 Tf [(an answer after a \(potentially large but finite\) number of iterations—which can be thought of as point computations. Moreover, if )] TJ ET
BT 26.250 236.039 Td /F1 9.8 Tf [(a function is computable, then it is Turing-computable: it can be implemented on a Universal Turing Machine \(Boolos & Jeffrey, )] TJ ET
BT 26.250 224.134 Td /F1 9.8 Tf [(1993\). Wolfram \(2002\) shows how to build CA representations of Turing machines, and thus how to reduce general-purpose )] TJ ET
BT 26.250 212.229 Td /F1 9.8 Tf [(computational processes to CA processes. Thus, by using the basic strategy shown in Figure 1, we can use this approach to )] TJ ET
BT 26.250 200.325 Td /F1 9.8 Tf [(reduce any organizational phenomenon that can in principle be symbolically represented to a set of CA processes.)] TJ ET
BT 26.250 180.920 Td /F5 9.8 Tf [(Are the resulting findings objective, or, at least, inter-subjective)] TJ ET
BT 296.656 180.920 Td /F1 9.8 Tf [(, i.e., do they depend on a particular embodiment of the )] TJ ET
BT 26.250 169.015 Td /F1 9.8 Tf [(computational device used for simulation? The significant point about Universal Turing Machines is that they are the most )] TJ ET
BT 26.250 157.110 Td /F1 9.8 Tf [(general computational devices available. They can be used to simulate the operation of )] TJ ET
BT 405.067 157.110 Td /F5 9.8 Tf [(any other computational device)] TJ ET
BT 540.007 157.110 Td /F1 9.8 Tf [(—such )] TJ ET
BT 26.250 145.206 Td /F1 9.8 Tf [(as a cellular automaton. Thus, if a process is simulable or representable on a particular CPU architecture \(a Pentium, say\) using )] TJ ET
BT 26.250 133.301 Td /F1 9.8 Tf [(the local set of rules associated with programming it, then it can be simulated using the \(irreducibly simple\) instruction set of a )] TJ ET
BT 26.250 121.396 Td /F1 9.8 Tf [(Turing machine. Our strategy thus allows modelers using different computational devices and different simulation languages )] TJ ET
BT 26.250 109.491 Td /F1 9.8 Tf [(\(such as ‘theories of the organization’, or ‘modeling heuristics’ or ‘models’ simpliciter\) to reach agreement about important )] TJ ET
BT 26.250 97.587 Td /F1 9.8 Tf [(quantities \(such as ‘complexity’\) by translating their models into a more universal rule set and then comparing their findings.)] TJ ET
BT 26.250 78.182 Td /F5 9.8 Tf [(Are the resulting distinctions, models and findings useful and relevant to the study of organizations?)] TJ ET
BT 455.464 78.182 Td /F1 9.8 Tf [( We argue that using the )] TJ ET
BT 26.250 66.277 Td /F1 9.8 Tf [(basic computational strategy outlined in this paper will allow us to draw important new distinctions in the study of organizations, )] TJ ET
BT 26.250 54.372 Td /F1 9.8 Tf [(such as those between the local complexity of the rules of interactions between individual agents and the rules of evolution of )] TJ ET
BT 26.250 42.468 Td /F1 9.8 Tf [(macro-organizational processes and the informational complexity of)] TJ ET
0.965 0.965 0.965 rg
26.250 -31.163 555.000 63.750 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 32.587 m 581.250 32.587 l 581.250 31.837 l 26.250 31.837 l f
Q
q
15.000 -31.163 577.500 808.163 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(predictive failures. To do so, however, we need a different foundational model of both ‘organizations’ and of \(the process of\) )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(‘organization’. Moldoveanu and Bauer \(2004\) have proposed that a useful model for the organization from the point of view of )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(studying ‘organizational complexity’ is a generalized computational device, such as a Universal Turing Machine \(UTM\). )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(Organizational tasks are conceptualized as computational processes, with ‘software’ \(or, the algorithms that ‘organize’ )] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(organizations\) provided by the plans and strategies of managers or the models used by researchers to represent organizational )] TJ ET
BT 26.250 707.952 Td /F1 9.8 Tf [(phenomena, and the hardware provided by the physical embodiment of the organization. Tasks can be understood as the )] TJ ET
BT 26.250 696.048 Td /F1 9.8 Tf [(processes by which algorithms run on the underlying computational structure. They are states of the computational device that )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(models organizations.)] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(Now, by Wolfram’s argument, the states of this computational device can be modeled by the instantaneous states of a CA, and )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(thus, a CA can be used to model what organizations \()] TJ ET
BT 257.656 652.833 Td /F5 9.8 Tf [(tout court\) do)] TJ ET
BT 315.103 652.833 Td /F1 9.8 Tf [(—and, ask: under what conditions can we make valid )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(predictions about the long-run dynamics of organizational processes? Because there is a straightforward type-type reduction of )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(organizational tasks to computational processes and of computational processes to the evolution of CAs, we can ask: what )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(sorts of organizational tasks lend themselves to predictable CA instantiations, and, under what conditions can we expect to be )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(able to make competent \(accurate and reliable\) predictions? Such a question can take many different forms. One can ask, for )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(instance, a parallel question about the evolution of language \(what are the attributes of words and concepts that ‘cut the world at )] TJ ET
BT 26.250 581.405 Td /F1 9.8 Tf [(the joints?’ \(Nozick, 2002\)—and model the rules by which we assign concepts to representations as the rules used to program )] TJ ET
BT 26.250 569.500 Td /F1 9.8 Tf [(the evolution of a CA; or, about the evolution of ‘ways of knowing’ \(what are ‘good discovery rules’ that account for predictive )] TJ ET
BT 26.250 557.595 Td /F1 9.8 Tf [(success in a particular science?\); or, about the evolution of reasoning more generally \(‘what is it in the nature of the received )] TJ ET
BT 26.250 545.691 Td /F1 9.8 Tf [(‘laws of thought’ that makes logical inference, for instance, a valuable building block for the causal inferences that underscore )] TJ ET
BT 26.250 533.786 Td /F1 9.8 Tf [(predictive successes?\). These problem statements can then be interpreted in the language of CAs in order to come up with )] TJ ET
BT 26.250 521.881 Td /F1 9.8 Tf [(representable—albeit potentially uncomputable—CA states that model the evolution of organizational patterns of behavior.)] TJ ET
BT 26.250 485.279 Td /F4 12.0 Tf [(Cellular automata \(CA\), universality and computational equivalence)] TJ ET
BT 26.250 465.324 Td /F1 9.8 Tf [(The computational modeling of organizational processes relies on the success of a series of reductions: of rule-based )] TJ ET
BT 26.250 453.420 Td /F1 9.8 Tf [(organizational phenomena to rule-based computational processes running on universal computational machines \(such as )] TJ ET
BT 26.250 441.515 Td /F1 9.8 Tf [(Universal Turing Machines\), of universal computational devices to CAs, and, quite often, of complex rule sets to simple rule )] TJ ET
BT 26.250 429.610 Td /F1 9.8 Tf [(sets. These reductions, in turn, allow us to reduce the ‘complexity’ of an organizational phenomenon to the rule-complexity, run-)] TJ ET
BT 26.250 417.705 Td /F1 9.8 Tf [(time-complexity and relative compressibility of the cellular automaton patterns that simulate that phenomenon, ‘computation’ writ )] TJ ET
BT 26.250 405.801 Td /F1 9.8 Tf [(large to the evolution of a cellular automaton embodying a particular rule set, the analysis of complexity to a typology of rule )] TJ ET
BT 26.250 393.896 Td /F1 9.8 Tf [(sets and of the evolved CA patterns associated with them, of macro-organizational processes to large scale cellular automaton )] TJ ET
BT 26.250 381.991 Td /F1 9.8 Tf [(patterns and of micro-organizational processes to local rule sets. As with any reductive strategy, we can ask:)] TJ ET
BT 26.250 362.586 Td /F5 9.8 Tf [(Is the strategy general enough to capture phenomena of interest?)] TJ ET
BT 309.166 362.586 Td /F1 9.8 Tf [( The principle of computational representability \(of in-principle )] TJ ET
BT 26.250 350.682 Td /F1 9.8 Tf [(computable functions\) or allows us to represent any organizational process or phenomenon that can in principle be represented. )] TJ ET
BT 26.250 338.777 Td /F1 9.8 Tf [(It states that natural processes can be understood as embodiments of computational processes. It is reasonable to ask if any )] TJ ET
BT 26.250 326.872 Td /F1 9.8 Tf [(phenomenon can properly be understood as a computational process, and, of course, an answer is not easily forthcoming: like )] TJ ET
BT 26.250 314.967 Td /F1 9.8 Tf [(any modeling device, the principle of computational representability is not refutable, because it is a )] TJ ET
BT 453.281 314.967 Td /F5 9.8 Tf [(paradigm)] TJ ET
BT 493.918 314.967 Td /F1 9.8 Tf [( \(Kuhn, 1962; )] TJ ET
BT 26.250 303.063 Td /F1 9.8 Tf [(1990\). It is, however, possible to inquire about the limits of the applicability of the move in particular cases.)] TJ ET
BT 26.250 283.658 Td /F1 9.8 Tf [(A fundamental theorem of computation \(Boolos & Jeffrey, 1993\) states that, if a process is simulable at all, then it is simulable )] TJ ET
BT 26.250 271.753 Td /F1 9.8 Tf [(on a Universal Turing Machine. A representable phenomenon is one that can be modeled using functions mapping independent )] TJ ET
BT 26.250 259.848 Td /F1 9.8 Tf [(to dependent variables. These functions are computable: algorithms that compute them on a computational device converge to )] TJ ET
BT 26.250 247.944 Td /F1 9.8 Tf [(an answer after a \(potentially large but finite\) number of iterations—which can be thought of as point computations. Moreover, if )] TJ ET
BT 26.250 236.039 Td /F1 9.8 Tf [(a function is computable, then it is Turing-computable: it can be implemented on a Universal Turing Machine \(Boolos & Jeffrey, )] TJ ET
BT 26.250 224.134 Td /F1 9.8 Tf [(1993\). Wolfram \(2002\) shows how to build CA representations of Turing machines, and thus how to reduce general-purpose )] TJ ET
BT 26.250 212.229 Td /F1 9.8 Tf [(computational processes to CA processes. Thus, by using the basic strategy shown in Figure 1, we can use this approach to )] TJ ET
BT 26.250 200.325 Td /F1 9.8 Tf [(reduce any organizational phenomenon that can in principle be symbolically represented to a set of CA processes.)] TJ ET
BT 26.250 180.920 Td /F5 9.8 Tf [(Are the resulting findings objective, or, at least, inter-subjective)] TJ ET
BT 296.656 180.920 Td /F1 9.8 Tf [(, i.e., do they depend on a particular embodiment of the )] TJ ET
BT 26.250 169.015 Td /F1 9.8 Tf [(computational device used for simulation? The significant point about Universal Turing Machines is that they are the most )] TJ ET
BT 26.250 157.110 Td /F1 9.8 Tf [(general computational devices available. They can be used to simulate the operation of )] TJ ET
BT 405.067 157.110 Td /F5 9.8 Tf [(any other computational device)] TJ ET
BT 540.007 157.110 Td /F1 9.8 Tf [(—such )] TJ ET
BT 26.250 145.206 Td /F1 9.8 Tf [(as a cellular automaton. Thus, if a process is simulable or representable on a particular CPU architecture \(a Pentium, say\) using )] TJ ET
BT 26.250 133.301 Td /F1 9.8 Tf [(the local set of rules associated with programming it, then it can be simulated using the \(irreducibly simple\) instruction set of a )] TJ ET
BT 26.250 121.396 Td /F1 9.8 Tf [(Turing machine. Our strategy thus allows modelers using different computational devices and different simulation languages )] TJ ET
BT 26.250 109.491 Td /F1 9.8 Tf [(\(such as ‘theories of the organization’, or ‘modeling heuristics’ or ‘models’ simpliciter\) to reach agreement about important )] TJ ET
BT 26.250 97.587 Td /F1 9.8 Tf [(quantities \(such as ‘complexity’\) by translating their models into a more universal rule set and then comparing their findings.)] TJ ET
BT 26.250 78.182 Td /F5 9.8 Tf [(Are the resulting distinctions, models and findings useful and relevant to the study of organizations?)] TJ ET
BT 455.464 78.182 Td /F1 9.8 Tf [( We argue that using the )] TJ ET
BT 26.250 66.277 Td /F1 9.8 Tf [(basic computational strategy outlined in this paper will allow us to draw important new distinctions in the study of organizations, )] TJ ET
BT 26.250 54.372 Td /F1 9.8 Tf [(such as those between the local complexity of the rules of interactions between individual agents and the rules of evolution of )] TJ ET
BT 26.250 42.468 Td /F1 9.8 Tf [(macro-organizational processes and the informational complexity of)] TJ ET
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BT 291.710 19.825 Td /F1 11.0 Tf [(5)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
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BT 35.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 759.600 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/b9154614-9914-eaeb-ca1c-49f54d7ccd95.png)] TJ ET
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BT 35.250 713.476 Td /F4 9.8 Tf [(Fig. 1: Figure 1)] TJ ET
BT 35.250 694.106 Td /F5 9.8 Tf [(The process of representing organizational phenomena as computational phenomena)] TJ ET
Q
BT 26.250 663.085 Td /F1 9.8 Tf [(local rules and the computational complexity of the processes by which global patterns are produced from simple rules. It will )] TJ ET
BT 26.250 651.180 Td /F1 9.8 Tf [(allow us to produce models of organizational phenomena in which organizational complexity functions as both a bound and a )] TJ ET
BT 26.250 639.276 Td /F1 9.8 Tf [(driver or the evolution of organizational processes \(Moldoveanu & Bauer, 2004\). It will also allow us to generate explanations of )] TJ ET
BT 26.250 627.371 Td /F1 9.8 Tf [(novel phenomena such as the dynamics of rules in organizations \(March )] TJ ET
BT 341.633 627.371 Td /F5 9.8 Tf [(et al)] TJ ET
BT 360.061 627.371 Td /F1 9.8 Tf [(., 2000\) which allow us to look not only at the )] TJ ET
BT 26.250 615.466 Td /F1 9.8 Tf [(differential birth and death rates of rules in organizations, but also at the content of rule systems, so as to be able to ask new )] TJ ET
BT 26.250 603.561 Td /F1 9.8 Tf [(empirical questions of the data, such as:)] TJ ET
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BT 41.206 581.924 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 581.907 Td /F1 9.8 Tf [(What kinds of rules are likely to proliferate in different environments?)] TJ ET
BT 41.206 558.769 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 558.752 Td /F1 9.8 Tf [(What kinds of rules are survivable across a large set of different environmental and organizational settings?)] TJ ET
BT 41.206 535.615 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 535.597 Td /F1 9.8 Tf [(What causal mechanisms relate changes in organizational behavior patterns to organizational rule sets?)] TJ ET
BT 26.250 493.692 Td /F1 9.8 Tf [(We begin by attempting to establish simple rule-based cellular automata as the basic building blocks for models of )] TJ ET
BT 26.250 481.788 Td /F1 9.8 Tf [(organizational phenomena. We do this by showing how organizational phenomena can be understood in terms of individual )] TJ ET
BT 26.250 469.883 Td /F1 9.8 Tf [(agents interacting according to )] TJ ET
BT 161.736 469.883 Td /F5 9.8 Tf [(locally simple rule sets)] TJ ET
BT 259.255 469.883 Td /F1 9.8 Tf [(, how globally ‘complex’ or ‘unfathomable’ phenomena can be )] TJ ET
BT 26.250 457.978 Td /F1 9.8 Tf [(decomposed into parsimonious models that use simple, local rule sets, how the underlying local rule sets can be captured, )] TJ ET
BT 26.250 446.073 Td /F1 9.8 Tf [(generally and parsimoniously, by CA processes. The main import of the argument for ‘doing organization science’ is then )] TJ ET
BT 26.250 434.169 Td /F1 9.8 Tf [(outlined and amounts to a research strategy \(or explanatory strategy\) that can generate both deep explanations for currently )] TJ ET
BT 26.250 422.264 Td /F1 9.8 Tf [(acknowledged ‘stylized facts’ about organizations and auditable predictions of the evolution of complex organizational )] TJ ET
BT 26.250 410.359 Td /F1 9.8 Tf [(phenomena.)] TJ ET
BT 26.250 373.757 Td /F4 12.0 Tf [(Representing rule-bound systems in terms of cellular automata)] TJ ET
BT 26.250 353.802 Td /F1 9.8 Tf [(Cellular automata models are rule-based systems consisting of:)] TJ ET
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26.250 217.743 555.000 126.178 re f
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26.250 343.921 m 581.250 343.921 l 581.250 343.171 l 26.250 343.171 l f
26.250 217.743 m 581.250 217.743 l 581.250 218.493 l 26.250 218.493 l f
0.271 0.267 0.267 rg
BT 41.206 324.665 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 324.648 Td /F1 9.8 Tf [(A set of interacting, local elements \(‘cells’\), 1. each of which can take on one of N possible states or values \(‘colors’ in )] TJ ET
BT 54.750 312.743 Td /F1 9.8 Tf [(many simulations using cellular automata\);)] TJ ET
BT 41.206 289.606 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 289.588 Td /F1 9.8 Tf [(A set of rules of interaction rules that pre2. scribe the state of each individual element as either a deterministic function )] TJ ET
BT 54.750 277.683 Td /F1 9.8 Tf [(of the color combinations of the neighboring elements, or a deterministic function of some statistical ensemble of the )] TJ ET
BT 54.750 265.779 Td /F1 9.8 Tf [(states of the neighboring elements, and;)] TJ ET
BT 41.206 242.641 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 242.624 Td /F1 9.8 Tf [(A set of initial and boundary conditions for 3. the states or values of the interacting elements of the CA.)] TJ ET
BT 26.250 200.719 Td /F1 9.8 Tf [(A CA model starts out by specifying the )] TJ ET
BT 199.118 200.719 Td /F5 9.8 Tf [(rules)] TJ ET
BT 220.246 200.719 Td /F1 9.8 Tf [( governing the local evolution of cells, and a set of initial conditions and boundary )] TJ ET
BT 26.250 188.814 Td /F1 9.8 Tf [(conditions for the two-dimensional space in which the cells are laid out. The basic strategy of the CA modeler is to start out with )] TJ ET
BT 26.250 176.910 Td /F1 9.8 Tf [(a macroscopic discernible pattern, to postulate a dynamical process in which the global pattern is generated through the )] TJ ET
BT 26.250 165.005 Td /F1 9.8 Tf [(interaction of individual elements using simple interaction rules and limited state-alphabets \(for instance, the numbers of )] TJ ET
BT 26.250 153.100 Td /F1 9.8 Tf [(possible colors a particular cell can take on\), to postulate a set of initial and boundary conditions \(number of interacting cells of )] TJ ET
BT 26.250 141.195 Td /F1 9.8 Tf [(the CA, initial states of these elements\) and to attempt to reproduce some salient aspect of the macroscopic pattern from )] TJ ET
BT 26.250 129.291 Td /F1 9.8 Tf [(knowledge of the micro-level rules, the initial conditions, and the applicable boundary conditions. ’Better’ models are locally )] TJ ET
BT 26.250 117.386 Td /F1 9.8 Tf [(simple and explain more globally ‘complex’ stylized facts: they explain ‘more with less’ and thus run parallel to the ‘stream of )] TJ ET
BT 26.250 105.481 Td /F1 9.8 Tf [(progress’—at least in some accepted views of scientific theorizing \(Friedman, 1953\), which attempt to make precise the norm, )] TJ ET
BT 26.250 93.576 Td /F1 9.8 Tf [(‘attempt to explain a lot by a little’—commonly known as ‘Ockham’s razor’.)] TJ ET
BT 26.250 56.974 Td /F4 12.0 Tf [(How have CA models been used in organization studies, and how might they be used?)] TJ ET
BT 26.250 37.020 Td /F1 9.8 Tf [(There already exists a rich literature that applies CA models to the explanation and prediction of organizational phenomena )] TJ ET
Q
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BT 35.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 759.600 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/b9154614-9914-eaeb-ca1c-49f54d7ccd95.png)] TJ ET
q
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BT 35.250 713.476 Td /F4 9.8 Tf [(Fig. 1: Figure 1)] TJ ET
BT 35.250 694.106 Td /F5 9.8 Tf [(The process of representing organizational phenomena as computational phenomena)] TJ ET
Q
BT 26.250 663.085 Td /F1 9.8 Tf [(local rules and the computational complexity of the processes by which global patterns are produced from simple rules. It will )] TJ ET
BT 26.250 651.180 Td /F1 9.8 Tf [(allow us to produce models of organizational phenomena in which organizational complexity functions as both a bound and a )] TJ ET
BT 26.250 639.276 Td /F1 9.8 Tf [(driver or the evolution of organizational processes \(Moldoveanu & Bauer, 2004\). It will also allow us to generate explanations of )] TJ ET
BT 26.250 627.371 Td /F1 9.8 Tf [(novel phenomena such as the dynamics of rules in organizations \(March )] TJ ET
BT 341.633 627.371 Td /F5 9.8 Tf [(et al)] TJ ET
BT 360.061 627.371 Td /F1 9.8 Tf [(., 2000\) which allow us to look not only at the )] TJ ET
BT 26.250 615.466 Td /F1 9.8 Tf [(differential birth and death rates of rules in organizations, but also at the content of rule systems, so as to be able to ask new )] TJ ET
BT 26.250 603.561 Td /F1 9.8 Tf [(empirical questions of the data, such as:)] TJ ET
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0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 601.180 m 581.250 601.180 l 581.250 600.430 l 26.250 600.430 l f
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0.271 0.267 0.267 rg
BT 41.206 581.924 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 581.907 Td /F1 9.8 Tf [(What kinds of rules are likely to proliferate in different environments?)] TJ ET
BT 41.206 558.769 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 558.752 Td /F1 9.8 Tf [(What kinds of rules are survivable across a large set of different environmental and organizational settings?)] TJ ET
BT 41.206 535.615 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 535.597 Td /F1 9.8 Tf [(What causal mechanisms relate changes in organizational behavior patterns to organizational rule sets?)] TJ ET
BT 26.250 493.692 Td /F1 9.8 Tf [(We begin by attempting to establish simple rule-based cellular automata as the basic building blocks for models of )] TJ ET
BT 26.250 481.788 Td /F1 9.8 Tf [(organizational phenomena. We do this by showing how organizational phenomena can be understood in terms of individual )] TJ ET
BT 26.250 469.883 Td /F1 9.8 Tf [(agents interacting according to )] TJ ET
BT 161.736 469.883 Td /F5 9.8 Tf [(locally simple rule sets)] TJ ET
BT 259.255 469.883 Td /F1 9.8 Tf [(, how globally ‘complex’ or ‘unfathomable’ phenomena can be )] TJ ET
BT 26.250 457.978 Td /F1 9.8 Tf [(decomposed into parsimonious models that use simple, local rule sets, how the underlying local rule sets can be captured, )] TJ ET
BT 26.250 446.073 Td /F1 9.8 Tf [(generally and parsimoniously, by CA processes. The main import of the argument for ‘doing organization science’ is then )] TJ ET
BT 26.250 434.169 Td /F1 9.8 Tf [(outlined and amounts to a research strategy \(or explanatory strategy\) that can generate both deep explanations for currently )] TJ ET
BT 26.250 422.264 Td /F1 9.8 Tf [(acknowledged ‘stylized facts’ about organizations and auditable predictions of the evolution of complex organizational )] TJ ET
BT 26.250 410.359 Td /F1 9.8 Tf [(phenomena.)] TJ ET
BT 26.250 373.757 Td /F4 12.0 Tf [(Representing rule-bound systems in terms of cellular automata)] TJ ET
BT 26.250 353.802 Td /F1 9.8 Tf [(Cellular automata models are rule-based systems consisting of:)] TJ ET
0.965 0.965 0.965 rg
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0.267 0.267 0.267 rg
26.250 343.921 m 581.250 343.921 l 581.250 343.171 l 26.250 343.171 l f
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0.271 0.267 0.267 rg
BT 41.206 324.665 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 324.648 Td /F1 9.8 Tf [(A set of interacting, local elements \(‘cells’\), 1. each of which can take on one of N possible states or values \(‘colors’ in )] TJ ET
BT 54.750 312.743 Td /F1 9.8 Tf [(many simulations using cellular automata\);)] TJ ET
BT 41.206 289.606 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 289.588 Td /F1 9.8 Tf [(A set of rules of interaction rules that pre2. scribe the state of each individual element as either a deterministic function )] TJ ET
BT 54.750 277.683 Td /F1 9.8 Tf [(of the color combinations of the neighboring elements, or a deterministic function of some statistical ensemble of the )] TJ ET
BT 54.750 265.779 Td /F1 9.8 Tf [(states of the neighboring elements, and;)] TJ ET
BT 41.206 242.641 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 242.624 Td /F1 9.8 Tf [(A set of initial and boundary conditions for 3. the states or values of the interacting elements of the CA.)] TJ ET
BT 26.250 200.719 Td /F1 9.8 Tf [(A CA model starts out by specifying the )] TJ ET
BT 199.118 200.719 Td /F5 9.8 Tf [(rules)] TJ ET
BT 220.246 200.719 Td /F1 9.8 Tf [( governing the local evolution of cells, and a set of initial conditions and boundary )] TJ ET
BT 26.250 188.814 Td /F1 9.8 Tf [(conditions for the two-dimensional space in which the cells are laid out. The basic strategy of the CA modeler is to start out with )] TJ ET
BT 26.250 176.910 Td /F1 9.8 Tf [(a macroscopic discernible pattern, to postulate a dynamical process in which the global pattern is generated through the )] TJ ET
BT 26.250 165.005 Td /F1 9.8 Tf [(interaction of individual elements using simple interaction rules and limited state-alphabets \(for instance, the numbers of )] TJ ET
BT 26.250 153.100 Td /F1 9.8 Tf [(possible colors a particular cell can take on\), to postulate a set of initial and boundary conditions \(number of interacting cells of )] TJ ET
BT 26.250 141.195 Td /F1 9.8 Tf [(the CA, initial states of these elements\) and to attempt to reproduce some salient aspect of the macroscopic pattern from )] TJ ET
BT 26.250 129.291 Td /F1 9.8 Tf [(knowledge of the micro-level rules, the initial conditions, and the applicable boundary conditions. ’Better’ models are locally )] TJ ET
BT 26.250 117.386 Td /F1 9.8 Tf [(simple and explain more globally ‘complex’ stylized facts: they explain ‘more with less’ and thus run parallel to the ‘stream of )] TJ ET
BT 26.250 105.481 Td /F1 9.8 Tf [(progress’—at least in some accepted views of scientific theorizing \(Friedman, 1953\), which attempt to make precise the norm, )] TJ ET
BT 26.250 93.576 Td /F1 9.8 Tf [(‘attempt to explain a lot by a little’—commonly known as ‘Ockham’s razor’.)] TJ ET
BT 26.250 56.974 Td /F4 12.0 Tf [(How have CA models been used in organization studies, and how might they be used?)] TJ ET
BT 26.250 37.020 Td /F1 9.8 Tf [(There already exists a rich literature that applies CA models to the explanation and prediction of organizational phenomena )] TJ ET
Q
q
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0.965 0.965 0.965 rg
26.250 680.109 555.000 96.891 re f
0.267 0.267 0.267 rg
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0.500 0.500 0.500 rg
BT 35.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 759.600 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/b9154614-9914-eaeb-ca1c-49f54d7ccd95.png)] TJ ET
q
35.250 683.859 537.000 39.141 re W n
0.271 0.267 0.267 rg
BT 35.250 713.476 Td /F4 9.8 Tf [(Fig. 1: Figure 1)] TJ ET
BT 35.250 694.106 Td /F5 9.8 Tf [(The process of representing organizational phenomena as computational phenomena)] TJ ET
Q
BT 26.250 663.085 Td /F1 9.8 Tf [(local rules and the computational complexity of the processes by which global patterns are produced from simple rules. It will )] TJ ET
BT 26.250 651.180 Td /F1 9.8 Tf [(allow us to produce models of organizational phenomena in which organizational complexity functions as both a bound and a )] TJ ET
BT 26.250 639.276 Td /F1 9.8 Tf [(driver or the evolution of organizational processes \(Moldoveanu & Bauer, 2004\). It will also allow us to generate explanations of )] TJ ET
BT 26.250 627.371 Td /F1 9.8 Tf [(novel phenomena such as the dynamics of rules in organizations \(March )] TJ ET
BT 341.633 627.371 Td /F5 9.8 Tf [(et al)] TJ ET
BT 360.061 627.371 Td /F1 9.8 Tf [(., 2000\) which allow us to look not only at the )] TJ ET
BT 26.250 615.466 Td /F1 9.8 Tf [(differential birth and death rates of rules in organizations, but also at the content of rule systems, so as to be able to ask new )] TJ ET
BT 26.250 603.561 Td /F1 9.8 Tf [(empirical questions of the data, such as:)] TJ ET
0.965 0.965 0.965 rg
26.250 510.716 555.000 90.464 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 601.180 m 581.250 601.180 l 581.250 600.430 l 26.250 600.430 l f
26.250 510.716 m 581.250 510.716 l 581.250 511.466 l 26.250 511.466 l f
0.271 0.267 0.267 rg
BT 41.206 581.924 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 581.907 Td /F1 9.8 Tf [(What kinds of rules are likely to proliferate in different environments?)] TJ ET
BT 41.206 558.769 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 558.752 Td /F1 9.8 Tf [(What kinds of rules are survivable across a large set of different environmental and organizational settings?)] TJ ET
BT 41.206 535.615 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 535.597 Td /F1 9.8 Tf [(What causal mechanisms relate changes in organizational behavior patterns to organizational rule sets?)] TJ ET
BT 26.250 493.692 Td /F1 9.8 Tf [(We begin by attempting to establish simple rule-based cellular automata as the basic building blocks for models of )] TJ ET
BT 26.250 481.788 Td /F1 9.8 Tf [(organizational phenomena. We do this by showing how organizational phenomena can be understood in terms of individual )] TJ ET
BT 26.250 469.883 Td /F1 9.8 Tf [(agents interacting according to )] TJ ET
BT 161.736 469.883 Td /F5 9.8 Tf [(locally simple rule sets)] TJ ET
BT 259.255 469.883 Td /F1 9.8 Tf [(, how globally ‘complex’ or ‘unfathomable’ phenomena can be )] TJ ET
BT 26.250 457.978 Td /F1 9.8 Tf [(decomposed into parsimonious models that use simple, local rule sets, how the underlying local rule sets can be captured, )] TJ ET
BT 26.250 446.073 Td /F1 9.8 Tf [(generally and parsimoniously, by CA processes. The main import of the argument for ‘doing organization science’ is then )] TJ ET
BT 26.250 434.169 Td /F1 9.8 Tf [(outlined and amounts to a research strategy \(or explanatory strategy\) that can generate both deep explanations for currently )] TJ ET
BT 26.250 422.264 Td /F1 9.8 Tf [(acknowledged ‘stylized facts’ about organizations and auditable predictions of the evolution of complex organizational )] TJ ET
BT 26.250 410.359 Td /F1 9.8 Tf [(phenomena.)] TJ ET
BT 26.250 373.757 Td /F4 12.0 Tf [(Representing rule-bound systems in terms of cellular automata)] TJ ET
BT 26.250 353.802 Td /F1 9.8 Tf [(Cellular automata models are rule-based systems consisting of:)] TJ ET
0.965 0.965 0.965 rg
26.250 217.743 555.000 126.178 re f
0.267 0.267 0.267 rg
26.250 343.921 m 581.250 343.921 l 581.250 343.171 l 26.250 343.171 l f
26.250 217.743 m 581.250 217.743 l 581.250 218.493 l 26.250 218.493 l f
0.271 0.267 0.267 rg
BT 41.206 324.665 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 324.648 Td /F1 9.8 Tf [(A set of interacting, local elements \(‘cells’\), 1. each of which can take on one of N possible states or values \(‘colors’ in )] TJ ET
BT 54.750 312.743 Td /F1 9.8 Tf [(many simulations using cellular automata\);)] TJ ET
BT 41.206 289.606 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 289.588 Td /F1 9.8 Tf [(A set of rules of interaction rules that pre2. scribe the state of each individual element as either a deterministic function )] TJ ET
BT 54.750 277.683 Td /F1 9.8 Tf [(of the color combinations of the neighboring elements, or a deterministic function of some statistical ensemble of the )] TJ ET
BT 54.750 265.779 Td /F1 9.8 Tf [(states of the neighboring elements, and;)] TJ ET
BT 41.206 242.641 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 242.624 Td /F1 9.8 Tf [(A set of initial and boundary conditions for 3. the states or values of the interacting elements of the CA.)] TJ ET
BT 26.250 200.719 Td /F1 9.8 Tf [(A CA model starts out by specifying the )] TJ ET
BT 199.118 200.719 Td /F5 9.8 Tf [(rules)] TJ ET
BT 220.246 200.719 Td /F1 9.8 Tf [( governing the local evolution of cells, and a set of initial conditions and boundary )] TJ ET
BT 26.250 188.814 Td /F1 9.8 Tf [(conditions for the two-dimensional space in which the cells are laid out. The basic strategy of the CA modeler is to start out with )] TJ ET
BT 26.250 176.910 Td /F1 9.8 Tf [(a macroscopic discernible pattern, to postulate a dynamical process in which the global pattern is generated through the )] TJ ET
BT 26.250 165.005 Td /F1 9.8 Tf [(interaction of individual elements using simple interaction rules and limited state-alphabets \(for instance, the numbers of )] TJ ET
BT 26.250 153.100 Td /F1 9.8 Tf [(possible colors a particular cell can take on\), to postulate a set of initial and boundary conditions \(number of interacting cells of )] TJ ET
BT 26.250 141.195 Td /F1 9.8 Tf [(the CA, initial states of these elements\) and to attempt to reproduce some salient aspect of the macroscopic pattern from )] TJ ET
BT 26.250 129.291 Td /F1 9.8 Tf [(knowledge of the micro-level rules, the initial conditions, and the applicable boundary conditions. ’Better’ models are locally )] TJ ET
BT 26.250 117.386 Td /F1 9.8 Tf [(simple and explain more globally ‘complex’ stylized facts: they explain ‘more with less’ and thus run parallel to the ‘stream of )] TJ ET
BT 26.250 105.481 Td /F1 9.8 Tf [(progress’—at least in some accepted views of scientific theorizing \(Friedman, 1953\), which attempt to make precise the norm, )] TJ ET
BT 26.250 93.576 Td /F1 9.8 Tf [(‘attempt to explain a lot by a little’—commonly known as ‘Ockham’s razor’.)] TJ ET
BT 26.250 56.974 Td /F4 12.0 Tf [(How have CA models been used in organization studies, and how might they be used?)] TJ ET
BT 26.250 37.020 Td /F1 9.8 Tf [(There already exists a rich literature that applies CA models to the explanation and prediction of organizational phenomena )] TJ ET
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BT 35.250 769.600 Td /F1 8.0 Tf [(Image not readable or empty)] TJ ET
BT 35.250 759.600 Td /F1 8.0 Tf [(https://journal.emergentpublications.com/wp-content/uploads/2015/11/b9154614-9914-eaeb-ca1c-49f54d7ccd95.png)] TJ ET
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BT 291.710 19.825 Td /F1 11.0 Tf [(6)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
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BT 26.250 767.476 Td /F1 9.8 Tf [(\(see, for instance, Lomi & Larsen, 2001\). Researchers have produced sophisticated models of organizational behavior starting )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(from postulates of bounded rationality and severe cognitive limitations at the level of individual agents making up the )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(organization \(modeled by a restricted set of local decision rules or heuristics\), which allow them to model these agents using )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(restricted state alphabets and simple, local, rule sets. By ‘simple’ one usually means \(often without stating\), admitting of low-)] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(Kolmogorov-complexity representations \(Li & Vitanyi, 1993\). Individual memory limitations—‘informational limitations’—are )] TJ ET
BT 26.250 707.952 Td /F1 9.8 Tf [(handled by the ‘small alphabet’ restriction and the restriction on the informational complexity of the rules of interaction, and )] TJ ET
BT 26.250 696.048 Td /F1 9.8 Tf [(individual computational limitations are handled by restricting the ‘search for rules’ to a space of computationally tractable ones. )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(Individual agents, for instance, may be assumed to not be able—or, willing—to solve NP-hard problems \(Moldoveanu & Bauer, )] TJ ET
BT 26.250 672.238 Td /F1 9.8 Tf [(2004\)—indeed, they are assumed to )] TJ ET
BT 186.667 672.238 Td /F5 9.8 Tf [(conceptualize)] TJ ET
BT 246.278 672.238 Td /F1 9.8 Tf [( their predicaments in terms of )] TJ ET
BT 380.116 672.238 Td /F5 9.8 Tf [(P-hard)] TJ ET
BT 409.376 672.238 Td /F1 9.8 Tf [( rather than )] TJ ET
BT 461.948 672.238 Td /F5 9.8 Tf [(NP-hard)] TJ ET
BT 498.247 672.238 Td /F1 9.8 Tf [( problems )] TJ ET
BT 26.250 660.333 Td /F1 9.8 Tf [(\(Moldoveanu, 2006\).)] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(But, individual cells are not always )] TJ ET
BT 177.434 640.929 Td /F5 9.8 Tf [(individual agents)] TJ ET
BT 250.052 640.929 Td /F1 9.8 Tf [(—i.e., stylized models of humans—in CA modeling. For instance, McKelvey )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(\(1999\), and Rivkin \(2000\) use CA models to represent coupled organizational activity sets. Organizational states are encoded )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(using binary-state \(‘logic gate’\) N-tuples of elements that are, on average, mutually N-wise coupled, and explore the dynamics )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(of such ‘nets’ by allowing them to evolve and measuring certain characteristics of their long-run dynamics \(such as their period )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(in the case of periodic dynamics\).)] TJ ET
BT 26.250 573.905 Td /F1 9.8 Tf [(CAs may also be used to ‘look inside’ the thinking of individual agents, whose local thinking and reasoning patterns are )] TJ ET
BT 26.250 562.000 Td /F1 9.8 Tf [(sometimes modeled using CAs. Rubinstein \(1996\) models the limited cognitive capacities of boundedly rational players )] TJ ET
BT 26.250 550.095 Td /F1 9.8 Tf [(engaged in iterated dominance reasoning to solve game-theoretic decision problems. The local rules of the CA device now act )] TJ ET
BT 26.250 538.191 Td /F1 9.8 Tf [(as propagators from a set of initial conditions \(the ‘problem formulation’\) to a ‘solution’ set that embodies both accuracy goals )] TJ ET
BT 26.250 526.286 Td /F1 9.8 Tf [(and time constraints. CAs can be set up to evaluate arbitrary logical expressions according to certain formulas and thus can act )] TJ ET
BT 26.250 514.381 Td /F1 9.8 Tf [(as propagators in the process of resolving a ‘well-structured problem’ \(Simon, 1973\); i.e., of proceeding, by self-evident steps \()] TJ ET
BT 26.250 502.476 Td /F5 9.8 Tf [(modus ponens and modus tollens)] TJ ET
BT 172.032 502.476 Td /F1 9.8 Tf [(\) from a set of initial conditions, to an ‘answer’.)] TJ ET
BT 26.250 483.072 Td /F1 9.8 Tf [(In view of these parallel developments, we have reason to ask: is there a ‘best, or, better way’ to proceed with the development )] TJ ET
BT 26.250 471.167 Td /F1 9.8 Tf [(of CA models in organizational research, such as:)] TJ ET
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BT 41.206 442.030 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 442.012 Td /F5 9.8 Tf [(Downward)] TJ ET
BT 100.799 442.012 Td /F1 9.8 Tf [( into a further decomposition of models of individual agents themselves as evolving complex entities?)] TJ ET
BT 41.206 418.875 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 418.857 Td /F5 9.8 Tf [(Upward)] TJ ET
BT 88.339 418.857 Td /F1 9.8 Tf [( into models of larger organizational sub-units as interacting elements?)] TJ ET
BT 41.206 395.720 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 395.703 Td /F5 9.8 Tf [(Inward)] TJ ET
BT 84.010 395.703 Td /F1 9.8 Tf [( towards more sophisticated models of individual cognition and reasoning?)] TJ ET
BT 41.206 372.565 Td /F1 9.8 Tf [(4.)] TJ ET
BT 54.750 372.548 Td /F5 9.8 Tf [(Sideways)] TJ ET
BT 96.470 372.548 Td /F1 9.8 Tf [( into using CA elements to represent basic entities—such as ‘activities’, or, elementary behaviors—that can )] TJ ET
BT 54.750 360.643 Td /F1 9.8 Tf [(plausibly be conceived as being linked by deterministic interaction rules?)] TJ ET
BT 26.250 318.738 Td /F1 9.8 Tf [(It is clear that CA models are sufficiently general to be used as representational devices for many different types of ‘ontologies’. )] TJ ET
BT 26.250 306.834 Td /F1 9.8 Tf [(Wolfram \(2002\) realizes this, and suggests that CA models are far more general than any one of these modeling applications. )] TJ ET
BT 26.250 294.929 Td /F1 9.8 Tf [(He constructs CA models for general computational processes and argues that any natural process —once understood as a )] TJ ET
BT 26.250 283.024 Td /F1 9.8 Tf [(computational process—can be simulated as a CA. \(He focuses, to be sure, on deterministic, rather than quantum computation; )] TJ ET
BT 26.250 271.119 Td /F1 9.8 Tf [(and, the extension of his arguments to quantum computation is not straightforward, but, the basic computational framework for )] TJ ET
BT 26.250 259.215 Td /F1 9.8 Tf [(studying organizational processes )] TJ ET
BT 175.815 259.215 Td /F5 9.8 Tf [(can)] TJ ET
BT 191.532 259.215 Td /F1 9.8 Tf [( be extended to include quantum computers as the basic modeling devices\).)] TJ ET
BT 26.250 239.810 Td /F1 9.8 Tf [(Thus, it is not necessary for the organizational modeler to impose unrealistic modeling conditions on the evolving entities \(as is )] TJ ET
BT 26.250 227.905 Td /F1 9.8 Tf [(often done by economic analyses of financial market behavior\) simply in order to ‘fit’ the studied phenomenon into a CA lattice )] TJ ET
BT 26.250 216.000 Td /F1 9.8 Tf [(model. Rather, CAs can provide general enough model of any organizational process that is representable in algorithmic form )] TJ ET
BT 26.250 204.096 Td /F1 9.8 Tf [(\(such as the form of a deterministic relationship between input and output, or independent and dependent variables\). The )] TJ ET
BT 26.250 192.191 Td /F1 9.8 Tf [(modeler’s problem is transformed, from the problem of how to get organizational phenomena that are already agreed-upon by )] TJ ET
BT 26.250 180.286 Td /F1 9.8 Tf [(researchers as ontologically ‘valid’ to fit into the straitjacket of ‘cells’ and ‘evolution rules’ to choosing from a multitude of local )] TJ ET
BT 26.250 168.381 Td /F1 9.8 Tf [(rules and interacting entities that together can produce particular patterns.)] TJ ET
BT 26.250 131.779 Td /F4 12.0 Tf [(Reduction of ‘complex’ rules, tasks and behaviors to ‘simple’ rule sets)] TJ ET
BT 26.250 111.825 Td /F1 9.8 Tf [(It is possible for either the modeler or the organizational designer, to trade off between the size of the ‘alphabet’ that defines the )] TJ ET
BT 26.250 99.920 Td /F1 9.8 Tf [(number of possible states that a cell can take on \(this defines the local, informational complexity of each interacting element\) )] TJ ET
BT 26.250 88.015 Td /F1 9.8 Tf [(and the complexity of the local interaction rules \(these define the computational sophistication of each interacting element\).)] TJ ET
BT 26.250 68.610 Td /F1 9.8 Tf [(Thus, it is possible, as a modeling \(or design\) assumption, to make trade-offs between the computational complexity of local )] TJ ET
BT 26.250 56.706 Td /F1 9.8 Tf [(rules and the informational complexity of local elements or cells. When the individual cells model individual agents within the )] TJ ET
BT 26.250 44.801 Td /F1 9.8 Tf [(organization, this exchangeability between computational and informational complexity becomes a critical capability for the )] TJ ET
Q
q
15.000 30.515 577.500 746.485 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(\(see, for instance, Lomi & Larsen, 2001\). Researchers have produced sophisticated models of organizational behavior starting )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(from postulates of bounded rationality and severe cognitive limitations at the level of individual agents making up the )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(organization \(modeled by a restricted set of local decision rules or heuristics\), which allow them to model these agents using )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(restricted state alphabets and simple, local, rule sets. By ‘simple’ one usually means \(often without stating\), admitting of low-)] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(Kolmogorov-complexity representations \(Li & Vitanyi, 1993\). Individual memory limitations—‘informational limitations’—are )] TJ ET
BT 26.250 707.952 Td /F1 9.8 Tf [(handled by the ‘small alphabet’ restriction and the restriction on the informational complexity of the rules of interaction, and )] TJ ET
BT 26.250 696.048 Td /F1 9.8 Tf [(individual computational limitations are handled by restricting the ‘search for rules’ to a space of computationally tractable ones. )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(Individual agents, for instance, may be assumed to not be able—or, willing—to solve NP-hard problems \(Moldoveanu & Bauer, )] TJ ET
BT 26.250 672.238 Td /F1 9.8 Tf [(2004\)—indeed, they are assumed to )] TJ ET
BT 186.667 672.238 Td /F5 9.8 Tf [(conceptualize)] TJ ET
BT 246.278 672.238 Td /F1 9.8 Tf [( their predicaments in terms of )] TJ ET
BT 380.116 672.238 Td /F5 9.8 Tf [(P-hard)] TJ ET
BT 409.376 672.238 Td /F1 9.8 Tf [( rather than )] TJ ET
BT 461.948 672.238 Td /F5 9.8 Tf [(NP-hard)] TJ ET
BT 498.247 672.238 Td /F1 9.8 Tf [( problems )] TJ ET
BT 26.250 660.333 Td /F1 9.8 Tf [(\(Moldoveanu, 2006\).)] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(But, individual cells are not always )] TJ ET
BT 177.434 640.929 Td /F5 9.8 Tf [(individual agents)] TJ ET
BT 250.052 640.929 Td /F1 9.8 Tf [(—i.e., stylized models of humans—in CA modeling. For instance, McKelvey )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(\(1999\), and Rivkin \(2000\) use CA models to represent coupled organizational activity sets. Organizational states are encoded )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(using binary-state \(‘logic gate’\) N-tuples of elements that are, on average, mutually N-wise coupled, and explore the dynamics )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(of such ‘nets’ by allowing them to evolve and measuring certain characteristics of their long-run dynamics \(such as their period )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(in the case of periodic dynamics\).)] TJ ET
BT 26.250 573.905 Td /F1 9.8 Tf [(CAs may also be used to ‘look inside’ the thinking of individual agents, whose local thinking and reasoning patterns are )] TJ ET
BT 26.250 562.000 Td /F1 9.8 Tf [(sometimes modeled using CAs. Rubinstein \(1996\) models the limited cognitive capacities of boundedly rational players )] TJ ET
BT 26.250 550.095 Td /F1 9.8 Tf [(engaged in iterated dominance reasoning to solve game-theoretic decision problems. The local rules of the CA device now act )] TJ ET
BT 26.250 538.191 Td /F1 9.8 Tf [(as propagators from a set of initial conditions \(the ‘problem formulation’\) to a ‘solution’ set that embodies both accuracy goals )] TJ ET
BT 26.250 526.286 Td /F1 9.8 Tf [(and time constraints. CAs can be set up to evaluate arbitrary logical expressions according to certain formulas and thus can act )] TJ ET
BT 26.250 514.381 Td /F1 9.8 Tf [(as propagators in the process of resolving a ‘well-structured problem’ \(Simon, 1973\); i.e., of proceeding, by self-evident steps \()] TJ ET
BT 26.250 502.476 Td /F5 9.8 Tf [(modus ponens and modus tollens)] TJ ET
BT 172.032 502.476 Td /F1 9.8 Tf [(\) from a set of initial conditions, to an ‘answer’.)] TJ ET
BT 26.250 483.072 Td /F1 9.8 Tf [(In view of these parallel developments, we have reason to ask: is there a ‘best, or, better way’ to proceed with the development )] TJ ET
BT 26.250 471.167 Td /F1 9.8 Tf [(of CA models in organizational research, such as:)] TJ ET
0.965 0.965 0.965 rg
26.250 335.762 555.000 125.524 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 461.286 m 581.250 461.286 l 581.250 460.536 l 26.250 460.536 l f
26.250 335.762 m 581.250 335.762 l 581.250 336.512 l 26.250 336.512 l f
0.271 0.267 0.267 rg
BT 41.206 442.030 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 442.012 Td /F5 9.8 Tf [(Downward)] TJ ET
BT 100.799 442.012 Td /F1 9.8 Tf [( into a further decomposition of models of individual agents themselves as evolving complex entities?)] TJ ET
BT 41.206 418.875 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 418.857 Td /F5 9.8 Tf [(Upward)] TJ ET
BT 88.339 418.857 Td /F1 9.8 Tf [( into models of larger organizational sub-units as interacting elements?)] TJ ET
BT 41.206 395.720 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 395.703 Td /F5 9.8 Tf [(Inward)] TJ ET
BT 84.010 395.703 Td /F1 9.8 Tf [( towards more sophisticated models of individual cognition and reasoning?)] TJ ET
BT 41.206 372.565 Td /F1 9.8 Tf [(4.)] TJ ET
BT 54.750 372.548 Td /F5 9.8 Tf [(Sideways)] TJ ET
BT 96.470 372.548 Td /F1 9.8 Tf [( into using CA elements to represent basic entities—such as ‘activities’, or, elementary behaviors—that can )] TJ ET
BT 54.750 360.643 Td /F1 9.8 Tf [(plausibly be conceived as being linked by deterministic interaction rules?)] TJ ET
BT 26.250 318.738 Td /F1 9.8 Tf [(It is clear that CA models are sufficiently general to be used as representational devices for many different types of ‘ontologies’. )] TJ ET
BT 26.250 306.834 Td /F1 9.8 Tf [(Wolfram \(2002\) realizes this, and suggests that CA models are far more general than any one of these modeling applications. )] TJ ET
BT 26.250 294.929 Td /F1 9.8 Tf [(He constructs CA models for general computational processes and argues that any natural process —once understood as a )] TJ ET
BT 26.250 283.024 Td /F1 9.8 Tf [(computational process—can be simulated as a CA. \(He focuses, to be sure, on deterministic, rather than quantum computation; )] TJ ET
BT 26.250 271.119 Td /F1 9.8 Tf [(and, the extension of his arguments to quantum computation is not straightforward, but, the basic computational framework for )] TJ ET
BT 26.250 259.215 Td /F1 9.8 Tf [(studying organizational processes )] TJ ET
BT 175.815 259.215 Td /F5 9.8 Tf [(can)] TJ ET
BT 191.532 259.215 Td /F1 9.8 Tf [( be extended to include quantum computers as the basic modeling devices\).)] TJ ET
BT 26.250 239.810 Td /F1 9.8 Tf [(Thus, it is not necessary for the organizational modeler to impose unrealistic modeling conditions on the evolving entities \(as is )] TJ ET
BT 26.250 227.905 Td /F1 9.8 Tf [(often done by economic analyses of financial market behavior\) simply in order to ‘fit’ the studied phenomenon into a CA lattice )] TJ ET
BT 26.250 216.000 Td /F1 9.8 Tf [(model. Rather, CAs can provide general enough model of any organizational process that is representable in algorithmic form )] TJ ET
BT 26.250 204.096 Td /F1 9.8 Tf [(\(such as the form of a deterministic relationship between input and output, or independent and dependent variables\). The )] TJ ET
BT 26.250 192.191 Td /F1 9.8 Tf [(modeler’s problem is transformed, from the problem of how to get organizational phenomena that are already agreed-upon by )] TJ ET
BT 26.250 180.286 Td /F1 9.8 Tf [(researchers as ontologically ‘valid’ to fit into the straitjacket of ‘cells’ and ‘evolution rules’ to choosing from a multitude of local )] TJ ET
BT 26.250 168.381 Td /F1 9.8 Tf [(rules and interacting entities that together can produce particular patterns.)] TJ ET
BT 26.250 131.779 Td /F4 12.0 Tf [(Reduction of ‘complex’ rules, tasks and behaviors to ‘simple’ rule sets)] TJ ET
BT 26.250 111.825 Td /F1 9.8 Tf [(It is possible for either the modeler or the organizational designer, to trade off between the size of the ‘alphabet’ that defines the )] TJ ET
BT 26.250 99.920 Td /F1 9.8 Tf [(number of possible states that a cell can take on \(this defines the local, informational complexity of each interacting element\) )] TJ ET
BT 26.250 88.015 Td /F1 9.8 Tf [(and the complexity of the local interaction rules \(these define the computational sophistication of each interacting element\).)] TJ ET
BT 26.250 68.610 Td /F1 9.8 Tf [(Thus, it is possible, as a modeling \(or design\) assumption, to make trade-offs between the computational complexity of local )] TJ ET
BT 26.250 56.706 Td /F1 9.8 Tf [(rules and the informational complexity of local elements or cells. When the individual cells model individual agents within the )] TJ ET
BT 26.250 44.801 Td /F1 9.8 Tf [(organization, this exchangeability between computational and informational complexity becomes a critical capability for the )] TJ ET
Q
q
15.000 30.515 577.500 746.485 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(\(see, for instance, Lomi & Larsen, 2001\). Researchers have produced sophisticated models of organizational behavior starting )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(from postulates of bounded rationality and severe cognitive limitations at the level of individual agents making up the )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(organization \(modeled by a restricted set of local decision rules or heuristics\), which allow them to model these agents using )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(restricted state alphabets and simple, local, rule sets. By ‘simple’ one usually means \(often without stating\), admitting of low-)] TJ ET
BT 26.250 719.857 Td /F1 9.8 Tf [(Kolmogorov-complexity representations \(Li & Vitanyi, 1993\). Individual memory limitations—‘informational limitations’—are )] TJ ET
BT 26.250 707.952 Td /F1 9.8 Tf [(handled by the ‘small alphabet’ restriction and the restriction on the informational complexity of the rules of interaction, and )] TJ ET
BT 26.250 696.048 Td /F1 9.8 Tf [(individual computational limitations are handled by restricting the ‘search for rules’ to a space of computationally tractable ones. )] TJ ET
BT 26.250 684.143 Td /F1 9.8 Tf [(Individual agents, for instance, may be assumed to not be able—or, willing—to solve NP-hard problems \(Moldoveanu & Bauer, )] TJ ET
BT 26.250 672.238 Td /F1 9.8 Tf [(2004\)—indeed, they are assumed to )] TJ ET
BT 186.667 672.238 Td /F5 9.8 Tf [(conceptualize)] TJ ET
BT 246.278 672.238 Td /F1 9.8 Tf [( their predicaments in terms of )] TJ ET
BT 380.116 672.238 Td /F5 9.8 Tf [(P-hard)] TJ ET
BT 409.376 672.238 Td /F1 9.8 Tf [( rather than )] TJ ET
BT 461.948 672.238 Td /F5 9.8 Tf [(NP-hard)] TJ ET
BT 498.247 672.238 Td /F1 9.8 Tf [( problems )] TJ ET
BT 26.250 660.333 Td /F1 9.8 Tf [(\(Moldoveanu, 2006\).)] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(But, individual cells are not always )] TJ ET
BT 177.434 640.929 Td /F5 9.8 Tf [(individual agents)] TJ ET
BT 250.052 640.929 Td /F1 9.8 Tf [(—i.e., stylized models of humans—in CA modeling. For instance, McKelvey )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(\(1999\), and Rivkin \(2000\) use CA models to represent coupled organizational activity sets. Organizational states are encoded )] TJ ET
BT 26.250 617.119 Td /F1 9.8 Tf [(using binary-state \(‘logic gate’\) N-tuples of elements that are, on average, mutually N-wise coupled, and explore the dynamics )] TJ ET
BT 26.250 605.214 Td /F1 9.8 Tf [(of such ‘nets’ by allowing them to evolve and measuring certain characteristics of their long-run dynamics \(such as their period )] TJ ET
BT 26.250 593.310 Td /F1 9.8 Tf [(in the case of periodic dynamics\).)] TJ ET
BT 26.250 573.905 Td /F1 9.8 Tf [(CAs may also be used to ‘look inside’ the thinking of individual agents, whose local thinking and reasoning patterns are )] TJ ET
BT 26.250 562.000 Td /F1 9.8 Tf [(sometimes modeled using CAs. Rubinstein \(1996\) models the limited cognitive capacities of boundedly rational players )] TJ ET
BT 26.250 550.095 Td /F1 9.8 Tf [(engaged in iterated dominance reasoning to solve game-theoretic decision problems. The local rules of the CA device now act )] TJ ET
BT 26.250 538.191 Td /F1 9.8 Tf [(as propagators from a set of initial conditions \(the ‘problem formulation’\) to a ‘solution’ set that embodies both accuracy goals )] TJ ET
BT 26.250 526.286 Td /F1 9.8 Tf [(and time constraints. CAs can be set up to evaluate arbitrary logical expressions according to certain formulas and thus can act )] TJ ET
BT 26.250 514.381 Td /F1 9.8 Tf [(as propagators in the process of resolving a ‘well-structured problem’ \(Simon, 1973\); i.e., of proceeding, by self-evident steps \()] TJ ET
BT 26.250 502.476 Td /F5 9.8 Tf [(modus ponens and modus tollens)] TJ ET
BT 172.032 502.476 Td /F1 9.8 Tf [(\) from a set of initial conditions, to an ‘answer’.)] TJ ET
BT 26.250 483.072 Td /F1 9.8 Tf [(In view of these parallel developments, we have reason to ask: is there a ‘best, or, better way’ to proceed with the development )] TJ ET
BT 26.250 471.167 Td /F1 9.8 Tf [(of CA models in organizational research, such as:)] TJ ET
0.965 0.965 0.965 rg
26.250 335.762 555.000 125.524 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 461.286 m 581.250 461.286 l 581.250 460.536 l 26.250 460.536 l f
26.250 335.762 m 581.250 335.762 l 581.250 336.512 l 26.250 336.512 l f
0.271 0.267 0.267 rg
BT 41.206 442.030 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 442.012 Td /F5 9.8 Tf [(Downward)] TJ ET
BT 100.799 442.012 Td /F1 9.8 Tf [( into a further decomposition of models of individual agents themselves as evolving complex entities?)] TJ ET
BT 41.206 418.875 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 418.857 Td /F5 9.8 Tf [(Upward)] TJ ET
BT 88.339 418.857 Td /F1 9.8 Tf [( into models of larger organizational sub-units as interacting elements?)] TJ ET
BT 41.206 395.720 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 395.703 Td /F5 9.8 Tf [(Inward)] TJ ET
BT 84.010 395.703 Td /F1 9.8 Tf [( towards more sophisticated models of individual cognition and reasoning?)] TJ ET
BT 41.206 372.565 Td /F1 9.8 Tf [(4.)] TJ ET
BT 54.750 372.548 Td /F5 9.8 Tf [(Sideways)] TJ ET
BT 96.470 372.548 Td /F1 9.8 Tf [( into using CA elements to represent basic entities—such as ‘activities’, or, elementary behaviors—that can )] TJ ET
BT 54.750 360.643 Td /F1 9.8 Tf [(plausibly be conceived as being linked by deterministic interaction rules?)] TJ ET
BT 26.250 318.738 Td /F1 9.8 Tf [(It is clear that CA models are sufficiently general to be used as representational devices for many different types of ‘ontologies’. )] TJ ET
BT 26.250 306.834 Td /F1 9.8 Tf [(Wolfram \(2002\) realizes this, and suggests that CA models are far more general than any one of these modeling applications. )] TJ ET
BT 26.250 294.929 Td /F1 9.8 Tf [(He constructs CA models for general computational processes and argues that any natural process —once understood as a )] TJ ET
BT 26.250 283.024 Td /F1 9.8 Tf [(computational process—can be simulated as a CA. \(He focuses, to be sure, on deterministic, rather than quantum computation; )] TJ ET
BT 26.250 271.119 Td /F1 9.8 Tf [(and, the extension of his arguments to quantum computation is not straightforward, but, the basic computational framework for )] TJ ET
BT 26.250 259.215 Td /F1 9.8 Tf [(studying organizational processes )] TJ ET
BT 175.815 259.215 Td /F5 9.8 Tf [(can)] TJ ET
BT 191.532 259.215 Td /F1 9.8 Tf [( be extended to include quantum computers as the basic modeling devices\).)] TJ ET
BT 26.250 239.810 Td /F1 9.8 Tf [(Thus, it is not necessary for the organizational modeler to impose unrealistic modeling conditions on the evolving entities \(as is )] TJ ET
BT 26.250 227.905 Td /F1 9.8 Tf [(often done by economic analyses of financial market behavior\) simply in order to ‘fit’ the studied phenomenon into a CA lattice )] TJ ET
BT 26.250 216.000 Td /F1 9.8 Tf [(model. Rather, CAs can provide general enough model of any organizational process that is representable in algorithmic form )] TJ ET
BT 26.250 204.096 Td /F1 9.8 Tf [(\(such as the form of a deterministic relationship between input and output, or independent and dependent variables\). The )] TJ ET
BT 26.250 192.191 Td /F1 9.8 Tf [(modeler’s problem is transformed, from the problem of how to get organizational phenomena that are already agreed-upon by )] TJ ET
BT 26.250 180.286 Td /F1 9.8 Tf [(researchers as ontologically ‘valid’ to fit into the straitjacket of ‘cells’ and ‘evolution rules’ to choosing from a multitude of local )] TJ ET
BT 26.250 168.381 Td /F1 9.8 Tf [(rules and interacting entities that together can produce particular patterns.)] TJ ET
BT 26.250 131.779 Td /F4 12.0 Tf [(Reduction of ‘complex’ rules, tasks and behaviors to ‘simple’ rule sets)] TJ ET
BT 26.250 111.825 Td /F1 9.8 Tf [(It is possible for either the modeler or the organizational designer, to trade off between the size of the ‘alphabet’ that defines the )] TJ ET
BT 26.250 99.920 Td /F1 9.8 Tf [(number of possible states that a cell can take on \(this defines the local, informational complexity of each interacting element\) )] TJ ET
BT 26.250 88.015 Td /F1 9.8 Tf [(and the complexity of the local interaction rules \(these define the computational sophistication of each interacting element\).)] TJ ET
BT 26.250 68.610 Td /F1 9.8 Tf [(Thus, it is possible, as a modeling \(or design\) assumption, to make trade-offs between the computational complexity of local )] TJ ET
BT 26.250 56.706 Td /F1 9.8 Tf [(rules and the informational complexity of local elements or cells. When the individual cells model individual agents within the )] TJ ET
BT 26.250 44.801 Td /F1 9.8 Tf [(organization, this exchangeability between computational and informational complexity becomes a critical capability for the )] TJ ET
Q
q
0.000 0.000 0.000 rg
BT 291.710 19.825 Td /F1 11.0 Tf [(7)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
Q
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15.000 23.997 577.500 753.003 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(modeler, as it provides an explicit way to make trade-offs between the use of short-run memory and the use of computational )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(power, a key design trade-off for any computational system.)] TJ ET
BT 26.250 718.969 Td /F4 12.0 Tf [(Reduction of organizational phenomena to computationally simple, evolving local rule sets)] TJ ET
BT 26.250 699.015 Td /F1 9.8 Tf [(If it is possible to reduce any organizational phenomenon of interest to a computational process, and that computational process )] TJ ET
BT 26.250 687.110 Td /F1 9.8 Tf [(to a CA model, then it becomes possible to reduce any organizational process that can be simulated to a corresponding CA )] TJ ET
BT 26.250 675.205 Td /F1 9.8 Tf [(model. CA models are structurally rich enough to allow us to tailor assumptions about individual elements to different cell-level )] TJ ET
BT 26.250 663.300 Td /F1 9.8 Tf [(alphabets and interaction rule sets of varying complexities. We can then apply CA models to the understanding of rule-based )] TJ ET
BT 26.250 651.396 Td /F1 9.8 Tf [(interactions between )] TJ ET
BT 118.378 651.396 Td /F5 9.8 Tf [(individual agents)] TJ ET
BT 190.996 651.396 Td /F1 9.8 Tf [( within organizations \(in harmony with the dominant paradigm in the theory of )] TJ ET
BT 26.250 639.491 Td /F1 9.8 Tf [(organizations that has emerged from the writings of Simon \(1947\), Cyert \(Cyert & March, 1963\), March and Simon \(1958\), )] TJ ET
BT 26.250 627.586 Td /F1 9.8 Tf [(Newell \(1990\) and Nelson and Winter \(1982\). Thus, we will focus of models that consider individual )] TJ ET
BT 455.952 627.586 Td /F5 9.8 Tf [(agents)] TJ ET
BT 485.221 627.586 Td /F1 9.8 Tf [( \(rather than activity )] TJ ET
BT 26.250 615.681 Td /F1 9.8 Tf [(sets, or the neurons of the pre-frontal and dorso-lateral cortex of interacting agents, for instance\), as the basic elements of CAs. )] TJ ET
BT 26.250 603.777 Td /F1 9.8 Tf [(In spite of this specialization of the argument of the paper, we note that it is possible \(and often desirable\) to apply CA )] TJ ET
BT 26.250 591.872 Td /F1 9.8 Tf [(decompositions and models to the understanding of intra-personal processes \(involving the interaction of various motivational )] TJ ET
BT 26.250 579.967 Td /F1 9.8 Tf [(systems or of various computational ‘nodes’ that carry out calculations\) or to that of inter-organizational processes \(involving the )] TJ ET
BT 26.250 568.062 Td /F1 9.8 Tf [(characterization of whole organizations as ‘cells’ in a CA model\); i.e., to think of a multi-resolution decomposition of )] TJ ET
BT 26.250 556.158 Td /F1 9.8 Tf [(organizational ‘reality’ \(as seen through the lens of computable models thereof\), with CA models applying at multiple levels of )] TJ ET
BT 26.250 544.253 Td /F1 9.8 Tf [(analysis.)] TJ ET
BT 26.250 507.650 Td /F4 12.0 Tf [(Which rules emerge, and why do these rules emerge? Modeling and predicting the dynamics of )] TJ ET
BT 26.250 492.998 Td /F4 12.0 Tf [(organizational rules.)] TJ ET
BT 26.250 473.044 Td /F1 9.8 Tf [(If we take seriously, as a modeling assumption, the picture of organizational behavior as macro-scopic behavior bound by rules )] TJ ET
BT 26.250 461.139 Td /F1 9.8 Tf [(that determine local agent-agent interactions, then individual behavior, on this view, can be understood as a purposive attempt )] TJ ET
BT 26.250 449.235 Td /F1 9.8 Tf [(to )] TJ ET
BT 37.092 449.235 Td /F5 9.8 Tf [(correspond)] TJ ET
BT 85.862 449.235 Td /F1 9.8 Tf [( or )] TJ ET
BT 99.950 449.235 Td /F5 9.8 Tf [(conform)] TJ ET
BT 135.167 449.235 Td /F1 9.8 Tf [( to a set of rules: of grammar and discourse ethics for modeling the production of speech acts and )] TJ ET
BT 26.250 437.330 Td /F1 9.8 Tf [(discoursive behavior \(Alexy, 1988\); of social norms in the production of social behavior \(March, 1994\): of the rules of rationality )] TJ ET
BT 26.250 425.425 Td /F1 9.8 Tf [(in making decisions and implementing choices based on those decisions \(Elster, 1982\), of the Bayesian normative logic of )] TJ ET
BT 26.250 413.520 Td /F1 9.8 Tf [(epistemic rationality in the production of judgments about uncertain future events \(Earman, 1992\); of the rules of computation in )] TJ ET
BT 26.250 401.616 Td /F1 9.8 Tf [(producing models and simulations of ambiguous phenomena \(Turing, 1950\). The ability to construct CA models that effectively )] TJ ET
BT 26.250 389.711 Td /F1 9.8 Tf [(reduce any of these rule systems to a set of N-color \(N-complex fundamental alphabets\), K-neighbor \(K-complex contingencies\) )] TJ ET
BT 26.250 377.806 Td /F1 9.8 Tf [(rule sets allows us to speak of any organizational phenomenon as the instantiation of the macroscopic dynamics of such an )] TJ ET
BT 26.250 365.901 Td /F1 9.8 Tf [(underlying rule set.)] TJ ET
BT 26.250 346.497 Td /F1 9.8 Tf [(An interesting question for the modeler, is: which rules will emerge as the best models of organizational phenomena? Why do )] TJ ET
BT 26.250 334.592 Td /F1 9.8 Tf [(these rules, rather than other rules, emerge over time? How and why do fundamental modeling rules change as a function of )] TJ ET
BT 26.250 322.687 Td /F1 9.8 Tf [(time?)] TJ ET
BT 26.250 303.282 Td /F5 9.8 Tf [(The existence and importance of ‘universal rules’: rules that can replicate patterns generated by any other rules)] TJ ET
BT 505.843 303.282 Td /F1 9.8 Tf [(. Wolfram \(2002\) )] TJ ET
BT 26.250 291.378 Td /F1 9.8 Tf [(notes that, of the 256 basic 2-color, 2-neighbor rule regimes there are some rules that can properly called ‘universal rules’. )] TJ ET
BT 26.250 279.473 Td /F1 9.8 Tf [(Universal rules are rules that, given enough time, can mimic the macroscopic pattern that is produced by )] TJ ET
BT 479.264 279.473 Td /F5 9.8 Tf [(any other)] TJ ET
BT 519.912 279.473 Td /F1 9.8 Tf [( single rule. )] TJ ET
BT 26.250 267.568 Td /F1 9.8 Tf [(They may require more computational effort to do so—they may, in other words, require that the rules in question be used to )] TJ ET
BT 26.250 255.663 Td /F1 9.8 Tf [(perform more computations than those required to achieve that pattern with the rule systems that they emulate; but they are )] TJ ET
BT 26.250 243.759 Td /F1 9.8 Tf [(guaranteed to produce the ensemble behavior \(represented in a CA by the two-dimensional, N-color pattern of the CA lattice )] TJ ET
BT 26.250 231.854 Td /F1 9.8 Tf [(that emerges after M iterations of the fundamental CA algorithm\) that is produced by the rules that they emulate.)] TJ ET
BT 26.250 212.449 Td /F1 9.8 Tf [(There is a direct analogy between the existence of such universal rules and the existence of simple yet ‘powerful’ models of )] TJ ET
BT 26.250 200.544 Td /F1 9.8 Tf [(organizational behavior \(such as rational choice-based models; or game-theoretic models\) that can essentially model any )] TJ ET
BT 26.250 188.640 Td /F1 9.8 Tf [(organizational phenomenon. The ‘no-fat’ modeling approach of game theorists \(Camerer, 1994\) consists of \(a\) finding an )] TJ ET
BT 26.250 176.735 Td /F1 9.8 Tf [(organization-level or industry-level regularity \(a macroscopic pattern, in the language of CAs\), \(b\) positing a set of agent-level )] TJ ET
BT 26.250 164.830 Td /F1 9.8 Tf [(strategies, payoffs and conjectures \(including conjectures about other agents and about other agents’ conjectures\) and \(c\) )] TJ ET
BT 26.250 152.925 Td /F1 9.8 Tf [(aggregating these agent-level micro-rules to simulate the evolution of the macroscopic phenomenon that is to be explained.)] TJ ET
BT 26.250 133.521 Td /F1 9.8 Tf [(A “powerful” explanation of an organizational phenomenon is one that reduces macroscopic patterns to microscopic \(agent-)] TJ ET
BT 26.250 121.616 Td /F1 9.8 Tf [(level, in this case\) behavioral patterns \(modeled as rules for behavior, or rules for choice, or rules for forming judgments, or )] TJ ET
BT 26.250 109.711 Td /F1 9.8 Tf [(rules for aggregating judgments in an actionable mind-set\). Not surprisingly, game theoretic \(more generally, rational choice-)] TJ ET
BT 26.250 97.806 Td /F1 9.8 Tf [(theoretic\) explanation of multi-agent phenomena in organizations are popular \(as both academic models and self-models, or )] TJ ET
BT 26.250 85.902 Td /F1 9.8 Tf [(social identities\), because they seem to be able to explain anything. They can be thought of as ‘universal rules’: given enough )] TJ ET
BT 26.250 73.997 Td /F1 9.8 Tf [(computations, they will be able to explain macroscopic behavioral patterns in most organizational contexts. There is, then, a )] TJ ET
BT 26.250 62.092 Td /F1 9.8 Tf [(direct application of Wolfram’s result regarding rule universality to the problem of predicting the kinds of rules that will emerge )] TJ ET
BT 26.250 50.187 Td /F1 9.8 Tf [(over time in explanations of organizational phenomena. Now, if we assume that explanatory models become interaction )] TJ ET
BT 26.250 38.283 Td /F1 9.8 Tf [(models—by a double hermeneutic by which individuals come to instantiate and follow as behavioral norms the rule sets they )] TJ ET
Q
q
15.000 23.997 577.500 753.003 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(modeler, as it provides an explicit way to make trade-offs between the use of short-run memory and the use of computational )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(power, a key design trade-off for any computational system.)] TJ ET
BT 26.250 718.969 Td /F4 12.0 Tf [(Reduction of organizational phenomena to computationally simple, evolving local rule sets)] TJ ET
BT 26.250 699.015 Td /F1 9.8 Tf [(If it is possible to reduce any organizational phenomenon of interest to a computational process, and that computational process )] TJ ET
BT 26.250 687.110 Td /F1 9.8 Tf [(to a CA model, then it becomes possible to reduce any organizational process that can be simulated to a corresponding CA )] TJ ET
BT 26.250 675.205 Td /F1 9.8 Tf [(model. CA models are structurally rich enough to allow us to tailor assumptions about individual elements to different cell-level )] TJ ET
BT 26.250 663.300 Td /F1 9.8 Tf [(alphabets and interaction rule sets of varying complexities. We can then apply CA models to the understanding of rule-based )] TJ ET
BT 26.250 651.396 Td /F1 9.8 Tf [(interactions between )] TJ ET
BT 118.378 651.396 Td /F5 9.8 Tf [(individual agents)] TJ ET
BT 190.996 651.396 Td /F1 9.8 Tf [( within organizations \(in harmony with the dominant paradigm in the theory of )] TJ ET
BT 26.250 639.491 Td /F1 9.8 Tf [(organizations that has emerged from the writings of Simon \(1947\), Cyert \(Cyert & March, 1963\), March and Simon \(1958\), )] TJ ET
BT 26.250 627.586 Td /F1 9.8 Tf [(Newell \(1990\) and Nelson and Winter \(1982\). Thus, we will focus of models that consider individual )] TJ ET
BT 455.952 627.586 Td /F5 9.8 Tf [(agents)] TJ ET
BT 485.221 627.586 Td /F1 9.8 Tf [( \(rather than activity )] TJ ET
BT 26.250 615.681 Td /F1 9.8 Tf [(sets, or the neurons of the pre-frontal and dorso-lateral cortex of interacting agents, for instance\), as the basic elements of CAs. )] TJ ET
BT 26.250 603.777 Td /F1 9.8 Tf [(In spite of this specialization of the argument of the paper, we note that it is possible \(and often desirable\) to apply CA )] TJ ET
BT 26.250 591.872 Td /F1 9.8 Tf [(decompositions and models to the understanding of intra-personal processes \(involving the interaction of various motivational )] TJ ET
BT 26.250 579.967 Td /F1 9.8 Tf [(systems or of various computational ‘nodes’ that carry out calculations\) or to that of inter-organizational processes \(involving the )] TJ ET
BT 26.250 568.062 Td /F1 9.8 Tf [(characterization of whole organizations as ‘cells’ in a CA model\); i.e., to think of a multi-resolution decomposition of )] TJ ET
BT 26.250 556.158 Td /F1 9.8 Tf [(organizational ‘reality’ \(as seen through the lens of computable models thereof\), with CA models applying at multiple levels of )] TJ ET
BT 26.250 544.253 Td /F1 9.8 Tf [(analysis.)] TJ ET
BT 26.250 507.650 Td /F4 12.0 Tf [(Which rules emerge, and why do these rules emerge? Modeling and predicting the dynamics of )] TJ ET
BT 26.250 492.998 Td /F4 12.0 Tf [(organizational rules.)] TJ ET
BT 26.250 473.044 Td /F1 9.8 Tf [(If we take seriously, as a modeling assumption, the picture of organizational behavior as macro-scopic behavior bound by rules )] TJ ET
BT 26.250 461.139 Td /F1 9.8 Tf [(that determine local agent-agent interactions, then individual behavior, on this view, can be understood as a purposive attempt )] TJ ET
BT 26.250 449.235 Td /F1 9.8 Tf [(to )] TJ ET
BT 37.092 449.235 Td /F5 9.8 Tf [(correspond)] TJ ET
BT 85.862 449.235 Td /F1 9.8 Tf [( or )] TJ ET
BT 99.950 449.235 Td /F5 9.8 Tf [(conform)] TJ ET
BT 135.167 449.235 Td /F1 9.8 Tf [( to a set of rules: of grammar and discourse ethics for modeling the production of speech acts and )] TJ ET
BT 26.250 437.330 Td /F1 9.8 Tf [(discoursive behavior \(Alexy, 1988\); of social norms in the production of social behavior \(March, 1994\): of the rules of rationality )] TJ ET
BT 26.250 425.425 Td /F1 9.8 Tf [(in making decisions and implementing choices based on those decisions \(Elster, 1982\), of the Bayesian normative logic of )] TJ ET
BT 26.250 413.520 Td /F1 9.8 Tf [(epistemic rationality in the production of judgments about uncertain future events \(Earman, 1992\); of the rules of computation in )] TJ ET
BT 26.250 401.616 Td /F1 9.8 Tf [(producing models and simulations of ambiguous phenomena \(Turing, 1950\). The ability to construct CA models that effectively )] TJ ET
BT 26.250 389.711 Td /F1 9.8 Tf [(reduce any of these rule systems to a set of N-color \(N-complex fundamental alphabets\), K-neighbor \(K-complex contingencies\) )] TJ ET
BT 26.250 377.806 Td /F1 9.8 Tf [(rule sets allows us to speak of any organizational phenomenon as the instantiation of the macroscopic dynamics of such an )] TJ ET
BT 26.250 365.901 Td /F1 9.8 Tf [(underlying rule set.)] TJ ET
BT 26.250 346.497 Td /F1 9.8 Tf [(An interesting question for the modeler, is: which rules will emerge as the best models of organizational phenomena? Why do )] TJ ET
BT 26.250 334.592 Td /F1 9.8 Tf [(these rules, rather than other rules, emerge over time? How and why do fundamental modeling rules change as a function of )] TJ ET
BT 26.250 322.687 Td /F1 9.8 Tf [(time?)] TJ ET
BT 26.250 303.282 Td /F5 9.8 Tf [(The existence and importance of ‘universal rules’: rules that can replicate patterns generated by any other rules)] TJ ET
BT 505.843 303.282 Td /F1 9.8 Tf [(. Wolfram \(2002\) )] TJ ET
BT 26.250 291.378 Td /F1 9.8 Tf [(notes that, of the 256 basic 2-color, 2-neighbor rule regimes there are some rules that can properly called ‘universal rules’. )] TJ ET
BT 26.250 279.473 Td /F1 9.8 Tf [(Universal rules are rules that, given enough time, can mimic the macroscopic pattern that is produced by )] TJ ET
BT 479.264 279.473 Td /F5 9.8 Tf [(any other)] TJ ET
BT 519.912 279.473 Td /F1 9.8 Tf [( single rule. )] TJ ET
BT 26.250 267.568 Td /F1 9.8 Tf [(They may require more computational effort to do so—they may, in other words, require that the rules in question be used to )] TJ ET
BT 26.250 255.663 Td /F1 9.8 Tf [(perform more computations than those required to achieve that pattern with the rule systems that they emulate; but they are )] TJ ET
BT 26.250 243.759 Td /F1 9.8 Tf [(guaranteed to produce the ensemble behavior \(represented in a CA by the two-dimensional, N-color pattern of the CA lattice )] TJ ET
BT 26.250 231.854 Td /F1 9.8 Tf [(that emerges after M iterations of the fundamental CA algorithm\) that is produced by the rules that they emulate.)] TJ ET
BT 26.250 212.449 Td /F1 9.8 Tf [(There is a direct analogy between the existence of such universal rules and the existence of simple yet ‘powerful’ models of )] TJ ET
BT 26.250 200.544 Td /F1 9.8 Tf [(organizational behavior \(such as rational choice-based models; or game-theoretic models\) that can essentially model any )] TJ ET
BT 26.250 188.640 Td /F1 9.8 Tf [(organizational phenomenon. The ‘no-fat’ modeling approach of game theorists \(Camerer, 1994\) consists of \(a\) finding an )] TJ ET
BT 26.250 176.735 Td /F1 9.8 Tf [(organization-level or industry-level regularity \(a macroscopic pattern, in the language of CAs\), \(b\) positing a set of agent-level )] TJ ET
BT 26.250 164.830 Td /F1 9.8 Tf [(strategies, payoffs and conjectures \(including conjectures about other agents and about other agents’ conjectures\) and \(c\) )] TJ ET
BT 26.250 152.925 Td /F1 9.8 Tf [(aggregating these agent-level micro-rules to simulate the evolution of the macroscopic phenomenon that is to be explained.)] TJ ET
BT 26.250 133.521 Td /F1 9.8 Tf [(A “powerful” explanation of an organizational phenomenon is one that reduces macroscopic patterns to microscopic \(agent-)] TJ ET
BT 26.250 121.616 Td /F1 9.8 Tf [(level, in this case\) behavioral patterns \(modeled as rules for behavior, or rules for choice, or rules for forming judgments, or )] TJ ET
BT 26.250 109.711 Td /F1 9.8 Tf [(rules for aggregating judgments in an actionable mind-set\). Not surprisingly, game theoretic \(more generally, rational choice-)] TJ ET
BT 26.250 97.806 Td /F1 9.8 Tf [(theoretic\) explanation of multi-agent phenomena in organizations are popular \(as both academic models and self-models, or )] TJ ET
BT 26.250 85.902 Td /F1 9.8 Tf [(social identities\), because they seem to be able to explain anything. They can be thought of as ‘universal rules’: given enough )] TJ ET
BT 26.250 73.997 Td /F1 9.8 Tf [(computations, they will be able to explain macroscopic behavioral patterns in most organizational contexts. There is, then, a )] TJ ET
BT 26.250 62.092 Td /F1 9.8 Tf [(direct application of Wolfram’s result regarding rule universality to the problem of predicting the kinds of rules that will emerge )] TJ ET
BT 26.250 50.187 Td /F1 9.8 Tf [(over time in explanations of organizational phenomena. Now, if we assume that explanatory models become interaction )] TJ ET
BT 26.250 38.283 Td /F1 9.8 Tf [(models—by a double hermeneutic by which individuals come to instantiate and follow as behavioral norms the rule sets they )] TJ ET
Q
q
15.000 23.997 577.500 753.003 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(modeler, as it provides an explicit way to make trade-offs between the use of short-run memory and the use of computational )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(power, a key design trade-off for any computational system.)] TJ ET
BT 26.250 718.969 Td /F4 12.0 Tf [(Reduction of organizational phenomena to computationally simple, evolving local rule sets)] TJ ET
BT 26.250 699.015 Td /F1 9.8 Tf [(If it is possible to reduce any organizational phenomenon of interest to a computational process, and that computational process )] TJ ET
BT 26.250 687.110 Td /F1 9.8 Tf [(to a CA model, then it becomes possible to reduce any organizational process that can be simulated to a corresponding CA )] TJ ET
BT 26.250 675.205 Td /F1 9.8 Tf [(model. CA models are structurally rich enough to allow us to tailor assumptions about individual elements to different cell-level )] TJ ET
BT 26.250 663.300 Td /F1 9.8 Tf [(alphabets and interaction rule sets of varying complexities. We can then apply CA models to the understanding of rule-based )] TJ ET
BT 26.250 651.396 Td /F1 9.8 Tf [(interactions between )] TJ ET
BT 118.378 651.396 Td /F5 9.8 Tf [(individual agents)] TJ ET
BT 190.996 651.396 Td /F1 9.8 Tf [( within organizations \(in harmony with the dominant paradigm in the theory of )] TJ ET
BT 26.250 639.491 Td /F1 9.8 Tf [(organizations that has emerged from the writings of Simon \(1947\), Cyert \(Cyert & March, 1963\), March and Simon \(1958\), )] TJ ET
BT 26.250 627.586 Td /F1 9.8 Tf [(Newell \(1990\) and Nelson and Winter \(1982\). Thus, we will focus of models that consider individual )] TJ ET
BT 455.952 627.586 Td /F5 9.8 Tf [(agents)] TJ ET
BT 485.221 627.586 Td /F1 9.8 Tf [( \(rather than activity )] TJ ET
BT 26.250 615.681 Td /F1 9.8 Tf [(sets, or the neurons of the pre-frontal and dorso-lateral cortex of interacting agents, for instance\), as the basic elements of CAs. )] TJ ET
BT 26.250 603.777 Td /F1 9.8 Tf [(In spite of this specialization of the argument of the paper, we note that it is possible \(and often desirable\) to apply CA )] TJ ET
BT 26.250 591.872 Td /F1 9.8 Tf [(decompositions and models to the understanding of intra-personal processes \(involving the interaction of various motivational )] TJ ET
BT 26.250 579.967 Td /F1 9.8 Tf [(systems or of various computational ‘nodes’ that carry out calculations\) or to that of inter-organizational processes \(involving the )] TJ ET
BT 26.250 568.062 Td /F1 9.8 Tf [(characterization of whole organizations as ‘cells’ in a CA model\); i.e., to think of a multi-resolution decomposition of )] TJ ET
BT 26.250 556.158 Td /F1 9.8 Tf [(organizational ‘reality’ \(as seen through the lens of computable models thereof\), with CA models applying at multiple levels of )] TJ ET
BT 26.250 544.253 Td /F1 9.8 Tf [(analysis.)] TJ ET
BT 26.250 507.650 Td /F4 12.0 Tf [(Which rules emerge, and why do these rules emerge? Modeling and predicting the dynamics of )] TJ ET
BT 26.250 492.998 Td /F4 12.0 Tf [(organizational rules.)] TJ ET
BT 26.250 473.044 Td /F1 9.8 Tf [(If we take seriously, as a modeling assumption, the picture of organizational behavior as macro-scopic behavior bound by rules )] TJ ET
BT 26.250 461.139 Td /F1 9.8 Tf [(that determine local agent-agent interactions, then individual behavior, on this view, can be understood as a purposive attempt )] TJ ET
BT 26.250 449.235 Td /F1 9.8 Tf [(to )] TJ ET
BT 37.092 449.235 Td /F5 9.8 Tf [(correspond)] TJ ET
BT 85.862 449.235 Td /F1 9.8 Tf [( or )] TJ ET
BT 99.950 449.235 Td /F5 9.8 Tf [(conform)] TJ ET
BT 135.167 449.235 Td /F1 9.8 Tf [( to a set of rules: of grammar and discourse ethics for modeling the production of speech acts and )] TJ ET
BT 26.250 437.330 Td /F1 9.8 Tf [(discoursive behavior \(Alexy, 1988\); of social norms in the production of social behavior \(March, 1994\): of the rules of rationality )] TJ ET
BT 26.250 425.425 Td /F1 9.8 Tf [(in making decisions and implementing choices based on those decisions \(Elster, 1982\), of the Bayesian normative logic of )] TJ ET
BT 26.250 413.520 Td /F1 9.8 Tf [(epistemic rationality in the production of judgments about uncertain future events \(Earman, 1992\); of the rules of computation in )] TJ ET
BT 26.250 401.616 Td /F1 9.8 Tf [(producing models and simulations of ambiguous phenomena \(Turing, 1950\). The ability to construct CA models that effectively )] TJ ET
BT 26.250 389.711 Td /F1 9.8 Tf [(reduce any of these rule systems to a set of N-color \(N-complex fundamental alphabets\), K-neighbor \(K-complex contingencies\) )] TJ ET
BT 26.250 377.806 Td /F1 9.8 Tf [(rule sets allows us to speak of any organizational phenomenon as the instantiation of the macroscopic dynamics of such an )] TJ ET
BT 26.250 365.901 Td /F1 9.8 Tf [(underlying rule set.)] TJ ET
BT 26.250 346.497 Td /F1 9.8 Tf [(An interesting question for the modeler, is: which rules will emerge as the best models of organizational phenomena? Why do )] TJ ET
BT 26.250 334.592 Td /F1 9.8 Tf [(these rules, rather than other rules, emerge over time? How and why do fundamental modeling rules change as a function of )] TJ ET
BT 26.250 322.687 Td /F1 9.8 Tf [(time?)] TJ ET
BT 26.250 303.282 Td /F5 9.8 Tf [(The existence and importance of ‘universal rules’: rules that can replicate patterns generated by any other rules)] TJ ET
BT 505.843 303.282 Td /F1 9.8 Tf [(. Wolfram \(2002\) )] TJ ET
BT 26.250 291.378 Td /F1 9.8 Tf [(notes that, of the 256 basic 2-color, 2-neighbor rule regimes there are some rules that can properly called ‘universal rules’. )] TJ ET
BT 26.250 279.473 Td /F1 9.8 Tf [(Universal rules are rules that, given enough time, can mimic the macroscopic pattern that is produced by )] TJ ET
BT 479.264 279.473 Td /F5 9.8 Tf [(any other)] TJ ET
BT 519.912 279.473 Td /F1 9.8 Tf [( single rule. )] TJ ET
BT 26.250 267.568 Td /F1 9.8 Tf [(They may require more computational effort to do so—they may, in other words, require that the rules in question be used to )] TJ ET
BT 26.250 255.663 Td /F1 9.8 Tf [(perform more computations than those required to achieve that pattern with the rule systems that they emulate; but they are )] TJ ET
BT 26.250 243.759 Td /F1 9.8 Tf [(guaranteed to produce the ensemble behavior \(represented in a CA by the two-dimensional, N-color pattern of the CA lattice )] TJ ET
BT 26.250 231.854 Td /F1 9.8 Tf [(that emerges after M iterations of the fundamental CA algorithm\) that is produced by the rules that they emulate.)] TJ ET
BT 26.250 212.449 Td /F1 9.8 Tf [(There is a direct analogy between the existence of such universal rules and the existence of simple yet ‘powerful’ models of )] TJ ET
BT 26.250 200.544 Td /F1 9.8 Tf [(organizational behavior \(such as rational choice-based models; or game-theoretic models\) that can essentially model any )] TJ ET
BT 26.250 188.640 Td /F1 9.8 Tf [(organizational phenomenon. The ‘no-fat’ modeling approach of game theorists \(Camerer, 1994\) consists of \(a\) finding an )] TJ ET
BT 26.250 176.735 Td /F1 9.8 Tf [(organization-level or industry-level regularity \(a macroscopic pattern, in the language of CAs\), \(b\) positing a set of agent-level )] TJ ET
BT 26.250 164.830 Td /F1 9.8 Tf [(strategies, payoffs and conjectures \(including conjectures about other agents and about other agents’ conjectures\) and \(c\) )] TJ ET
BT 26.250 152.925 Td /F1 9.8 Tf [(aggregating these agent-level micro-rules to simulate the evolution of the macroscopic phenomenon that is to be explained.)] TJ ET
BT 26.250 133.521 Td /F1 9.8 Tf [(A “powerful” explanation of an organizational phenomenon is one that reduces macroscopic patterns to microscopic \(agent-)] TJ ET
BT 26.250 121.616 Td /F1 9.8 Tf [(level, in this case\) behavioral patterns \(modeled as rules for behavior, or rules for choice, or rules for forming judgments, or )] TJ ET
BT 26.250 109.711 Td /F1 9.8 Tf [(rules for aggregating judgments in an actionable mind-set\). Not surprisingly, game theoretic \(more generally, rational choice-)] TJ ET
BT 26.250 97.806 Td /F1 9.8 Tf [(theoretic\) explanation of multi-agent phenomena in organizations are popular \(as both academic models and self-models, or )] TJ ET
BT 26.250 85.902 Td /F1 9.8 Tf [(social identities\), because they seem to be able to explain anything. They can be thought of as ‘universal rules’: given enough )] TJ ET
BT 26.250 73.997 Td /F1 9.8 Tf [(computations, they will be able to explain macroscopic behavioral patterns in most organizational contexts. There is, then, a )] TJ ET
BT 26.250 62.092 Td /F1 9.8 Tf [(direct application of Wolfram’s result regarding rule universality to the problem of predicting the kinds of rules that will emerge )] TJ ET
BT 26.250 50.187 Td /F1 9.8 Tf [(over time in explanations of organizational phenomena. Now, if we assume that explanatory models become interaction )] TJ ET
BT 26.250 38.283 Td /F1 9.8 Tf [(models—by a double hermeneutic by which individuals come to instantiate and follow as behavioral norms the rule sets they )] TJ ET
Q
q
0.000 0.000 0.000 rg
BT 291.710 19.825 Td /F1 11.0 Tf [(8)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
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BT 26.250 767.476 Td /F1 9.8 Tf [(use to explain their own behavior—then it is likely that universal rules—and the associated universal micro-models of agent )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(behavior—will emerge as dominant rules of interaction because of their virtually unlimited applicability to a large variety of )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(organizational scenarios and different organizational contexts. \(This modeling assumption is not far-fetched, can be understood )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(as a direct extension of Wittgenstein’s \(1953\) ‘no private language’ argument.)] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(Empirical studies of organizational rule systems \(March )] TJ ET
BT 267.358 712.357 Td /F5 9.8 Tf [(et al)] TJ ET
BT 285.785 712.357 Td /F1 9.8 Tf [(., 2000\) have focused on empirical regularities that regard the )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(incidence of rule births, rule deaths and rule transitions, as a function of changes in the environment of the organization, but, )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(have not—as yet—focused on the structural and semantic features of the rules that emerge as dominant rules, or of the rules )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(that regularly supersede other rules in the history of organizations. The concept of rule universality \(together with the posited )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(mechanism for the emergence of universal rules after repeated realizations of the organizational ‘game’ allows to make )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(predictions about the substantive attributes of dominant rules: we look for the emergence of universal rules in organizational )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(situations that reward the ability of organizational modelers and designers to create local rule sets with predictive capability over )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(a large number of different situations \(characterized by the macroscopic patterns of the resulting CA models\).)] TJ ET
BT 26.250 609.619 Td /F5 9.8 Tf [(Measuring the trade-off between global computational load and local informational depth in the selection of rules: a new )] TJ ET
BT 26.250 597.714 Td /F5 9.8 Tf [(characterization of organizational complexity)] TJ ET
BT 218.071 597.714 Td /F1 9.8 Tf [(. We can seek to understand the evolution of different sets of micro-rules within the )] TJ ET
BT 26.250 585.810 Td /F1 9.8 Tf [(organization as the embodiment or instantiation of trade-offs among four fundamental quantities that quantify and qualify both )] TJ ET
BT 26.250 573.905 Td /F1 9.8 Tf [(the local complexity of micro-rules and the global complexity of the evolving patterns:)] TJ ET
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0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(use to explain their own behavior—then it is likely that universal rules—and the associated universal micro-models of agent )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(behavior—will emerge as dominant rules of interaction because of their virtually unlimited applicability to a large variety of )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(organizational scenarios and different organizational contexts. \(This modeling assumption is not far-fetched, can be understood )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(as a direct extension of Wittgenstein’s \(1953\) ‘no private language’ argument.)] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(Empirical studies of organizational rule systems \(March )] TJ ET
BT 267.358 712.357 Td /F5 9.8 Tf [(et al)] TJ ET
BT 285.785 712.357 Td /F1 9.8 Tf [(., 2000\) have focused on empirical regularities that regard the )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(incidence of rule births, rule deaths and rule transitions, as a function of changes in the environment of the organization, but, )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(have not—as yet—focused on the structural and semantic features of the rules that emerge as dominant rules, or of the rules )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(that regularly supersede other rules in the history of organizations. The concept of rule universality \(together with the posited )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(mechanism for the emergence of universal rules after repeated realizations of the organizational ‘game’ allows to make )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(predictions about the substantive attributes of dominant rules: we look for the emergence of universal rules in organizational )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(situations that reward the ability of organizational modelers and designers to create local rule sets with predictive capability over )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(a large number of different situations \(characterized by the macroscopic patterns of the resulting CA models\).)] TJ ET
BT 26.250 609.619 Td /F5 9.8 Tf [(Measuring the trade-off between global computational load and local informational depth in the selection of rules: a new )] TJ ET
BT 26.250 597.714 Td /F5 9.8 Tf [(characterization of organizational complexity)] TJ ET
BT 218.071 597.714 Td /F1 9.8 Tf [(. We can seek to understand the evolution of different sets of micro-rules within the )] TJ ET
BT 26.250 585.810 Td /F1 9.8 Tf [(organization as the embodiment or instantiation of trade-offs among four fundamental quantities that quantify and qualify both )] TJ ET
BT 26.250 573.905 Td /F1 9.8 Tf [(the local complexity of micro-rules and the global complexity of the evolving patterns:)] TJ ET
0.965 0.965 0.965 rg
26.250 163.382 555.000 400.642 re f
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0.267 0.267 0.267 RG
26.250 564.024 m 581.250 564.024 l 581.250 563.274 l 26.250 563.274 l f
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15.000 163.382 577.500 613.618 re W n
0.271 0.267 0.267 rg
BT 26.250 767.476 Td /F1 9.8 Tf [(use to explain their own behavior—then it is likely that universal rules—and the associated universal micro-models of agent )] TJ ET
BT 26.250 755.571 Td /F1 9.8 Tf [(behavior—will emerge as dominant rules of interaction because of their virtually unlimited applicability to a large variety of )] TJ ET
BT 26.250 743.667 Td /F1 9.8 Tf [(organizational scenarios and different organizational contexts. \(This modeling assumption is not far-fetched, can be understood )] TJ ET
BT 26.250 731.762 Td /F1 9.8 Tf [(as a direct extension of Wittgenstein’s \(1953\) ‘no private language’ argument.)] TJ ET
BT 26.250 712.357 Td /F1 9.8 Tf [(Empirical studies of organizational rule systems \(March )] TJ ET
BT 267.358 712.357 Td /F5 9.8 Tf [(et al)] TJ ET
BT 285.785 712.357 Td /F1 9.8 Tf [(., 2000\) have focused on empirical regularities that regard the )] TJ ET
BT 26.250 700.452 Td /F1 9.8 Tf [(incidence of rule births, rule deaths and rule transitions, as a function of changes in the environment of the organization, but, )] TJ ET
BT 26.250 688.548 Td /F1 9.8 Tf [(have not—as yet—focused on the structural and semantic features of the rules that emerge as dominant rules, or of the rules )] TJ ET
BT 26.250 676.643 Td /F1 9.8 Tf [(that regularly supersede other rules in the history of organizations. The concept of rule universality \(together with the posited )] TJ ET
BT 26.250 664.738 Td /F1 9.8 Tf [(mechanism for the emergence of universal rules after repeated realizations of the organizational ‘game’ allows to make )] TJ ET
BT 26.250 652.833 Td /F1 9.8 Tf [(predictions about the substantive attributes of dominant rules: we look for the emergence of universal rules in organizational )] TJ ET
BT 26.250 640.929 Td /F1 9.8 Tf [(situations that reward the ability of organizational modelers and designers to create local rule sets with predictive capability over )] TJ ET
BT 26.250 629.024 Td /F1 9.8 Tf [(a large number of different situations \(characterized by the macroscopic patterns of the resulting CA models\).)] TJ ET
BT 26.250 609.619 Td /F5 9.8 Tf [(Measuring the trade-off between global computational load and local informational depth in the selection of rules: a new )] TJ ET
BT 26.250 597.714 Td /F5 9.8 Tf [(characterization of organizational complexity)] TJ ET
BT 218.071 597.714 Td /F1 9.8 Tf [(. We can seek to understand the evolution of different sets of micro-rules within the )] TJ ET
BT 26.250 585.810 Td /F1 9.8 Tf [(organization as the embodiment or instantiation of trade-offs among four fundamental quantities that quantify and qualify both )] TJ ET
BT 26.250 573.905 Td /F1 9.8 Tf [(the local complexity of micro-rules and the global complexity of the evolving patterns:)] TJ ET
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0.267 0.267 0.267 RG
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BT 291.710 19.825 Td /F1 11.0 Tf [(9)] TJ ET
BT 25.000 19.825 Td /F1 11.0 Tf [(Emergence: Complexity and Organization)] TJ ET
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BT 41.206 767.494 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 767.476 Td /F4 9.8 Tf [(The informational or algorithmic complexity of the local rule sets)] TJ ET
BT 354.913 767.476 Td /F1 9.8 Tf [(. This quantity \(which we shall refer to as )] TJ ET
BT 54.750 755.571 Td /F5 9.8 Tf [(local informational depth)] TJ ET
BT 160.421 755.571 Td /F1 9.8 Tf [(\) is the Kolmogorov complexity \(see Li & Vitanyi, 1993\) of the local CA rules, and can be )] TJ ET
BT 54.750 743.667 Td /F1 9.8 Tf [(defined as the length \(in bits or M-ary units of information\) of the minimum-length algorithm that can execute these )] TJ ET
BT 54.750 731.762 Td /F1 9.8 Tf [(rules. It is a measure of local ‘random access memory’—or short term memory required by an element of a CA to act )] TJ ET
BT 54.750 719.857 Td /F1 9.8 Tf [(according to ‘its’ local rule system: if the agent’s memory is not capable of supporting at least the number of states that )] TJ ET
BT 54.750 707.952 Td /F1 9.8 Tf [(the rule system that governs its local interactions refers to, then it will not be able to ‘sustain’ local interactions )] TJ ET
BT 54.750 696.048 Td /F1 9.8 Tf [(governed by that rule. If the CA element is an individual agent, then the local informational depth of the rules of the CA )] TJ ET
BT 54.750 684.143 Td /F1 9.8 Tf [(measures the short-term memory required in order for the agent to ‘g)] TJ ET
BT 351.696 684.143 Td /F5 9.8 Tf [(et al)] TJ ET
BT 370.124 684.143 Td /F1 9.8 Tf [(ong’ with other agents.)] TJ ET
BT 41.206 661.006 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 660.988 Td /F4 9.8 Tf [(The computational complexity of the local rule sets)] TJ ET
BT 292.065 660.988 Td /F1 9.8 Tf [(. This quantity measures the number of calculations required at )] TJ ET
BT 54.750 649.083 Td /F1 9.8 Tf [(the level of each CA element for the implementation of the local CA rules, as a function of the number of CA neighbors )] TJ ET
BT 54.750 637.179 Td /F1 9.8 Tf [(on which the state of the original CA element depends, and the number of possible states of the neighboring elements )] TJ ET
BT 54.750 625.274 Td /F1 9.8 Tf [(on which the state of each CA element depends. We shall refer to this quantity as the )] TJ ET
BT 424.899 625.274 Td /F5 9.8 Tf [(local computational load)] TJ ET
BT 529.487 625.274 Td /F1 9.8 Tf [( of the )] TJ ET
BT 54.750 613.369 Td /F1 9.8 Tf [(CA model \(Moldoveanu & Bauer, 2003; see Cormen )] TJ ET
BT 282.890 613.369 Td /F5 9.8 Tf [(et al)] TJ ET
BT 301.318 613.369 Td /F1 9.8 Tf [(., 1993 for a pedagogical introduction to computational )] TJ ET
BT 54.750 601.464 Td /F1 9.8 Tf [(complexity for algorithmic problems.For a 2-state CA model \(each element can take on the value of ‘0’ or ‘1’\) in which )] TJ ET
BT 54.750 589.560 Td /F1 9.8 Tf [(the state of each element depends linearly on the individual states of each of K neighbors, the local computational load )] TJ ET
BT 54.750 577.655 Td /F1 9.8 Tf [(for each CA element is bounded from above by 2K. Nonlinear dependencies of the state of one CA element on )] TJ ET
BT 54.750 565.750 Td /F1 9.8 Tf [(\(including ‘memory effects’\) will increase the number of operations required locally to compute the next state of the CA )] TJ ET
BT 54.750 553.845 Td /F1 9.8 Tf [(element from knowledge of the states of the neighboring elements. If each CA element is taken to model an agent \(in a )] TJ ET
BT 54.750 541.941 Td /F1 9.8 Tf [(multi-agent model of an organization\), then local computational load measures the computational power required of an )] TJ ET
BT 54.750 530.036 Td /F1 9.8 Tf [(agent to successfully interact with other agents according to the local rule sets.)] TJ ET
BT 41.206 506.899 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 506.881 Td /F4 9.8 Tf [(The global computational complexity of the resulting CA pattern produced by the iterative application of a set )] TJ ET
BT 54.750 494.976 Td /F4 9.8 Tf [(of micro-local rules or rule sets)] TJ ET
BT 199.966 494.976 Td /F1 9.8 Tf [(. This quantity, which is defined as a function of the total number of variables \()] TJ ET
BT 54.750 483.072 Td /F5 9.8 Tf [(CA elements x states\))] TJ ET
BT 150.115 483.072 Td /F1 9.8 Tf [( that determine the evolution of local CA elements, measures how ‘difficult’ it is for a particular )] TJ ET
BT 54.750 471.167 Td /F1 9.8 Tf [(micro-rule set to replicate a particular large-scale pattern of the CA. It is a measure of the computational complexity of )] TJ ET
BT 54.750 459.262 Td /F1 9.8 Tf [(producing a simulation of an organizational phenomenon starting from a set of agent-level models \(such as rational )] TJ ET
BT 54.750 447.357 Td /F1 9.8 Tf [(choice models or game-theoretic models\). This quantity measures the relative efficiency of using micro-rule systems )] TJ ET
BT 54.750 435.453 Td /F1 9.8 Tf [(\(organizational ‘common law’ and statutes is often written in terms of such rule systems\) in order to predict the global )] TJ ET
BT 54.750 423.548 Td /F1 9.8 Tf [(evolution of the organization. It also measures the relative difficulty for the modeler of explaining an organizational )] TJ ET
BT 54.750 411.643 Td /F1 9.8 Tf [(phenomenon based on a metaphysical commitment to methodological individualism \(i.e., to the explanation of social )] TJ ET
BT 54.750 399.738 Td /F1 9.8 Tf [(phenomena by reduction to individual-level phenomena\) and a set of models of individual behavior.)] TJ ET
BT 41.206 376.601 Td /F1 9.8 Tf [(4.)] TJ ET
BT 54.750 376.584 Td /F4 9.8 Tf [(The global informational depth of the resulting CA pattern.)] TJ ET
BT 325.615 376.584 Td /F1 9.8 Tf [( It is a measure of the relative ‘compressibility’ or )] TJ ET
BT 54.750 364.679 Td /F1 9.8 Tf [(‘reducibility’ of the global pattern exhibited by the CA. Here, Wolfram’s \(2002\) 4 complexity regimes are illuminating and )] TJ ET
BT 54.750 352.774 Td /F1 9.8 Tf [(can provide useful qualitative discriminators for the study of organizational phenomena. If, for instance, the global )] TJ ET
BT 54.750 340.869 Td /F1 9.8 Tf [(informational depth of a CA pattern is the pattern itself, then the pattern is incompressible. There is no ‘short-cut’ to )] TJ ET
BT 54.750 328.965 Td /F1 9.8 Tf [(producing the pattern, other than the iterative application of the local rule sets that produced it \(regime 4\) to the )] TJ ET
BT 54.750 317.060 Td /F1 9.8 Tf [(particular initial conditions of the CA. \(Different initial conditions will produce different end results at the macroscopic )] TJ ET
BT 54.750 305.155 Td /F1 9.8 Tf [(level. Thus, regime 4 behavior has something in common with dynamical regimes characterized by sensitive )] TJ ET
BT 54.750 293.250 Td /F1 9.8 Tf [(dependence on initial conditions, such as chaotic dynamical systems\). )] TJ ET
BT 359.837 293.250 Td /F5 9.8 Tf [(Organizational example)] TJ ET
BT 462.251 293.250 Td /F1 9.8 Tf [(: speculative bubbles )] TJ ET
BT 54.750 281.346 Td /F1 9.8 Tf [(\(positive feedbacks in trading behavior which are not based on underlying, inter-subjectively agreeable ‘facts of the )] TJ ET
BT 54.750 269.441 Td /F1 9.8 Tf [(matter’\) are structures or patterns that may or may not occur at particular space-time instances, depending on the kinds )] TJ ET
BT 54.750 257.536 Td /F1 9.8 Tf [(of information \(i.e., initial conditions\) that conditions the actions of the various traders. If, on the other hand, a )] TJ ET
BT 54.750 245.631 Td /F1 9.8 Tf [(macroscopic pattern is easily discerned and emerges from )] TJ ET
BT 309.420 245.631 Td /F5 9.8 Tf [(any)] TJ ET
BT 325.137 245.631 Td /F1 9.8 Tf [( initial conditions, then the CA model can be reduced to )] TJ ET
BT 54.750 233.727 Td /F1 9.8 Tf [(a set of rules that predict macroscopic evolution from microscopic rule systems without depending solely on the )] TJ ET
BT 54.750 221.822 Td /F1 9.8 Tf [(microscopic rule systems or varying with initial conditions \(regime 1\). )] TJ ET
BT 353.276 221.822 Td /F5 9.8 Tf [(Organizational example:)] TJ ET
BT 458.400 221.822 Td /F1 9.8 Tf [( Bertrand-Nash or )] TJ ET
BT 54.750 209.917 Td /F1 9.8 Tf [(Cournot-Nash equilibria, when they accurately represent oligopolistic behavior, exemplify the convergence of prices, )] TJ ET
BT 54.750 198.012 Td /F1 9.8 Tf [(from )] TJ ET
BT 76.960 198.012 Td /F5 9.8 Tf [(any)] TJ ET
BT 92.678 198.012 Td /F1 9.8 Tf [( initial conditions, to a set of prices that is predictable from knowledge of the producers’ cost functions. )] TJ ET
BT 54.750 186.108 Td /F1 9.8 Tf [(Wolfram’s ‘in-between’ regimes \(2 and 3\) are regimes in which there are macroscopic patterns \(thus they are )] TJ ET
BT 54.750 174.203 Td /F1 9.8 Tf [(compressible\) whose topological features \(regime 3\) or structural properties \(regime 2\) can be predicted via ‘short cuts’ )] TJ ET
BT 54.750 162.298 Td /F1 9.8 Tf [(from knowledge of the structure of the micro-rules via short-cuts \(intermediate rules\) whose implementation to simulate )] TJ ET
BT 54.750 150.393 Td /F1 9.8 Tf [(the macro-pattern is computationally lighter than the implementation of the micro-rules to simulate the same pattern. To )] TJ ET
BT 54.750 138.489 Td /F1 9.8 Tf [(use an intuitive organizational example for regime 2: Oscillatory behavior \(of under-supply/over-supply\) in feedback-)] TJ ET
BT 54.750 126.584 Td /F1 9.8 Tf [(regulated production systems \(Sterman, 2000\) is regular macroscopic behavior determined by microscopic rules of )] TJ ET
BT 54.750 114.679 Td /F1 9.8 Tf [(‘local rationality’ that governs the behavior of individual agents; and here is one for regime 3: the topology of inter-)] TJ ET
BT 54.750 102.774 Td /F1 9.8 Tf [(organizational networks \(Moldoveanu )] TJ ET
BT 218.940 102.774 Td /F5 9.8 Tf [(et al)] TJ ET
BT 237.368 102.774 Td /F1 9.8 Tf [(., 2003\) exhibits certain consistent regularities in situations in which network )] TJ ET
BT 54.750 90.870 Td /F1 9.8 Tf [(agents follow certain \(micro-locally well-defined\) strategies for sharing or withholding information, even though the )] TJ ET
BT 54.750 78.965 Td /F1 9.8 Tf [(precise structure of these networks \(i.e., the positions, in the network lattice, where these topological structures will )] TJ ET
BT 54.750 67.060 Td /F1 9.8 Tf [(occur\) cannot be specified in advance, as they sensitively depend on initial conditions. One may know that a particular )] TJ ET
BT 54.750 55.155 Td /F1 9.8 Tf [(network will evolve towards a large set of ‘center-periphery sub-networks, without being able to figure out where, )] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(precisely, each particular sub-network will emerge.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Fundamental trade-offs in the design of organizations and the pursuit of ‘organization science’)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(We are now in a position to discuss the essential tradeoffs that both the organizational designer \(such as a top manager or top management team\) and the organizational modeler \(the researcher\) make in designing micro-rules and micro-models to control, explain, predict or influence macro-level organizational phenomena. To connect the discussion to familiar terms and concepts—even though, we note the need for a fundamentally ‘new’ language for describing organizations which emerges if the premises of this paper are followed up on—we follow Cohen and March \(1972\) and posit three fundamental problems that the organizational designer or modeler must seek resolve as part of their tasks: the problem of conflict among local rules and rule sets; the problem of ambiguity—of representing a new phenomenon or signal and synthesizing a set of actionable or intelligible micro-level explanations for it; and the problem of uncertainty—that of narrowing down a set of possible worlds that are causally connected to the actual world to a smaller set of plausible worlds, ordered according to the plausibilities of these worlds.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The problem of conflict: Increasing the complexity of local rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Conflict among micro-local rules is usually resolved by introducing synthetic, ‘new’ rules, based on contingency clauses. Consider two simple ‘if-then-else’ rules, such as: ‘if a superior issues a command, then obey it \(to facilitate coordination\)’ and ‘if a superior issues a command, then question it \(to facilitate validation of information\)’. The two rules are not compatible on their face, and indicate 2 different action sequences \(changes in the micro-states of a CA element\) in response to the same ostensible signal \(an order from a superior\). This conflict may be directly experienced by the individual member of the organization to whom they supposedly apply, or it may be experienced by those, higher up in the hierarchy, who can see the conflict and have the lucidity to conceptualize it as a conflict. It can be resolved by issuing a synthetic and more complex rule, which says, “if a superior utters a command, then subject to a process of inquiry, follow the process, and, if the process comes out either uncertain or positive, then follow the order; else, question the command.” The rule is more complex—and may become more complex still as one begins to ponder the various processes of inquiry to which an individual agent may subject the order from the superior. In that case, we may have developments of the rule which specify norms of epistemic and discoursive rationality to which any such order must be made to answer, which will conform to a set of rules about the ways in which evidence is to be presented, the way it should be made to count, and so forth. Thus, conflict resolution at the organizational level comes at the expense of increasing the complexity of the micro-local rule sets. Here, again, there is a further distinction to be made:)] TJ ET
0.965 0.965 0.965 rg
0.000 792.000 0.000 0.000 re f
0.267 0.267 0.267 rg
0.000 792.000 m 0.000 792.000 l 0.000 791.250 l 0.000 791.250 l f
0.000 792.000 m 0.000 792.000 l 0.000 792.750 l 0.000 792.750 l f
0.271 0.267 0.267 rg
BT -8.132 782.494 Td /F1 9.8 Tf [(1.)] TJ ET
BT 0.000 782.981 Td /F4 9.8 Tf [(Increases in informational depth of the local rule sets)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. One can effect an increase in the informational depth of the local rule sets, usually by increasing the number of different ‘but for’ or )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(classes of exceptions)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( to the rule. This move can be modeled by increasing the number of neighbors of an element in a CA model on whose states the immediate future state of the said element depends, or, an increase in the number possible states of each CA element \(or, both together\). Such an increase in local informational depth corresponds—in the case of the organizational designer—to taxing the short-term-memory \(or ‘working memory’\) of each individual agent. For the organizational modeler, this move amounts to assigning a larger ‘working memory’ on the part of the individual agent. Such increases can be traded off against:)] TJ ET
BT -8.132 782.494 Td /F1 9.8 Tf [(2.)] TJ ET
BT 0.000 782.981 Td /F4 9.8 Tf [(Increases in computational load of the local rule sets)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. A key insight that comes out of a large scale study of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(CA)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( systems is that the behavior of CAs bound by )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(N)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(-state rules can be emulated by the behavior of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(CA)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(s bound by \()] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(N-l)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(\)-state rules that are )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(computationally deeper)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. One can compensate for ‘simpler’ agents—in the informational sense—by increasing the computational complexity of the interaction rules. \(Of course, the precise characteristics of this trade-off are not, in general, well understood, and require significant research\). This suggests that the organizational designer \(or modeler\) can substitute computational load \(‘what agents do’\) for informational depth \(‘what agents think’\)of the local rule sets. The organizational designer can design rule systems that makes individual agents ‘think more, but interact less’, as they would have to interact in order to exchange timely information about their states at any given time. The organizational modeler can develop models of organizational agents that are more computationally adept \(as in rational choice and game theoretic models\) but more interactionally and imaginatively inept \(as in the very same kinds of models\).)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The problem of ambiguity: Increasing the complexity of synthesizable macroscopic patterns)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The problem of ambiguity refers to the problem of categorizing emergent macro-level organizational phenomena—of representing those phenomena in ways that makes them either intelligible to organizational agents acting according to simple rule sets or that makes them synthesizable \(in the case of the organizational modeler\) from a set of agent-level models. The problem of ambiguity is usually resolved by creating micro rules that can synthesize the largest possible number of macroscopic-level patterns—because ‘understanding’ a macro-level pattern entails the ability to synthesize it from a set of micro-level rules, if we take ‘understnding’ to be synonymous with ‘valid explanation’. Thus, universal interaction rules may come to be favored over non-universal rules. Moreover, regime-3 and regime-4-producing rules will come to be favored over regime-1 and regime-2-producing rules \(as the set of macro-level patterns that can be synthesized under regimes 3 and 4 is far greater than that which can be synthesized with regime-1 and regime-2-producing rules. However, no sooner has the organizational designer, or organizational modeler, dealt with the problem of ambiguity than he or she is confronted with:)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The problem of uncertainty: decreasing the \(informational and computational\) complexity of macroscopic patterns)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The problem of uncertainty refers to narrowing down the set of possible macro-worlds that can be produced from a set of interacting micro-worlds. Macroscopic complexity refers to the relative compressibility of the macroscopic pattern. An intelligible organizational pattern—one that does not produce a lot of uncertainty—is one that can be simulated without running through the entire set of calculations which the CA had to perform in order to get to the answer. Equilibrium models of consumer behavior, game-theoretic models of competitive behavior among oligopolists, agency-theoretic models of owner-manager interactions in firms are all models that work by reproducing essential features of macro-level behavior by starting from caricatural models of agent behavior, and positing a ‘short-cut’ \(Nash equilibrium, general equilibrium, ‘perfect markets’\) to the prediction of a global pattern. An intelligible organizational pattern is also one that does not sensitively depend on the initial conditions of the CA. General equilibrium models, for instance, do not rely, for their predictions, on detailed knowledge of the preferences and personal histories of the agents engaged in exchange with one another. Thus, regime-1- and regime-2-producing CA micro-rules will come to be favored over regime-3 and regime-4-producing micro-rules by organizational designers and modelers bent on reducing uncertainty. Our analysis thus highlights the fact that there is a trade-off \(and not a confluence\) between measures aimed at reducing uncertainty and measured aimed at reducing ambiguity.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(In sum, CA models can be used to produce a ‘design landscape’ or ‘design space’ that allows both modelers and designers of organizations to make trade-offs in their choices of interaction rules and CA element models. The reduction of conflict appears to come at the expense of an increase in global uncertainty \(due to more complex—informationally deep or computationally heavy—local rule sets\). Increasing local complexity can be accomplished in one of two ways. Increasing local informational depth can be traded off against increasing local computational load—amounting to a trade-off between computational prowess and storage/access prowess at the level of the rule-following agent. The resolution of ambiguity and the mitigation of uncertainty can be understood as embodying yet another trade-off—between increasing the contingency and complexity of global patterns that are deterministically synthesizable from local rules, and decreasing the contingency and complexity of global patterns that are deterministically synthesizable from local rules. It is likely, therefore, that ambiguity-reducing measures will increase uncertainty \(which would also be increased by conflict-reducing measures\); and vice-versa.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Models of rule interactions for complex organizational systems)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The language of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(CA)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( models allows us to talk about the evolution of the semantic and syntactic content of rules in organizations, by focusing on the fundamental trade-offs that the rule designer must make. If organizations solve—by the adoption of rules and rule systems—the problems of ambiguity, conflict and uncertainty—then it is possible, as we have seen to quantify the fundamental trade-offs at the local level \(between informational depth and computational load of micro-rules\) and at the global level \(between the need for comprehensiveness of a particular pattern, which speaks to the problem of ambiguity resolution, and the need for simplicity \(informational and computational\) of that pattern, which speaks to the problem of uncertainty mitigation\). But, to make the application of CA models to the modeling of organizational phenomena persuasive, we should incorporate the reflexive nature of social rules and social systems. That is, simple, rule-driven CA models, if worth their salt )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(qua)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( explanation-generating engines, should accommodate the reflexive adaptation of rules to changing conditions or to the recognition of inefficiencies or faults with the existing rule systems.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(There is, of course, no reason to hope that a rule-bound system will be able to model itself fully \(Hofstadter, 1981\), prove the validity of its own core rules \(Putnam, 1985\) or provide computability or provability conditions for an arbitrarily large number of propositions \(Gödel, 1931; Nagel & Newman, 1958 for a pedagogical exposition of Gödel’s well-known result\). These conundrums of reflexivity cannot be resolved—except by meta-logical means.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(But, not being to resolve undecidable problems that arise in second-order logic does not mean that adaptations of rule systems cannot be modeled and understood in the simple language of first-order rules. Examples of such successful modeling enterprises include the use of genetic algorithms to model organizational learning \(Bruderer & Singh, 1996; Moldoveanu & Singh, 2003\). Reflexivity in this case manifests itself as competition between ideas and behaviors at the organizational level, coupled with a decision criterion that selects surviving beliefs and behaviors. Thus, there is ample reason to hope that a general framework for modeling rules can help us understand the evolution of rule systems as a result of \(possibly incomplete\) reflexive or meta-logical operations, and shed light on the important ‘phase change’ from decidable to undecidable problems, which are so common in second order logic, and, by inference, in social systems that are capable of modifying their behavior in response to an awareness of the causes, consequences or symbolism of that behavior.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Interactions between Rules and Meta-Rules: The Phenomenon of Irreducibility)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Meta-rules can e modeled as rules that govern changes in rules. For instance, ‘always seek an informationally constrained adaptation in response to a local rule conflict’ is an example of a meta-rule that constrains an organization’s adaptation to micro-local rule conflict. Meta-rules can be thought of as learning rules, or, perhaps more precisely, as rules for learning. If learning \(at the level of either the individual or the organization\) is about the \(inductive or abductive\) “discovery” of regularities in both the environment and the organization’s response to the environment and the adaptive modification of rule sets for the optimal exploitation of such regularities, then ‘learning algorithms’ in general are also examples of meta-rules.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(If we understand the organization as an instantiation of a set of micro-local rules and rule sets that produce—when combined together and combined with arbitrary initial conditions \(representing environmental inputs\)—a large-scale pattern, then ‘organizational learning’ is about the ability to predict what that pattern shall be in the most efficient possible way. It is about producing short cuts, or rules that ‘cut through’ the amount of computation and information required to predict the global behavioral pattern of the organization, starting from knowledge of the local rules and environmental inputs.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(But organizational learning is not merely about the production of insight about patterns \(Weick, 1991\), but also about the production of adaptive behavior that is causally linked to such insights. Thus ‘short cuts’ and predictively powerful rules of thumb will themselves come to substitute existing micro-local rules. The CA that models the continuously learning organization can be understood as a ‘self-compression’ engine, driving toward patterns that are less and less compressible over time. What is the limiting point of this process? It is precisely the set of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(computationally irreducible CA rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(—the very rules that govern CA patterns that cannot be ‘short cut’ by another set of rules, because they provide the quickest route to generating a large scale pattern from knowledge of themselves and the initial conditions for the CA. Thus, ‘learning’ \(in the precise sense in which we have defined it here\) drives rule-bound systems towards computational irreducibility. This also means, incidentally, that continuously learning organizations will exhibit phenomena that are )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(decreasingly predictable)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( by models which use ‘short cuts’—i.e., precisely the models that researchers use in order to explain organizational phenomena, or, more simply, that )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(learning organizations evolve toward unpredictability)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Interactions between rules and para-rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Para-rules are rules for resolving micro-local rule conflicts. They serve as ‘tie-breakers’ in situations of ‘conflict of laws’. Synthetic resolutions \(resolutions that incorporate elements of both conflicting rules and rule sets\) are likely to be more complex \(computationally, informationally, or both together\) than the rules that they effectively replace—as we have seen. If para-rules replace the rules they over-rule and themselves become part of the micro-local rule set, then we might expect an increase in micro-local rule complexity in response to events that cause micro-local rule conflicts \(such as organizational mergers, or strategic re-focusing of the organization\).)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Interactions between rules and ortho-rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Ortho-rules are rules that specify the domain of applicability of rules. They specify the situations under which it is sensible, for instance, to think of a particular rule as applicable, or to assert a command that is based on that rule. Ortho-rules thus can be thought to mediate between rules and the behaviors that rules proscribe or prescribe. Paradigm shifts \(Kuhn, 1962; 1990\) in the environment are situations in which the basic ontology in terms of which propositions and beliefs are advanced in the organization is challenged. The objects to which rules refer become diffuse and ambiguous. Ortho-rules become necessary for creating new distinctions, and thus for helping agents within the organization differentiate between various environmental signals. Once again, there will be an attending increase in the complexity of the micro-local rule sets:)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Discussion: Open questions and opportunities for inquiry)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Reducing organizational phenomena to macro-patterns that can be seen as instantiations of computations carried out by rule-following micro-agents leaves us in a position to inquire into the dynamics of the processes by which such phenomena are modeled: it allows us to also model the processes by which organizational researchers seek to explain and predict organizational phenomena of interest. Models are themselves rule-bound entities. They often proceed from micro-analytic assumptions about human behavior, cognition and motivation to supply explanations of organizational behaviors, or behaviors of aggregates of individual agents. Such models are computational entities—it is not merely the case that they )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(can be conceptualized as computational entities)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. Thus, it makes sense to ask: what does the reducibility of organizational phenomena to generalized CA models tell us about the enterprise of modeling organizational phenomena? Here, we can only offer preliminary remarks, which await future development, and can be roughly grouped as follows:)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Observing the observers of the observers when all observers are rule-bound: How do explanations explain?)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(What happens when a model ‘models’ an organizational phenomenon? To recognize it as the model of the phenomenon it \(putatively\) models, we must be able to observe some informational compression: the model should be informationally simpler \(though it can be computationally more complex\) than a ‘mere’ description of the phenomenon. Moreover, a model ‘models’ by allowing the modeler to make at least some valid predictions of future states of that phenomenon. It functions as a prism or lens for seeing forward through time \(or through the time axis of a particular entity\), linking past observables to future observables in a predictable fashion.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(It would be interesting to discover the relative frequency with which we can expect to come across predictively valid models \(rather than models based on illusory correlations\), and the language of CA does now make it possible to discover relative frequencies \(by simulating organizational processes and also processes that model organizational processes based on various plausible conjectured patterns, and monitoring the ‘hit rate’ for these patterns—the frequency with which they successfully predict future behavior in the system that they model\). Thus, we can begin to develop ‘ignorance metrics’ for various classes of organizational processes—metrics of the a priori likelihood that we will attribute an explanatory success to an illusory correlation rather than a ‘valid model’.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The relationship between computational experiments and ‘normal’ organization science: The pursuit of rule selection and rule change through computational experiments, and the problem of organizational design)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Is there a ‘new organization science’ that emerges from projects such as Wolfram’s ‘new kind of science? ‘Normal’ organization science—to the extent that anyone would self-consciously admit to engaging in such an exercise \(perhaps ‘theory-driven organization science’ would be a better term\)—consists in \(a\) the specification of a ‘theory’ or ‘model’ \(‘rational choice theory’, for instance; or, ‘game-theoretic models’ or ‘institutional analysis’; or, models drawn from ‘conflict sociology’\); \(b\) the derivation of hypotheses to be tested against observation statements; \(c\) the validation of the said hypotheses by comparison with the \(potentially falsifying\) observation statements, and; \(d\) the modification of the hypotheses, the theory or the set of \(extra-theoretical\) ‘background assumptions’ in line with the results of the empirical tests.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Wolfram \(2002\) is correct in arguing that such ‘theory-driven’ scientific endeavor runs the risk of turning into a dogma-preserving exercise, by the following mechanism: once we allow theory and model to drive the process of looking for, conceptualizing and gathering data, putting the data into the form of observation statements and deciding which among the data sets ‘count’, there is a lot of phenomenology \(which does not get addressed or has no ontological basis in the theory that we start with\) that will simply ‘escape’ the modeler. In organization science, where the ambiguity and value-ladenness of theories and complexity of the phenomena is notorious, this effect can only be amplified.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The ‘alternative’ or ‘new’ science that emerges from the systematic, reductionist study of generalized patterns \(which is, in fact, what the CA modeler does\) is one which attempts to solve the \(usually much more complicated\) inverse problem: given a macroscopic pattern \(an ‘organizational phenomenon’\) what is the simplest valid way of understanding it \(predicting its future course, intervening successfully in its evolution\)? The steps here are very different than they are in the case of ‘normal science: data drives the process. One \(a\) starts with a general macroscopic pattern and \(b\) a set of ontologically plausible micro-agents \(people, sensory, motor and/or computational neural centers in the human brain, organizational activity and routine sets\) and posits \(c\) a set of plausible interaction mechanisms among these entities, which together, will reproduce the macroscopic pattern, barring which \(d\) one adjusts the ‘search space’ by modifying either the entities or the interaction mechanisms. Moreover, one also \(e\) studies, using numerical experiments, the various micro-local processes by which the interacting micro-entities produce macroscopic behavior. In such cases one starts from ontologically plausible micro-agents and empirically plausible \(and testable\) micro-interaction rules to ‘look into the future’ of the macro-entity in question.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Arguably, a such ‘new’ organization science is significantly more challenging than that which is currently practiced, but it has the benefit that it places the phenomena squarely in the foreground \(rather than leaving them as potential justifications for using a particular kind of theory\) and thus does not limit the horizon of the researcher to what we \(always-already?\) knew could be explained by the chosen model, theory or ‘framework’. ‘Challenging’ is meant to subsume both computational and non-computational difficulties. It is genuinely ‘harder’ in a computational sense to do data-driven organization science, and attempt to recover \(through numerical experimentation\) the micro-analytic rule patterns that produce a particular data pattern, than it is to start from a ‘well-grounded’ macro-analytic theory, produce a set of hypotheses by simple deductive steps, then search for the data that best exemplifies the theory in question \(i.e offers the most plausible prima facie testing ground for it\) in order to validate the derived hypotheses. It is also genuinely harder \(in an ontological sense\) to look for the right micro-analytic set of agents and interaction patterns that explain a particular macroscopic behavior, than it is to assume that the inherited theory has already latched onto the right ontological commitments. The ‘payoff’ for these greater \(and often sunk\) costs incurred by the researcher will be a science of organizations in which the explanandum \(the phenomenon\) will be far less determined by the choice of the explanans \(the theory\), and thus fewer phenomena will escape through the sparse ‘netting’ of a parsimonious theory.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(References)] TJ ET
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BT 0.000 782.981 Td /F1 9.8 Tf [(Lewis, D. \(1969\). Convention: A Philosophical Study, ISBN 9780674170254.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Li, Y.-F. and Vitanyi, M. \(1993\). An Introduction to Kolmogorov Complexity and Its Applications, ISBN 9780387948683 \(1997\).)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Lomi, A. and E. Larsen \(2001\). Dynamics of Organizations: Computational Modeling and Organization Theories, ISBN 9780262621526.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(March, J.G. \(1991\). “Exploration and exploitation in organizational learning,” Organization Science, ISSN 1047-7039, 2:71-87.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(March, J.G. \(1994\). A Primer on Decision Making, ISBN 9780029200353.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(March, J.G. and Olsen, J.P. \(1976\). Ambiguity and Choice in Organizations, ISBN 9788200014782.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(March, J.G. and Simon, H.A. \(1958\). Organizations, ISBN 9780471567936.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(March, J.G., Schulz, M. and Zhou, X. \(2000\). The Dynamics of Rules: Change in Written organizational Codes, ISBN 9780804739962.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(McKelvey, B. \(1999\). “Avoiding complexity catastrophe in co-evolutionary pockets,” Organization Science, ISSN 1526-5455, 10: 343-356.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. \(1999\). “Reasoning about choices and choosing among reasons,” Proceedings of the 3rd International Conference on Complex Systems, Nashua, New Hampshire.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. \(2002\). “Epistemology in action: A framework for understanding organizational due diligence processes,” in C.W. Choo and N. Bontis \(eds.\), The Strategic Management of Intellectual Capital and Organizational Knowledge: A Collection of Readings, ISBN 9780195138665.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. \(2006\). “Thinking strategically about thinking strategically: On the computational structure and dynamics of managerial cognition,” Working Paper No. 06-03, Rotman School of Management, University of Toronto, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=901443.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. and Bauer, R. \(2000\). “Circumspective rationality: The logic of problem-choice in strategic management,” Proceedings of 16th IAREP/SABE Conference, Baden, Austria.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. and Bauer, R. \(2002\). “What is organizational complexity and what difference does organizational complexity make? Academy of Management Proceedings, Washington, D.C.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. and Bauer, R. \(2004\). “On the relationship between organizational complexity and organizational dynamics,” Organization Science, ISSN 1526-5455, 15\(1\): 98-118.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. and Nohria, N. \(2002\). Master Passions: Emotions, Narrative and the Development of Culture, ISBN 9780262134057.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. and Singh, J. \(2003\). “The evolutionary metaphor: A synthetic framework for the study of strategic organization,” Strategic Organi-zation, ISSN 1476-1270, 1\(4\):439-449.)] TJ ET
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BT 0.000 782.981 Td /F1 9.8 Tf [(Schelling, T. \(1960\). The Strategy of Conflict, ISBN 9780674840300.)] TJ ET
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BT 0.000 782.981 Td /F1 9.8 Tf [(Simon, H.A. \(1973\). “The structure of ill-structured problems,” Artificial Intelligence, ISSN 0004-3702,4\(3\):181-200.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Simon, H.A. \(1992\). “What is an explanation of behavior?” Psychological Science, ISSN 0956-7976, 3\(3\):150-161.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Simon, H.A. \(1996\). The Sciences of the Artificial, ISBN 9780262691918.)] TJ ET
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BT 0.000 782.981 Td /F1 9.8 Tf [(Weick, K.E. \(1995\). Sense-Making in Organizations, ISBN 9780803971776.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Wittgenstein, L. \(1953\). Philosophical Investigations, ISBN 9780631146704.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Wolfram, S. \(1983\). “The statistical mechanics of cellular automata,” Reviews of Modern Physics, ISSN 0034-6861, 43\(3\): 601-644.)] TJ ET
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Q
q
15.000 -383.118 577.500 1160.118 re W n
0.965 0.965 0.965 rg
26.250 -375.618 555.000 1152.618 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 -375.618 m 581.250 -375.618 l 581.250 -374.868 l 26.250 -374.868 l f
0.271 0.267 0.267 rg
BT 41.206 767.494 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 767.476 Td /F4 9.8 Tf [(The informational or algorithmic complexity of the local rule sets)] TJ ET
BT 354.913 767.476 Td /F1 9.8 Tf [(. This quantity \(which we shall refer to as )] TJ ET
BT 54.750 755.571 Td /F5 9.8 Tf [(local informational depth)] TJ ET
BT 160.421 755.571 Td /F1 9.8 Tf [(\) is the Kolmogorov complexity \(see Li & Vitanyi, 1993\) of the local CA rules, and can be )] TJ ET
BT 54.750 743.667 Td /F1 9.8 Tf [(defined as the length \(in bits or M-ary units of information\) of the minimum-length algorithm that can execute these )] TJ ET
BT 54.750 731.762 Td /F1 9.8 Tf [(rules. It is a measure of local ‘random access memory’—or short term memory required by an element of a CA to act )] TJ ET
BT 54.750 719.857 Td /F1 9.8 Tf [(according to ‘its’ local rule system: if the agent’s memory is not capable of supporting at least the number of states that )] TJ ET
BT 54.750 707.952 Td /F1 9.8 Tf [(the rule system that governs its local interactions refers to, then it will not be able to ‘sustain’ local interactions )] TJ ET
BT 54.750 696.048 Td /F1 9.8 Tf [(governed by that rule. If the CA element is an individual agent, then the local informational depth of the rules of the CA )] TJ ET
BT 54.750 684.143 Td /F1 9.8 Tf [(measures the short-term memory required in order for the agent to ‘g)] TJ ET
BT 351.696 684.143 Td /F5 9.8 Tf [(et al)] TJ ET
BT 370.124 684.143 Td /F1 9.8 Tf [(ong’ with other agents.)] TJ ET
BT 41.206 661.006 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 660.988 Td /F4 9.8 Tf [(The computational complexity of the local rule sets)] TJ ET
BT 292.065 660.988 Td /F1 9.8 Tf [(. This quantity measures the number of calculations required at )] TJ ET
BT 54.750 649.083 Td /F1 9.8 Tf [(the level of each CA element for the implementation of the local CA rules, as a function of the number of CA neighbors )] TJ ET
BT 54.750 637.179 Td /F1 9.8 Tf [(on which the state of the original CA element depends, and the number of possible states of the neighboring elements )] TJ ET
BT 54.750 625.274 Td /F1 9.8 Tf [(on which the state of each CA element depends. We shall refer to this quantity as the )] TJ ET
BT 424.899 625.274 Td /F5 9.8 Tf [(local computational load)] TJ ET
BT 529.487 625.274 Td /F1 9.8 Tf [( of the )] TJ ET
BT 54.750 613.369 Td /F1 9.8 Tf [(CA model \(Moldoveanu & Bauer, 2003; see Cormen )] TJ ET
BT 282.890 613.369 Td /F5 9.8 Tf [(et al)] TJ ET
BT 301.318 613.369 Td /F1 9.8 Tf [(., 1993 for a pedagogical introduction to computational )] TJ ET
BT 54.750 601.464 Td /F1 9.8 Tf [(complexity for algorithmic problems.For a 2-state CA model \(each element can take on the value of ‘0’ or ‘1’\) in which )] TJ ET
BT 54.750 589.560 Td /F1 9.8 Tf [(the state of each element depends linearly on the individual states of each of K neighbors, the local computational load )] TJ ET
BT 54.750 577.655 Td /F1 9.8 Tf [(for each CA element is bounded from above by 2K. Nonlinear dependencies of the state of one CA element on )] TJ ET
BT 54.750 565.750 Td /F1 9.8 Tf [(\(including ‘memory effects’\) will increase the number of operations required locally to compute the next state of the CA )] TJ ET
BT 54.750 553.845 Td /F1 9.8 Tf [(element from knowledge of the states of the neighboring elements. If each CA element is taken to model an agent \(in a )] TJ ET
BT 54.750 541.941 Td /F1 9.8 Tf [(multi-agent model of an organization\), then local computational load measures the computational power required of an )] TJ ET
BT 54.750 530.036 Td /F1 9.8 Tf [(agent to successfully interact with other agents according to the local rule sets.)] TJ ET
BT 41.206 506.899 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 506.881 Td /F4 9.8 Tf [(The global computational complexity of the resulting CA pattern produced by the iterative application of a set )] TJ ET
BT 54.750 494.976 Td /F4 9.8 Tf [(of micro-local rules or rule sets)] TJ ET
BT 199.966 494.976 Td /F1 9.8 Tf [(. This quantity, which is defined as a function of the total number of variables \()] TJ ET
BT 54.750 483.072 Td /F5 9.8 Tf [(CA elements x states\))] TJ ET
BT 150.115 483.072 Td /F1 9.8 Tf [( that determine the evolution of local CA elements, measures how ‘difficult’ it is for a particular )] TJ ET
BT 54.750 471.167 Td /F1 9.8 Tf [(micro-rule set to replicate a particular large-scale pattern of the CA. It is a measure of the computational complexity of )] TJ ET
BT 54.750 459.262 Td /F1 9.8 Tf [(producing a simulation of an organizational phenomenon starting from a set of agent-level models \(such as rational )] TJ ET
BT 54.750 447.357 Td /F1 9.8 Tf [(choice models or game-theoretic models\). This quantity measures the relative efficiency of using micro-rule systems )] TJ ET
BT 54.750 435.453 Td /F1 9.8 Tf [(\(organizational ‘common law’ and statutes is often written in terms of such rule systems\) in order to predict the global )] TJ ET
BT 54.750 423.548 Td /F1 9.8 Tf [(evolution of the organization. It also measures the relative difficulty for the modeler of explaining an organizational )] TJ ET
BT 54.750 411.643 Td /F1 9.8 Tf [(phenomenon based on a metaphysical commitment to methodological individualism \(i.e., to the explanation of social )] TJ ET
BT 54.750 399.738 Td /F1 9.8 Tf [(phenomena by reduction to individual-level phenomena\) and a set of models of individual behavior.)] TJ ET
BT 41.206 376.601 Td /F1 9.8 Tf [(4.)] TJ ET
BT 54.750 376.584 Td /F4 9.8 Tf [(The global informational depth of the resulting CA pattern.)] TJ ET
BT 325.615 376.584 Td /F1 9.8 Tf [( It is a measure of the relative ‘compressibility’ or )] TJ ET
BT 54.750 364.679 Td /F1 9.8 Tf [(‘reducibility’ of the global pattern exhibited by the CA. Here, Wolfram’s \(2002\) 4 complexity regimes are illuminating and )] TJ ET
BT 54.750 352.774 Td /F1 9.8 Tf [(can provide useful qualitative discriminators for the study of organizational phenomena. If, for instance, the global )] TJ ET
BT 54.750 340.869 Td /F1 9.8 Tf [(informational depth of a CA pattern is the pattern itself, then the pattern is incompressible. There is no ‘short-cut’ to )] TJ ET
BT 54.750 328.965 Td /F1 9.8 Tf [(producing the pattern, other than the iterative application of the local rule sets that produced it \(regime 4\) to the )] TJ ET
BT 54.750 317.060 Td /F1 9.8 Tf [(particular initial conditions of the CA. \(Different initial conditions will produce different end results at the macroscopic )] TJ ET
BT 54.750 305.155 Td /F1 9.8 Tf [(level. Thus, regime 4 behavior has something in common with dynamical regimes characterized by sensitive )] TJ ET
BT 54.750 293.250 Td /F1 9.8 Tf [(dependence on initial conditions, such as chaotic dynamical systems\). )] TJ ET
BT 359.837 293.250 Td /F5 9.8 Tf [(Organizational example)] TJ ET
BT 462.251 293.250 Td /F1 9.8 Tf [(: speculative bubbles )] TJ ET
BT 54.750 281.346 Td /F1 9.8 Tf [(\(positive feedbacks in trading behavior which are not based on underlying, inter-subjectively agreeable ‘facts of the )] TJ ET
BT 54.750 269.441 Td /F1 9.8 Tf [(matter’\) are structures or patterns that may or may not occur at particular space-time instances, depending on the kinds )] TJ ET
BT 54.750 257.536 Td /F1 9.8 Tf [(of information \(i.e., initial conditions\) that conditions the actions of the various traders. If, on the other hand, a )] TJ ET
BT 54.750 245.631 Td /F1 9.8 Tf [(macroscopic pattern is easily discerned and emerges from )] TJ ET
BT 309.420 245.631 Td /F5 9.8 Tf [(any)] TJ ET
BT 325.137 245.631 Td /F1 9.8 Tf [( initial conditions, then the CA model can be reduced to )] TJ ET
BT 54.750 233.727 Td /F1 9.8 Tf [(a set of rules that predict macroscopic evolution from microscopic rule systems without depending solely on the )] TJ ET
BT 54.750 221.822 Td /F1 9.8 Tf [(microscopic rule systems or varying with initial conditions \(regime 1\). )] TJ ET
BT 353.276 221.822 Td /F5 9.8 Tf [(Organizational example:)] TJ ET
BT 458.400 221.822 Td /F1 9.8 Tf [( Bertrand-Nash or )] TJ ET
BT 54.750 209.917 Td /F1 9.8 Tf [(Cournot-Nash equilibria, when they accurately represent oligopolistic behavior, exemplify the convergence of prices, )] TJ ET
BT 54.750 198.012 Td /F1 9.8 Tf [(from )] TJ ET
BT 76.960 198.012 Td /F5 9.8 Tf [(any)] TJ ET
BT 92.678 198.012 Td /F1 9.8 Tf [( initial conditions, to a set of prices that is predictable from knowledge of the producers’ cost functions. )] TJ ET
BT 54.750 186.108 Td /F1 9.8 Tf [(Wolfram’s ‘in-between’ regimes \(2 and 3\) are regimes in which there are macroscopic patterns \(thus they are )] TJ ET
BT 54.750 174.203 Td /F1 9.8 Tf [(compressible\) whose topological features \(regime 3\) or structural properties \(regime 2\) can be predicted via ‘short cuts’ )] TJ ET
BT 54.750 162.298 Td /F1 9.8 Tf [(from knowledge of the structure of the micro-rules via short-cuts \(intermediate rules\) whose implementation to simulate )] TJ ET
BT 54.750 150.393 Td /F1 9.8 Tf [(the macro-pattern is computationally lighter than the implementation of the micro-rules to simulate the same pattern. To )] TJ ET
BT 54.750 138.489 Td /F1 9.8 Tf [(use an intuitive organizational example for regime 2: Oscillatory behavior \(of under-supply/over-supply\) in feedback-)] TJ ET
BT 54.750 126.584 Td /F1 9.8 Tf [(regulated production systems \(Sterman, 2000\) is regular macroscopic behavior determined by microscopic rules of )] TJ ET
BT 54.750 114.679 Td /F1 9.8 Tf [(‘local rationality’ that governs the behavior of individual agents; and here is one for regime 3: the topology of inter-)] TJ ET
BT 54.750 102.774 Td /F1 9.8 Tf [(organizational networks \(Moldoveanu )] TJ ET
BT 218.940 102.774 Td /F5 9.8 Tf [(et al)] TJ ET
BT 237.368 102.774 Td /F1 9.8 Tf [(., 2003\) exhibits certain consistent regularities in situations in which network )] TJ ET
BT 54.750 90.870 Td /F1 9.8 Tf [(agents follow certain \(micro-locally well-defined\) strategies for sharing or withholding information, even though the )] TJ ET
BT 54.750 78.965 Td /F1 9.8 Tf [(precise structure of these networks \(i.e., the positions, in the network lattice, where these topological structures will )] TJ ET
BT 54.750 67.060 Td /F1 9.8 Tf [(occur\) cannot be specified in advance, as they sensitively depend on initial conditions. One may know that a particular )] TJ ET
BT 54.750 55.155 Td /F1 9.8 Tf [(network will evolve towards a large set of ‘center-periphery sub-networks, without being able to figure out where, )] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(precisely, each particular sub-network will emerge.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Fundamental trade-offs in the design of organizations and the pursuit of ‘organization science’)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(We are now in a position to discuss the essential tradeoffs that both the organizational designer \(such as a top manager or top management team\) and the organizational modeler \(the researcher\) make in designing micro-rules and micro-models to control, explain, predict or influence macro-level organizational phenomena. To connect the discussion to familiar terms and concepts—even though, we note the need for a fundamentally ‘new’ language for describing organizations which emerges if the premises of this paper are followed up on—we follow Cohen and March \(1972\) and posit three fundamental problems that the organizational designer or modeler must seek resolve as part of their tasks: the problem of conflict among local rules and rule sets; the problem of ambiguity—of representing a new phenomenon or signal and synthesizing a set of actionable or intelligible micro-level explanations for it; and the problem of uncertainty—that of narrowing down a set of possible worlds that are causally connected to the actual world to a smaller set of plausible worlds, ordered according to the plausibilities of these worlds.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The problem of conflict: Increasing the complexity of local rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Conflict among micro-local rules is usually resolved by introducing synthetic, ‘new’ rules, based on contingency clauses. Consider two simple ‘if-then-else’ rules, such as: ‘if a superior issues a command, then obey it \(to facilitate coordination\)’ and ‘if a superior issues a command, then question it \(to facilitate validation of information\)’. The two rules are not compatible on their face, and indicate 2 different action sequences \(changes in the micro-states of a CA element\) in response to the same ostensible signal \(an order from a superior\). This conflict may be directly experienced by the individual member of the organization to whom they supposedly apply, or it may be experienced by those, higher up in the hierarchy, who can see the conflict and have the lucidity to conceptualize it as a conflict. It can be resolved by issuing a synthetic and more complex rule, which says, “if a superior utters a command, then subject to a process of inquiry, follow the process, and, if the process comes out either uncertain or positive, then follow the order; else, question the command.” The rule is more complex—and may become more complex still as one begins to ponder the various processes of inquiry to which an individual agent may subject the order from the superior. In that case, we may have developments of the rule which specify norms of epistemic and discoursive rationality to which any such order must be made to answer, which will conform to a set of rules about the ways in which evidence is to be presented, the way it should be made to count, and so forth. Thus, conflict resolution at the organizational level comes at the expense of increasing the complexity of the micro-local rule sets. Here, again, there is a further distinction to be made:)] TJ ET
0.965 0.965 0.965 rg
0.000 792.000 0.000 0.000 re f
0.267 0.267 0.267 rg
0.000 792.000 m 0.000 792.000 l 0.000 791.250 l 0.000 791.250 l f
0.000 792.000 m 0.000 792.000 l 0.000 792.750 l 0.000 792.750 l f
0.271 0.267 0.267 rg
BT -8.132 782.494 Td /F1 9.8 Tf [(1.)] TJ ET
BT 0.000 782.981 Td /F4 9.8 Tf [(Increases in informational depth of the local rule sets)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. One can effect an increase in the informational depth of the local rule sets, usually by increasing the number of different ‘but for’ or )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(classes of exceptions)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( to the rule. This move can be modeled by increasing the number of neighbors of an element in a CA model on whose states the immediate future state of the said element depends, or, an increase in the number possible states of each CA element \(or, both together\). Such an increase in local informational depth corresponds—in the case of the organizational designer—to taxing the short-term-memory \(or ‘working memory’\) of each individual agent. For the organizational modeler, this move amounts to assigning a larger ‘working memory’ on the part of the individual agent. Such increases can be traded off against:)] TJ ET
BT -8.132 782.494 Td /F1 9.8 Tf [(2.)] TJ ET
BT 0.000 782.981 Td /F4 9.8 Tf [(Increases in computational load of the local rule sets)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. A key insight that comes out of a large scale study of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(CA)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( systems is that the behavior of CAs bound by )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(N)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(-state rules can be emulated by the behavior of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(CA)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(s bound by \()] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(N-l)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(\)-state rules that are )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(computationally deeper)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. One can compensate for ‘simpler’ agents—in the informational sense—by increasing the computational complexity of the interaction rules. \(Of course, the precise characteristics of this trade-off are not, in general, well understood, and require significant research\). This suggests that the organizational designer \(or modeler\) can substitute computational load \(‘what agents do’\) for informational depth \(‘what agents think’\)of the local rule sets. The organizational designer can design rule systems that makes individual agents ‘think more, but interact less’, as they would have to interact in order to exchange timely information about their states at any given time. The organizational modeler can develop models of organizational agents that are more computationally adept \(as in rational choice and game theoretic models\) but more interactionally and imaginatively inept \(as in the very same kinds of models\).)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The problem of ambiguity: Increasing the complexity of synthesizable macroscopic patterns)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The problem of ambiguity refers to the problem of categorizing emergent macro-level organizational phenomena—of representing those phenomena in ways that makes them either intelligible to organizational agents acting according to simple rule sets or that makes them synthesizable \(in the case of the organizational modeler\) from a set of agent-level models. The problem of ambiguity is usually resolved by creating micro rules that can synthesize the largest possible number of macroscopic-level patterns—because ‘understanding’ a macro-level pattern entails the ability to synthesize it from a set of micro-level rules, if we take ‘understnding’ to be synonymous with ‘valid explanation’. Thus, universal interaction rules may come to be favored over non-universal rules. Moreover, regime-3 and regime-4-producing rules will come to be favored over regime-1 and regime-2-producing rules \(as the set of macro-level patterns that can be synthesized under regimes 3 and 4 is far greater than that which can be synthesized with regime-1 and regime-2-producing rules. However, no sooner has the organizational designer, or organizational modeler, dealt with the problem of ambiguity than he or she is confronted with:)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The problem of uncertainty: decreasing the \(informational and computational\) complexity of macroscopic patterns)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The problem of uncertainty refers to narrowing down the set of possible macro-worlds that can be produced from a set of interacting micro-worlds. Macroscopic complexity refers to the relative compressibility of the macroscopic pattern. An intelligible organizational pattern—one that does not produce a lot of uncertainty—is one that can be simulated without running through the entire set of calculations which the CA had to perform in order to get to the answer. Equilibrium models of consumer behavior, game-theoretic models of competitive behavior among oligopolists, agency-theoretic models of owner-manager interactions in firms are all models that work by reproducing essential features of macro-level behavior by starting from caricatural models of agent behavior, and positing a ‘short-cut’ \(Nash equilibrium, general equilibrium, ‘perfect markets’\) to the prediction of a global pattern. An intelligible organizational pattern is also one that does not sensitively depend on the initial conditions of the CA. General equilibrium models, for instance, do not rely, for their predictions, on detailed knowledge of the preferences and personal histories of the agents engaged in exchange with one another. Thus, regime-1- and regime-2-producing CA micro-rules will come to be favored over regime-3 and regime-4-producing micro-rules by organizational designers and modelers bent on reducing uncertainty. Our analysis thus highlights the fact that there is a trade-off \(and not a confluence\) between measures aimed at reducing uncertainty and measured aimed at reducing ambiguity.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(In sum, CA models can be used to produce a ‘design landscape’ or ‘design space’ that allows both modelers and designers of organizations to make trade-offs in their choices of interaction rules and CA element models. The reduction of conflict appears to come at the expense of an increase in global uncertainty \(due to more complex—informationally deep or computationally heavy—local rule sets\). Increasing local complexity can be accomplished in one of two ways. Increasing local informational depth can be traded off against increasing local computational load—amounting to a trade-off between computational prowess and storage/access prowess at the level of the rule-following agent. The resolution of ambiguity and the mitigation of uncertainty can be understood as embodying yet another trade-off—between increasing the contingency and complexity of global patterns that are deterministically synthesizable from local rules, and decreasing the contingency and complexity of global patterns that are deterministically synthesizable from local rules. It is likely, therefore, that ambiguity-reducing measures will increase uncertainty \(which would also be increased by conflict-reducing measures\); and vice-versa.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Models of rule interactions for complex organizational systems)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The language of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(CA)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( models allows us to talk about the evolution of the semantic and syntactic content of rules in organizations, by focusing on the fundamental trade-offs that the rule designer must make. If organizations solve—by the adoption of rules and rule systems—the problems of ambiguity, conflict and uncertainty—then it is possible, as we have seen to quantify the fundamental trade-offs at the local level \(between informational depth and computational load of micro-rules\) and at the global level \(between the need for comprehensiveness of a particular pattern, which speaks to the problem of ambiguity resolution, and the need for simplicity \(informational and computational\) of that pattern, which speaks to the problem of uncertainty mitigation\). But, to make the application of CA models to the modeling of organizational phenomena persuasive, we should incorporate the reflexive nature of social rules and social systems. That is, simple, rule-driven CA models, if worth their salt )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(qua)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( explanation-generating engines, should accommodate the reflexive adaptation of rules to changing conditions or to the recognition of inefficiencies or faults with the existing rule systems.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(There is, of course, no reason to hope that a rule-bound system will be able to model itself fully \(Hofstadter, 1981\), prove the validity of its own core rules \(Putnam, 1985\) or provide computability or provability conditions for an arbitrarily large number of propositions \(Gödel, 1931; Nagel & Newman, 1958 for a pedagogical exposition of Gödel’s well-known result\). These conundrums of reflexivity cannot be resolved—except by meta-logical means.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(But, not being to resolve undecidable problems that arise in second-order logic does not mean that adaptations of rule systems cannot be modeled and understood in the simple language of first-order rules. Examples of such successful modeling enterprises include the use of genetic algorithms to model organizational learning \(Bruderer & Singh, 1996; Moldoveanu & Singh, 2003\). Reflexivity in this case manifests itself as competition between ideas and behaviors at the organizational level, coupled with a decision criterion that selects surviving beliefs and behaviors. Thus, there is ample reason to hope that a general framework for modeling rules can help us understand the evolution of rule systems as a result of \(possibly incomplete\) reflexive or meta-logical operations, and shed light on the important ‘phase change’ from decidable to undecidable problems, which are so common in second order logic, and, by inference, in social systems that are capable of modifying their behavior in response to an awareness of the causes, consequences or symbolism of that behavior.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Interactions between Rules and Meta-Rules: The Phenomenon of Irreducibility)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Meta-rules can e modeled as rules that govern changes in rules. For instance, ‘always seek an informationally constrained adaptation in response to a local rule conflict’ is an example of a meta-rule that constrains an organization’s adaptation to micro-local rule conflict. Meta-rules can be thought of as learning rules, or, perhaps more precisely, as rules for learning. If learning \(at the level of either the individual or the organization\) is about the \(inductive or abductive\) “discovery” of regularities in both the environment and the organization’s response to the environment and the adaptive modification of rule sets for the optimal exploitation of such regularities, then ‘learning algorithms’ in general are also examples of meta-rules.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(If we understand the organization as an instantiation of a set of micro-local rules and rule sets that produce—when combined together and combined with arbitrary initial conditions \(representing environmental inputs\)—a large-scale pattern, then ‘organizational learning’ is about the ability to predict what that pattern shall be in the most efficient possible way. It is about producing short cuts, or rules that ‘cut through’ the amount of computation and information required to predict the global behavioral pattern of the organization, starting from knowledge of the local rules and environmental inputs.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(But organizational learning is not merely about the production of insight about patterns \(Weick, 1991\), but also about the production of adaptive behavior that is causally linked to such insights. Thus ‘short cuts’ and predictively powerful rules of thumb will themselves come to substitute existing micro-local rules. The CA that models the continuously learning organization can be understood as a ‘self-compression’ engine, driving toward patterns that are less and less compressible over time. What is the limiting point of this process? It is precisely the set of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(computationally irreducible CA rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(—the very rules that govern CA patterns that cannot be ‘short cut’ by another set of rules, because they provide the quickest route to generating a large scale pattern from knowledge of themselves and the initial conditions for the CA. Thus, ‘learning’ \(in the precise sense in which we have defined it here\) drives rule-bound systems towards computational irreducibility. This also means, incidentally, that continuously learning organizations will exhibit phenomena that are )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(decreasingly predictable)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( by models which use ‘short cuts’—i.e., precisely the models that researchers use in order to explain organizational phenomena, or, more simply, that )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(learning organizations evolve toward unpredictability)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Interactions between rules and para-rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Para-rules are rules for resolving micro-local rule conflicts. They serve as ‘tie-breakers’ in situations of ‘conflict of laws’. Synthetic resolutions \(resolutions that incorporate elements of both conflicting rules and rule sets\) are likely to be more complex \(computationally, informationally, or both together\) than the rules that they effectively replace—as we have seen. If para-rules replace the rules they over-rule and themselves become part of the micro-local rule set, then we might expect an increase in micro-local rule complexity in response to events that cause micro-local rule conflicts \(such as organizational mergers, or strategic re-focusing of the organization\).)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Interactions between rules and ortho-rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Ortho-rules are rules that specify the domain of applicability of rules. They specify the situations under which it is sensible, for instance, to think of a particular rule as applicable, or to assert a command that is based on that rule. Ortho-rules thus can be thought to mediate between rules and the behaviors that rules proscribe or prescribe. Paradigm shifts \(Kuhn, 1962; 1990\) in the environment are situations in which the basic ontology in terms of which propositions and beliefs are advanced in the organization is challenged. The objects to which rules refer become diffuse and ambiguous. Ortho-rules become necessary for creating new distinctions, and thus for helping agents within the organization differentiate between various environmental signals. Once again, there will be an attending increase in the complexity of the micro-local rule sets:)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Discussion: Open questions and opportunities for inquiry)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Reducing organizational phenomena to macro-patterns that can be seen as instantiations of computations carried out by rule-following micro-agents leaves us in a position to inquire into the dynamics of the processes by which such phenomena are modeled: it allows us to also model the processes by which organizational researchers seek to explain and predict organizational phenomena of interest. Models are themselves rule-bound entities. They often proceed from micro-analytic assumptions about human behavior, cognition and motivation to supply explanations of organizational behaviors, or behaviors of aggregates of individual agents. Such models are computational entities—it is not merely the case that they )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(can be conceptualized as computational entities)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. Thus, it makes sense to ask: what does the reducibility of organizational phenomena to generalized CA models tell us about the enterprise of modeling organizational phenomena? Here, we can only offer preliminary remarks, which await future development, and can be roughly grouped as follows:)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Observing the observers of the observers when all observers are rule-bound: How do explanations explain?)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(What happens when a model ‘models’ an organizational phenomenon? To recognize it as the model of the phenomenon it \(putatively\) models, we must be able to observe some informational compression: the model should be informationally simpler \(though it can be computationally more complex\) than a ‘mere’ description of the phenomenon. Moreover, a model ‘models’ by allowing the modeler to make at least some valid predictions of future states of that phenomenon. It functions as a prism or lens for seeing forward through time \(or through the time axis of a particular entity\), linking past observables to future observables in a predictable fashion.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(It would be interesting to discover the relative frequency with which we can expect to come across predictively valid models \(rather than models based on illusory correlations\), and the language of CA does now make it possible to discover relative frequencies \(by simulating organizational processes and also processes that model organizational processes based on various plausible conjectured patterns, and monitoring the ‘hit rate’ for these patterns—the frequency with which they successfully predict future behavior in the system that they model\). Thus, we can begin to develop ‘ignorance metrics’ for various classes of organizational processes—metrics of the a priori likelihood that we will attribute an explanatory success to an illusory correlation rather than a ‘valid model’.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The relationship between computational experiments and ‘normal’ organization science: The pursuit of rule selection and rule change through computational experiments, and the problem of organizational design)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Is there a ‘new organization science’ that emerges from projects such as Wolfram’s ‘new kind of science? ‘Normal’ organization science—to the extent that anyone would self-consciously admit to engaging in such an exercise \(perhaps ‘theory-driven organization science’ would be a better term\)—consists in \(a\) the specification of a ‘theory’ or ‘model’ \(‘rational choice theory’, for instance; or, ‘game-theoretic models’ or ‘institutional analysis’; or, models drawn from ‘conflict sociology’\); \(b\) the derivation of hypotheses to be tested against observation statements; \(c\) the validation of the said hypotheses by comparison with the \(potentially falsifying\) observation statements, and; \(d\) the modification of the hypotheses, the theory or the set of \(extra-theoretical\) ‘background assumptions’ in line with the results of the empirical tests.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Wolfram \(2002\) is correct in arguing that such ‘theory-driven’ scientific endeavor runs the risk of turning into a dogma-preserving exercise, by the following mechanism: once we allow theory and model to drive the process of looking for, conceptualizing and gathering data, putting the data into the form of observation statements and deciding which among the data sets ‘count’, there is a lot of phenomenology \(which does not get addressed or has no ontological basis in the theory that we start with\) that will simply ‘escape’ the modeler. In organization science, where the ambiguity and value-ladenness of theories and complexity of the phenomena is notorious, this effect can only be amplified.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The ‘alternative’ or ‘new’ science that emerges from the systematic, reductionist study of generalized patterns \(which is, in fact, what the CA modeler does\) is one which attempts to solve the \(usually much more complicated\) inverse problem: given a macroscopic pattern \(an ‘organizational phenomenon’\) what is the simplest valid way of understanding it \(predicting its future course, intervening successfully in its evolution\)? The steps here are very different than they are in the case of ‘normal science: data drives the process. One \(a\) starts with a general macroscopic pattern and \(b\) a set of ontologically plausible micro-agents \(people, sensory, motor and/or computational neural centers in the human brain, organizational activity and routine sets\) and posits \(c\) a set of plausible interaction mechanisms among these entities, which together, will reproduce the macroscopic pattern, barring which \(d\) one adjusts the ‘search space’ by modifying either the entities or the interaction mechanisms. Moreover, one also \(e\) studies, using numerical experiments, the various micro-local processes by which the interacting micro-entities produce macroscopic behavior. In such cases one starts from ontologically plausible micro-agents and empirically plausible \(and testable\) micro-interaction rules to ‘look into the future’ of the macro-entity in question.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Arguably, a such ‘new’ organization science is significantly more challenging than that which is currently practiced, but it has the benefit that it places the phenomena squarely in the foreground \(rather than leaving them as potential justifications for using a particular kind of theory\) and thus does not limit the horizon of the researcher to what we \(always-already?\) knew could be explained by the chosen model, theory or ‘framework’. ‘Challenging’ is meant to subsume both computational and non-computational difficulties. It is genuinely ‘harder’ in a computational sense to do data-driven organization science, and attempt to recover \(through numerical experimentation\) the micro-analytic rule patterns that produce a particular data pattern, than it is to start from a ‘well-grounded’ macro-analytic theory, produce a set of hypotheses by simple deductive steps, then search for the data that best exemplifies the theory in question \(i.e offers the most plausible prima facie testing ground for it\) in order to validate the derived hypotheses. It is also genuinely harder \(in an ontological sense\) to look for the right micro-analytic set of agents and interaction patterns that explain a particular macroscopic behavior, than it is to assume that the inherited theory has already latched onto the right ontological commitments. The ‘payoff’ for these greater \(and often sunk\) costs incurred by the researcher will be a science of organizations in which the explanandum \(the phenomenon\) will be far less determined by the choice of the explanans \(the theory\), and thus fewer phenomena will escape through the sparse ‘netting’ of a parsimonious theory.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(References)] TJ ET
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BT 0.000 782.981 Td /F1 9.8 Tf [(Cyert, R.M. and March, J.G. \(1963\). A Behavioral Theory of the Firm, ISBN 9780631174516 \(1992\).)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Earman, J. \(1992\). Bayes or Bust, ISBN 9780262050463.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Elster, J. \(1986\). Rational Choice, ISBN 9780814721698.)] TJ ET
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BT 0.000 782.981 Td /F1 9.8 Tf [(Friedman, M. \(1953\). “The methodology ofpositive economics,” in L. McIntyre and M. Martin \(eds.\), Readings in the Philosophy of the Social Sciences, ISBN 9780262631518 \(1994\).)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Gigerenzer, G. and Goldstein, D. G. \(1996\). “Mind as computer: Birth of a metaphor,” Creativity Research Journal, ISSN 1040-0419, 9:131-144.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Gödel, K. \(1931\). “Uber formal unentscheidbare Satze der Principia Mathematics und verwandtner Systeme,” Monatshefte fur Mathematik und Physik, 38: 173-198.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Grice, H.P. \(1975\). “Logic and conversation,” in P. Cole and J. Morgan \(eds.\), Syntax and Semantics, Vol. 3: Speech Acts, New York, NY: Academic Press.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Hempel, C. \(1966\). The Philosophy of Natural Science, ISBN 9780136638230.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Hofstadter, D. \(1999\). Gödel, Escher, Bach: An Eternal Golden Braid, ISBN 9780394756820 \(1989\).)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Hume, D. \(1949\). An Enquiry Concerning Human Understanding, ISBN 9780872202290 \(1993\).)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Kreps, D. \(1990\). “On corporate culture,” in J. Alt and K. Shepsle \(eds.\), Perspectives on Positive Political Economy, ISBN 9780521398510.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Kuhn, T.S. \(1967\). The Structure of Scientific Revolutions, ISBN 9780226458045.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Kuhn, T.S. \(2000\). The Road Since ’Structure’: Philo-sophical Essays, ISBN 9780226457987.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Lakoff, R. \(2001\). The Language Wars, ISBN 9780520232075.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Levinthal, D. and March, J.G. \(1993\). “The myopia of learning,” Strategic Management Journal, ISSN 0143-2095,14\(s2\): 95-112.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Lewis, D. \(1969\). Convention: A Philosophical Study, ISBN 9780674170254.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Li, Y.-F. and Vitanyi, M. \(1993\). An Introduction to Kolmogorov Complexity and Its Applications, ISBN 9780387948683 \(1997\).)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Lomi, A. and E. Larsen \(2001\). Dynamics of Organizations: Computational Modeling and Organization Theories, ISBN 9780262621526.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(March, J.G. \(1991\). “Exploration and exploitation in organizational learning,” Organization Science, ISSN 1047-7039, 2:71-87.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(March, J.G. \(1994\). A Primer on Decision Making, ISBN 9780029200353.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(March, J.G. and Olsen, J.P. \(1976\). Ambiguity and Choice in Organizations, ISBN 9788200014782.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(March, J.G. and Simon, H.A. \(1958\). Organizations, ISBN 9780471567936.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(March, J.G., Schulz, M. and Zhou, X. \(2000\). The Dynamics of Rules: Change in Written organizational Codes, ISBN 9780804739962.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(McKelvey, B. \(1999\). “Avoiding complexity catastrophe in co-evolutionary pockets,” Organization Science, ISSN 1526-5455, 10: 343-356.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. \(1999\). “Reasoning about choices and choosing among reasons,” Proceedings of the 3rd International Conference on Complex Systems, Nashua, New Hampshire.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. \(2002\). “Epistemology in action: A framework for understanding organizational due diligence processes,” in C.W. Choo and N. Bontis \(eds.\), The Strategic Management of Intellectual Capital and Organizational Knowledge: A Collection of Readings, ISBN 9780195138665.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. \(2006\). “Thinking strategically about thinking strategically: On the computational structure and dynamics of managerial cognition,” Working Paper No. 06-03, Rotman School of Management, University of Toronto, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=901443.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. and Bauer, R. \(2000\). “Circumspective rationality: The logic of problem-choice in strategic management,” Proceedings of 16th IAREP/SABE Conference, Baden, Austria.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. and Bauer, R. \(2002\). “What is organizational complexity and what difference does organizational complexity make? Academy of Management Proceedings, Washington, D.C.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. and Bauer, R. \(2004\). “On the relationship between organizational complexity and organizational dynamics,” Organization Science, ISSN 1526-5455, 15\(1\): 98-118.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. and Nohria, N. \(2002\). Master Passions: Emotions, Narrative and the Development of Culture, ISBN 9780262134057.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C. and Singh, J. \(2003\). “The evolutionary metaphor: A synthetic framework for the study of strategic organization,” Strategic Organi-zation, ISSN 1476-1270, 1\(4\):439-449.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C., Baum, J.A.C. and Rowley, T. \(2003\). “Information regimes, information strategies and the evolution of interfirm network topologies,” in F. Dansereau and M. Yammarino \(eds.\), Multi-Level Issues in Organizational Behavior and Strategy, ISBN 9780762310395, pp. 221-264.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Nagel, E. and Newman, E. \(1958\). Gödel’s Proof, ISBN 9780814703243.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Nelson, R.A. and Winter, S.G. \(1982\). An Evolutionary Theory of Economic Change, ISBN 9780674272286 \(2006\).)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Newell, A. \(1990\). Unified Theories of Cognition, ISBN 9780674921016 \(1994\).)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Nozick, R. \(2001\). Invariances, ISBN 9780674006317.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Putnam, H. \(1985\). “Reflexive reflections,” Erkenntnis, ISSN 0165-0106, 22:143-153.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Rivkin, J. \(2000\). “Imitation of complex strategies,” Management Science, ISSN 0025-1909, 46\(6\): 824-844.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Rubinstein, A. and Piccione, M. \(1993\). “Finite automata play a repeated extensive game,” Journal of Economic Theory, ISSN 0022-0531, 61: 160-168.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Schelling, T. \(1960\). The Strategy of Conflict, ISBN 9780674840300.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Sen, A. \(1993\). “Internal consistency of choice,” Econometrica, ISSN 0012-9682, 61\(3\): 495-521.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Sen, A. \(1997\). “Maximization and the act of choice,” Econometrica, ISSN 0012-9682, 73: 1-34.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Simon, H. \(1990\). “The invariants of human behavior,” Annual Reviews of Psychology, ISSN 0066-4308, 41: 1-20.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Simon, H.A. \(1947\). Administrative Behavior, ISBN 9780029289709 \(1976\).)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Simon, H.A. \(1969\). “The architecture of complexity,” in H. A. Simon, The Sciences of the Artificial, ISBN 9780262691918 \(1996\).)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Simon, H.A. \(1973\). “The structure of ill-structured problems,” Artificial Intelligence, ISSN 0004-3702,4\(3\):181-200.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Simon, H.A. \(1992\). “What is an explanation of behavior?” Psychological Science, ISSN 0956-7976, 3\(3\):150-161.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Simon, H.A. \(1996\). The Sciences of the Artificial, ISBN 9780262691918.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Sterman, J. \(2000\). Business Dynamics: Systems Thinking and Modeling for a Complex World, ISBN 9780072389159.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Toulmin, S. \(1980\). The Uses of Argument, ISBN 9780521827485.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Turing, A.M. \(1950\). “Computing machinery and intelligence,” Mind, ISSN 0026-4423, 59: 433-460.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Weick, K.E. \(1991\). “The non-traditional quality of organizational learning,” Organization Science, ISSN 1047-7039, 2\(1\):116-124.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Weick, K.E. \(1995\). Sense-Making in Organizations, ISBN 9780803971776.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Wittgenstein, L. \(1953\). Philosophical Investigations, ISBN 9780631146704.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Wolfram, S. \(1983\). “The statistical mechanics of cellular automata,” Reviews of Modern Physics, ISSN 0034-6861, 43\(3\): 601-644.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Wolfram, S. \(1985\). “Undecidability and intractability in theoretical physics,” Physical Review Letters, ISSN 0031-9007, 54: 735-738.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Wolfram, S. \(2002\). A New Kind of Science, ISBN 9781579550080.)] TJ ET
Q
q
15.000 -383.118 577.500 1160.118 re W n
0.965 0.965 0.965 rg
26.250 -375.618 555.000 1152.618 re f
0.267 0.267 0.267 rg
0.267 0.267 0.267 RG
26.250 -375.618 m 581.250 -375.618 l 581.250 -374.868 l 26.250 -374.868 l f
0.271 0.267 0.267 rg
BT 41.206 767.494 Td /F1 9.8 Tf [(1.)] TJ ET
BT 54.750 767.476 Td /F4 9.8 Tf [(The informational or algorithmic complexity of the local rule sets)] TJ ET
BT 354.913 767.476 Td /F1 9.8 Tf [(. This quantity \(which we shall refer to as )] TJ ET
BT 54.750 755.571 Td /F5 9.8 Tf [(local informational depth)] TJ ET
BT 160.421 755.571 Td /F1 9.8 Tf [(\) is the Kolmogorov complexity \(see Li & Vitanyi, 1993\) of the local CA rules, and can be )] TJ ET
BT 54.750 743.667 Td /F1 9.8 Tf [(defined as the length \(in bits or M-ary units of information\) of the minimum-length algorithm that can execute these )] TJ ET
BT 54.750 731.762 Td /F1 9.8 Tf [(rules. It is a measure of local ‘random access memory’—or short term memory required by an element of a CA to act )] TJ ET
BT 54.750 719.857 Td /F1 9.8 Tf [(according to ‘its’ local rule system: if the agent’s memory is not capable of supporting at least the number of states that )] TJ ET
BT 54.750 707.952 Td /F1 9.8 Tf [(the rule system that governs its local interactions refers to, then it will not be able to ‘sustain’ local interactions )] TJ ET
BT 54.750 696.048 Td /F1 9.8 Tf [(governed by that rule. If the CA element is an individual agent, then the local informational depth of the rules of the CA )] TJ ET
BT 54.750 684.143 Td /F1 9.8 Tf [(measures the short-term memory required in order for the agent to ‘g)] TJ ET
BT 351.696 684.143 Td /F5 9.8 Tf [(et al)] TJ ET
BT 370.124 684.143 Td /F1 9.8 Tf [(ong’ with other agents.)] TJ ET
BT 41.206 661.006 Td /F1 9.8 Tf [(2.)] TJ ET
BT 54.750 660.988 Td /F4 9.8 Tf [(The computational complexity of the local rule sets)] TJ ET
BT 292.065 660.988 Td /F1 9.8 Tf [(. This quantity measures the number of calculations required at )] TJ ET
BT 54.750 649.083 Td /F1 9.8 Tf [(the level of each CA element for the implementation of the local CA rules, as a function of the number of CA neighbors )] TJ ET
BT 54.750 637.179 Td /F1 9.8 Tf [(on which the state of the original CA element depends, and the number of possible states of the neighboring elements )] TJ ET
BT 54.750 625.274 Td /F1 9.8 Tf [(on which the state of each CA element depends. We shall refer to this quantity as the )] TJ ET
BT 424.899 625.274 Td /F5 9.8 Tf [(local computational load)] TJ ET
BT 529.487 625.274 Td /F1 9.8 Tf [( of the )] TJ ET
BT 54.750 613.369 Td /F1 9.8 Tf [(CA model \(Moldoveanu & Bauer, 2003; see Cormen )] TJ ET
BT 282.890 613.369 Td /F5 9.8 Tf [(et al)] TJ ET
BT 301.318 613.369 Td /F1 9.8 Tf [(., 1993 for a pedagogical introduction to computational )] TJ ET
BT 54.750 601.464 Td /F1 9.8 Tf [(complexity for algorithmic problems.For a 2-state CA model \(each element can take on the value of ‘0’ or ‘1’\) in which )] TJ ET
BT 54.750 589.560 Td /F1 9.8 Tf [(the state of each element depends linearly on the individual states of each of K neighbors, the local computational load )] TJ ET
BT 54.750 577.655 Td /F1 9.8 Tf [(for each CA element is bounded from above by 2K. Nonlinear dependencies of the state of one CA element on )] TJ ET
BT 54.750 565.750 Td /F1 9.8 Tf [(\(including ‘memory effects’\) will increase the number of operations required locally to compute the next state of the CA )] TJ ET
BT 54.750 553.845 Td /F1 9.8 Tf [(element from knowledge of the states of the neighboring elements. If each CA element is taken to model an agent \(in a )] TJ ET
BT 54.750 541.941 Td /F1 9.8 Tf [(multi-agent model of an organization\), then local computational load measures the computational power required of an )] TJ ET
BT 54.750 530.036 Td /F1 9.8 Tf [(agent to successfully interact with other agents according to the local rule sets.)] TJ ET
BT 41.206 506.899 Td /F1 9.8 Tf [(3.)] TJ ET
BT 54.750 506.881 Td /F4 9.8 Tf [(The global computational complexity of the resulting CA pattern produced by the iterative application of a set )] TJ ET
BT 54.750 494.976 Td /F4 9.8 Tf [(of micro-local rules or rule sets)] TJ ET
BT 199.966 494.976 Td /F1 9.8 Tf [(. This quantity, which is defined as a function of the total number of variables \()] TJ ET
BT 54.750 483.072 Td /F5 9.8 Tf [(CA elements x states\))] TJ ET
BT 150.115 483.072 Td /F1 9.8 Tf [( that determine the evolution of local CA elements, measures how ‘difficult’ it is for a particular )] TJ ET
BT 54.750 471.167 Td /F1 9.8 Tf [(micro-rule set to replicate a particular large-scale pattern of the CA. It is a measure of the computational complexity of )] TJ ET
BT 54.750 459.262 Td /F1 9.8 Tf [(producing a simulation of an organizational phenomenon starting from a set of agent-level models \(such as rational )] TJ ET
BT 54.750 447.357 Td /F1 9.8 Tf [(choice models or game-theoretic models\). This quantity measures the relative efficiency of using micro-rule systems )] TJ ET
BT 54.750 435.453 Td /F1 9.8 Tf [(\(organizational ‘common law’ and statutes is often written in terms of such rule systems\) in order to predict the global )] TJ ET
BT 54.750 423.548 Td /F1 9.8 Tf [(evolution of the organization. It also measures the relative difficulty for the modeler of explaining an organizational )] TJ ET
BT 54.750 411.643 Td /F1 9.8 Tf [(phenomenon based on a metaphysical commitment to methodological individualism \(i.e., to the explanation of social )] TJ ET
BT 54.750 399.738 Td /F1 9.8 Tf [(phenomena by reduction to individual-level phenomena\) and a set of models of individual behavior.)] TJ ET
BT 41.206 376.601 Td /F1 9.8 Tf [(4.)] TJ ET
BT 54.750 376.584 Td /F4 9.8 Tf [(The global informational depth of the resulting CA pattern.)] TJ ET
BT 325.615 376.584 Td /F1 9.8 Tf [( It is a measure of the relative ‘compressibility’ or )] TJ ET
BT 54.750 364.679 Td /F1 9.8 Tf [(‘reducibility’ of the global pattern exhibited by the CA. Here, Wolfram’s \(2002\) 4 complexity regimes are illuminating and )] TJ ET
BT 54.750 352.774 Td /F1 9.8 Tf [(can provide useful qualitative discriminators for the study of organizational phenomena. If, for instance, the global )] TJ ET
BT 54.750 340.869 Td /F1 9.8 Tf [(informational depth of a CA pattern is the pattern itself, then the pattern is incompressible. There is no ‘short-cut’ to )] TJ ET
BT 54.750 328.965 Td /F1 9.8 Tf [(producing the pattern, other than the iterative application of the local rule sets that produced it \(regime 4\) to the )] TJ ET
BT 54.750 317.060 Td /F1 9.8 Tf [(particular initial conditions of the CA. \(Different initial conditions will produce different end results at the macroscopic )] TJ ET
BT 54.750 305.155 Td /F1 9.8 Tf [(level. Thus, regime 4 behavior has something in common with dynamical regimes characterized by sensitive )] TJ ET
BT 54.750 293.250 Td /F1 9.8 Tf [(dependence on initial conditions, such as chaotic dynamical systems\). )] TJ ET
BT 359.837 293.250 Td /F5 9.8 Tf [(Organizational example)] TJ ET
BT 462.251 293.250 Td /F1 9.8 Tf [(: speculative bubbles )] TJ ET
BT 54.750 281.346 Td /F1 9.8 Tf [(\(positive feedbacks in trading behavior which are not based on underlying, inter-subjectively agreeable ‘facts of the )] TJ ET
BT 54.750 269.441 Td /F1 9.8 Tf [(matter’\) are structures or patterns that may or may not occur at particular space-time instances, depending on the kinds )] TJ ET
BT 54.750 257.536 Td /F1 9.8 Tf [(of information \(i.e., initial conditions\) that conditions the actions of the various traders. If, on the other hand, a )] TJ ET
BT 54.750 245.631 Td /F1 9.8 Tf [(macroscopic pattern is easily discerned and emerges from )] TJ ET
BT 309.420 245.631 Td /F5 9.8 Tf [(any)] TJ ET
BT 325.137 245.631 Td /F1 9.8 Tf [( initial conditions, then the CA model can be reduced to )] TJ ET
BT 54.750 233.727 Td /F1 9.8 Tf [(a set of rules that predict macroscopic evolution from microscopic rule systems without depending solely on the )] TJ ET
BT 54.750 221.822 Td /F1 9.8 Tf [(microscopic rule systems or varying with initial conditions \(regime 1\). )] TJ ET
BT 353.276 221.822 Td /F5 9.8 Tf [(Organizational example:)] TJ ET
BT 458.400 221.822 Td /F1 9.8 Tf [( Bertrand-Nash or )] TJ ET
BT 54.750 209.917 Td /F1 9.8 Tf [(Cournot-Nash equilibria, when they accurately represent oligopolistic behavior, exemplify the convergence of prices, )] TJ ET
BT 54.750 198.012 Td /F1 9.8 Tf [(from )] TJ ET
BT 76.960 198.012 Td /F5 9.8 Tf [(any)] TJ ET
BT 92.678 198.012 Td /F1 9.8 Tf [( initial conditions, to a set of prices that is predictable from knowledge of the producers’ cost functions. )] TJ ET
BT 54.750 186.108 Td /F1 9.8 Tf [(Wolfram’s ‘in-between’ regimes \(2 and 3\) are regimes in which there are macroscopic patterns \(thus they are )] TJ ET
BT 54.750 174.203 Td /F1 9.8 Tf [(compressible\) whose topological features \(regime 3\) or structural properties \(regime 2\) can be predicted via ‘short cuts’ )] TJ ET
BT 54.750 162.298 Td /F1 9.8 Tf [(from knowledge of the structure of the micro-rules via short-cuts \(intermediate rules\) whose implementation to simulate )] TJ ET
BT 54.750 150.393 Td /F1 9.8 Tf [(the macro-pattern is computationally lighter than the implementation of the micro-rules to simulate the same pattern. To )] TJ ET
BT 54.750 138.489 Td /F1 9.8 Tf [(use an intuitive organizational example for regime 2: Oscillatory behavior \(of under-supply/over-supply\) in feedback-)] TJ ET
BT 54.750 126.584 Td /F1 9.8 Tf [(regulated production systems \(Sterman, 2000\) is regular macroscopic behavior determined by microscopic rules of )] TJ ET
BT 54.750 114.679 Td /F1 9.8 Tf [(‘local rationality’ that governs the behavior of individual agents; and here is one for regime 3: the topology of inter-)] TJ ET
BT 54.750 102.774 Td /F1 9.8 Tf [(organizational networks \(Moldoveanu )] TJ ET
BT 218.940 102.774 Td /F5 9.8 Tf [(et al)] TJ ET
BT 237.368 102.774 Td /F1 9.8 Tf [(., 2003\) exhibits certain consistent regularities in situations in which network )] TJ ET
BT 54.750 90.870 Td /F1 9.8 Tf [(agents follow certain \(micro-locally well-defined\) strategies for sharing or withholding information, even though the )] TJ ET
BT 54.750 78.965 Td /F1 9.8 Tf [(precise structure of these networks \(i.e., the positions, in the network lattice, where these topological structures will )] TJ ET
BT 54.750 67.060 Td /F1 9.8 Tf [(occur\) cannot be specified in advance, as they sensitively depend on initial conditions. One may know that a particular )] TJ ET
BT 54.750 55.155 Td /F1 9.8 Tf [(network will evolve towards a large set of ‘center-periphery sub-networks, without being able to figure out where, )] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(precisely, each particular sub-network will emerge.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Fundamental trade-offs in the design of organizations and the pursuit of ‘organization science’)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(We are now in a position to discuss the essential tradeoffs that both the organizational designer \(such as a top manager or top management team\) and the organizational modeler \(the researcher\) make in designing micro-rules and micro-models to control, explain, predict or influence macro-level organizational phenomena. To connect the discussion to familiar terms and concepts—even though, we note the need for a fundamentally ‘new’ language for describing organizations which emerges if the premises of this paper are followed up on—we follow Cohen and March \(1972\) and posit three fundamental problems that the organizational designer or modeler must seek resolve as part of their tasks: the problem of conflict among local rules and rule sets; the problem of ambiguity—of representing a new phenomenon or signal and synthesizing a set of actionable or intelligible micro-level explanations for it; and the problem of uncertainty—that of narrowing down a set of possible worlds that are causally connected to the actual world to a smaller set of plausible worlds, ordered according to the plausibilities of these worlds.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The problem of conflict: Increasing the complexity of local rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Conflict among micro-local rules is usually resolved by introducing synthetic, ‘new’ rules, based on contingency clauses. Consider two simple ‘if-then-else’ rules, such as: ‘if a superior issues a command, then obey it \(to facilitate coordination\)’ and ‘if a superior issues a command, then question it \(to facilitate validation of information\)’. The two rules are not compatible on their face, and indicate 2 different action sequences \(changes in the micro-states of a CA element\) in response to the same ostensible signal \(an order from a superior\). This conflict may be directly experienced by the individual member of the organization to whom they supposedly apply, or it may be experienced by those, higher up in the hierarchy, who can see the conflict and have the lucidity to conceptualize it as a conflict. It can be resolved by issuing a synthetic and more complex rule, which says, “if a superior utters a command, then subject to a process of inquiry, follow the process, and, if the process comes out either uncertain or positive, then follow the order; else, question the command.” The rule is more complex—and may become more complex still as one begins to ponder the various processes of inquiry to which an individual agent may subject the order from the superior. In that case, we may have developments of the rule which specify norms of epistemic and discoursive rationality to which any such order must be made to answer, which will conform to a set of rules about the ways in which evidence is to be presented, the way it should be made to count, and so forth. Thus, conflict resolution at the organizational level comes at the expense of increasing the complexity of the micro-local rule sets. Here, again, there is a further distinction to be made:)] TJ ET
0.965 0.965 0.965 rg
0.000 792.000 0.000 0.000 re f
0.267 0.267 0.267 rg
0.000 792.000 m 0.000 792.000 l 0.000 791.250 l 0.000 791.250 l f
0.000 792.000 m 0.000 792.000 l 0.000 792.750 l 0.000 792.750 l f
0.271 0.267 0.267 rg
BT -8.132 782.494 Td /F1 9.8 Tf [(1.)] TJ ET
BT 0.000 782.981 Td /F4 9.8 Tf [(Increases in informational depth of the local rule sets)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. One can effect an increase in the informational depth of the local rule sets, usually by increasing the number of different ‘but for’ or )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(classes of exceptions)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( to the rule. This move can be modeled by increasing the number of neighbors of an element in a CA model on whose states the immediate future state of the said element depends, or, an increase in the number possible states of each CA element \(or, both together\). Such an increase in local informational depth corresponds—in the case of the organizational designer—to taxing the short-term-memory \(or ‘working memory’\) of each individual agent. For the organizational modeler, this move amounts to assigning a larger ‘working memory’ on the part of the individual agent. Such increases can be traded off against:)] TJ ET
BT -8.132 782.494 Td /F1 9.8 Tf [(2.)] TJ ET
BT 0.000 782.981 Td /F4 9.8 Tf [(Increases in computational load of the local rule sets)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. A key insight that comes out of a large scale study of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(CA)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( systems is that the behavior of CAs bound by )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(N)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(-state rules can be emulated by the behavior of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(CA)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(s bound by \()] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(N-l)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(\)-state rules that are )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(computationally deeper)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. One can compensate for ‘simpler’ agents—in the informational sense—by increasing the computational complexity of the interaction rules. \(Of course, the precise characteristics of this trade-off are not, in general, well understood, and require significant research\). This suggests that the organizational designer \(or modeler\) can substitute computational load \(‘what agents do’\) for informational depth \(‘what agents think’\)of the local rule sets. The organizational designer can design rule systems that makes individual agents ‘think more, but interact less’, as they would have to interact in order to exchange timely information about their states at any given time. The organizational modeler can develop models of organizational agents that are more computationally adept \(as in rational choice and game theoretic models\) but more interactionally and imaginatively inept \(as in the very same kinds of models\).)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The problem of ambiguity: Increasing the complexity of synthesizable macroscopic patterns)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The problem of ambiguity refers to the problem of categorizing emergent macro-level organizational phenomena—of representing those phenomena in ways that makes them either intelligible to organizational agents acting according to simple rule sets or that makes them synthesizable \(in the case of the organizational modeler\) from a set of agent-level models. The problem of ambiguity is usually resolved by creating micro rules that can synthesize the largest possible number of macroscopic-level patterns—because ‘understanding’ a macro-level pattern entails the ability to synthesize it from a set of micro-level rules, if we take ‘understnding’ to be synonymous with ‘valid explanation’. Thus, universal interaction rules may come to be favored over non-universal rules. Moreover, regime-3 and regime-4-producing rules will come to be favored over regime-1 and regime-2-producing rules \(as the set of macro-level patterns that can be synthesized under regimes 3 and 4 is far greater than that which can be synthesized with regime-1 and regime-2-producing rules. However, no sooner has the organizational designer, or organizational modeler, dealt with the problem of ambiguity than he or she is confronted with:)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The problem of uncertainty: decreasing the \(informational and computational\) complexity of macroscopic patterns)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The problem of uncertainty refers to narrowing down the set of possible macro-worlds that can be produced from a set of interacting micro-worlds. Macroscopic complexity refers to the relative compressibility of the macroscopic pattern. An intelligible organizational pattern—one that does not produce a lot of uncertainty—is one that can be simulated without running through the entire set of calculations which the CA had to perform in order to get to the answer. Equilibrium models of consumer behavior, game-theoretic models of competitive behavior among oligopolists, agency-theoretic models of owner-manager interactions in firms are all models that work by reproducing essential features of macro-level behavior by starting from caricatural models of agent behavior, and positing a ‘short-cut’ \(Nash equilibrium, general equilibrium, ‘perfect markets’\) to the prediction of a global pattern. An intelligible organizational pattern is also one that does not sensitively depend on the initial conditions of the CA. General equilibrium models, for instance, do not rely, for their predictions, on detailed knowledge of the preferences and personal histories of the agents engaged in exchange with one another. Thus, regime-1- and regime-2-producing CA micro-rules will come to be favored over regime-3 and regime-4-producing micro-rules by organizational designers and modelers bent on reducing uncertainty. Our analysis thus highlights the fact that there is a trade-off \(and not a confluence\) between measures aimed at reducing uncertainty and measured aimed at reducing ambiguity.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(In sum, CA models can be used to produce a ‘design landscape’ or ‘design space’ that allows both modelers and designers of organizations to make trade-offs in their choices of interaction rules and CA element models. The reduction of conflict appears to come at the expense of an increase in global uncertainty \(due to more complex—informationally deep or computationally heavy—local rule sets\). Increasing local complexity can be accomplished in one of two ways. Increasing local informational depth can be traded off against increasing local computational load—amounting to a trade-off between computational prowess and storage/access prowess at the level of the rule-following agent. The resolution of ambiguity and the mitigation of uncertainty can be understood as embodying yet another trade-off—between increasing the contingency and complexity of global patterns that are deterministically synthesizable from local rules, and decreasing the contingency and complexity of global patterns that are deterministically synthesizable from local rules. It is likely, therefore, that ambiguity-reducing measures will increase uncertainty \(which would also be increased by conflict-reducing measures\); and vice-versa.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Models of rule interactions for complex organizational systems)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The language of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(CA)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( models allows us to talk about the evolution of the semantic and syntactic content of rules in organizations, by focusing on the fundamental trade-offs that the rule designer must make. If organizations solve—by the adoption of rules and rule systems—the problems of ambiguity, conflict and uncertainty—then it is possible, as we have seen to quantify the fundamental trade-offs at the local level \(between informational depth and computational load of micro-rules\) and at the global level \(between the need for comprehensiveness of a particular pattern, which speaks to the problem of ambiguity resolution, and the need for simplicity \(informational and computational\) of that pattern, which speaks to the problem of uncertainty mitigation\). But, to make the application of CA models to the modeling of organizational phenomena persuasive, we should incorporate the reflexive nature of social rules and social systems. That is, simple, rule-driven CA models, if worth their salt )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(qua)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( explanation-generating engines, should accommodate the reflexive adaptation of rules to changing conditions or to the recognition of inefficiencies or faults with the existing rule systems.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(There is, of course, no reason to hope that a rule-bound system will be able to model itself fully \(Hofstadter, 1981\), prove the validity of its own core rules \(Putnam, 1985\) or provide computability or provability conditions for an arbitrarily large number of propositions \(Gödel, 1931; Nagel & Newman, 1958 for a pedagogical exposition of Gödel’s well-known result\). These conundrums of reflexivity cannot be resolved—except by meta-logical means.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(But, not being to resolve undecidable problems that arise in second-order logic does not mean that adaptations of rule systems cannot be modeled and understood in the simple language of first-order rules. Examples of such successful modeling enterprises include the use of genetic algorithms to model organizational learning \(Bruderer & Singh, 1996; Moldoveanu & Singh, 2003\). Reflexivity in this case manifests itself as competition between ideas and behaviors at the organizational level, coupled with a decision criterion that selects surviving beliefs and behaviors. Thus, there is ample reason to hope that a general framework for modeling rules can help us understand the evolution of rule systems as a result of \(possibly incomplete\) reflexive or meta-logical operations, and shed light on the important ‘phase change’ from decidable to undecidable problems, which are so common in second order logic, and, by inference, in social systems that are capable of modifying their behavior in response to an awareness of the causes, consequences or symbolism of that behavior.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Interactions between Rules and Meta-Rules: The Phenomenon of Irreducibility)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Meta-rules can e modeled as rules that govern changes in rules. For instance, ‘always seek an informationally constrained adaptation in response to a local rule conflict’ is an example of a meta-rule that constrains an organization’s adaptation to micro-local rule conflict. Meta-rules can be thought of as learning rules, or, perhaps more precisely, as rules for learning. If learning \(at the level of either the individual or the organization\) is about the \(inductive or abductive\) “discovery” of regularities in both the environment and the organization’s response to the environment and the adaptive modification of rule sets for the optimal exploitation of such regularities, then ‘learning algorithms’ in general are also examples of meta-rules.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(If we understand the organization as an instantiation of a set of micro-local rules and rule sets that produce—when combined together and combined with arbitrary initial conditions \(representing environmental inputs\)—a large-scale pattern, then ‘organizational learning’ is about the ability to predict what that pattern shall be in the most efficient possible way. It is about producing short cuts, or rules that ‘cut through’ the amount of computation and information required to predict the global behavioral pattern of the organization, starting from knowledge of the local rules and environmental inputs.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(But organizational learning is not merely about the production of insight about patterns \(Weick, 1991\), but also about the production of adaptive behavior that is causally linked to such insights. Thus ‘short cuts’ and predictively powerful rules of thumb will themselves come to substitute existing micro-local rules. The CA that models the continuously learning organization can be understood as a ‘self-compression’ engine, driving toward patterns that are less and less compressible over time. What is the limiting point of this process? It is precisely the set of )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(computationally irreducible CA rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(—the very rules that govern CA patterns that cannot be ‘short cut’ by another set of rules, because they provide the quickest route to generating a large scale pattern from knowledge of themselves and the initial conditions for the CA. Thus, ‘learning’ \(in the precise sense in which we have defined it here\) drives rule-bound systems towards computational irreducibility. This also means, incidentally, that continuously learning organizations will exhibit phenomena that are )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(decreasingly predictable)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [( by models which use ‘short cuts’—i.e., precisely the models that researchers use in order to explain organizational phenomena, or, more simply, that )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(learning organizations evolve toward unpredictability)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Interactions between rules and para-rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Para-rules are rules for resolving micro-local rule conflicts. They serve as ‘tie-breakers’ in situations of ‘conflict of laws’. Synthetic resolutions \(resolutions that incorporate elements of both conflicting rules and rule sets\) are likely to be more complex \(computationally, informationally, or both together\) than the rules that they effectively replace—as we have seen. If para-rules replace the rules they over-rule and themselves become part of the micro-local rule set, then we might expect an increase in micro-local rule complexity in response to events that cause micro-local rule conflicts \(such as organizational mergers, or strategic re-focusing of the organization\).)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Interactions between rules and ortho-rules)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Ortho-rules are rules that specify the domain of applicability of rules. They specify the situations under which it is sensible, for instance, to think of a particular rule as applicable, or to assert a command that is based on that rule. Ortho-rules thus can be thought to mediate between rules and the behaviors that rules proscribe or prescribe. Paradigm shifts \(Kuhn, 1962; 1990\) in the environment are situations in which the basic ontology in terms of which propositions and beliefs are advanced in the organization is challenged. The objects to which rules refer become diffuse and ambiguous. Ortho-rules become necessary for creating new distinctions, and thus for helping agents within the organization differentiate between various environmental signals. Once again, there will be an attending increase in the complexity of the micro-local rule sets:)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Discussion: Open questions and opportunities for inquiry)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Reducing organizational phenomena to macro-patterns that can be seen as instantiations of computations carried out by rule-following micro-agents leaves us in a position to inquire into the dynamics of the processes by which such phenomena are modeled: it allows us to also model the processes by which organizational researchers seek to explain and predict organizational phenomena of interest. Models are themselves rule-bound entities. They often proceed from micro-analytic assumptions about human behavior, cognition and motivation to supply explanations of organizational behaviors, or behaviors of aggregates of individual agents. Such models are computational entities—it is not merely the case that they )] TJ ET
BT 0.000 782.981 Td /F5 9.8 Tf [(can be conceptualized as computational entities)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(. Thus, it makes sense to ask: what does the reducibility of organizational phenomena to generalized CA models tell us about the enterprise of modeling organizational phenomena? Here, we can only offer preliminary remarks, which await future development, and can be roughly grouped as follows:)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(Observing the observers of the observers when all observers are rule-bound: How do explanations explain?)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(What happens when a model ‘models’ an organizational phenomenon? To recognize it as the model of the phenomenon it \(putatively\) models, we must be able to observe some informational compression: the model should be informationally simpler \(though it can be computationally more complex\) than a ‘mere’ description of the phenomenon. Moreover, a model ‘models’ by allowing the modeler to make at least some valid predictions of future states of that phenomenon. It functions as a prism or lens for seeing forward through time \(or through the time axis of a particular entity\), linking past observables to future observables in a predictable fashion.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(It would be interesting to discover the relative frequency with which we can expect to come across predictively valid models \(rather than models based on illusory correlations\), and the language of CA does now make it possible to discover relative frequencies \(by simulating organizational processes and also processes that model organizational processes based on various plausible conjectured patterns, and monitoring the ‘hit rate’ for these patterns—the frequency with which they successfully predict future behavior in the system that they model\). Thus, we can begin to develop ‘ignorance metrics’ for various classes of organizational processes—metrics of the a priori likelihood that we will attribute an explanatory success to an illusory correlation rather than a ‘valid model’.)] TJ ET
BT 0.000 780.900 Td /F4 12.0 Tf [(The relationship between computational experiments and ‘normal’ organization science: The pursuit of rule selection and rule change through computational experiments, and the problem of organizational design)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Is there a ‘new organization science’ that emerges from projects such as Wolfram’s ‘new kind of science? ‘Normal’ organization science—to the extent that anyone would self-consciously admit to engaging in such an exercise \(perhaps ‘theory-driven organization science’ would be a better term\)—consists in \(a\) the specification of a ‘theory’ or ‘model’ \(‘rational choice theory’, for instance; or, ‘game-theoretic models’ or ‘institutional analysis’; or, models drawn from ‘conflict sociology’\); \(b\) the derivation of hypotheses to be tested against observation statements; \(c\) the validation of the said hypotheses by comparison with the \(potentially falsifying\) observation statements, and; \(d\) the modification of the hypotheses, the theory or the set of \(extra-theoretical\) ‘background assumptions’ in line with the results of the empirical tests.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Wolfram \(2002\) is correct in arguing that such ‘theory-driven’ scientific endeavor runs the risk of turning into a dogma-preserving exercise, by the following mechanism: once we allow theory and model to drive the process of looking for, conceptualizing and gathering data, putting the data into the form of observation statements and deciding which among the data sets ‘count’, there is a lot of phenomenology \(which does not get addressed or has no ontological basis in the theory that we start with\) that will simply ‘escape’ the modeler. In organization science, where the ambiguity and value-ladenness of theories and complexity of the phenomena is notorious, this effect can only be amplified.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(The ‘alternative’ or ‘new’ science that emerges from the systematic, reductionist study of generalized patterns \(which is, in fact, what the CA modeler does\) is one which attempts to solve the \(usually much more complicated\) inverse problem: given a macroscopic pattern \(an ‘organizational phenomenon’\) what is the simplest valid way of understanding it \(predicting its future course, intervening successfully in its evolution\)? The steps here are very different than they are in the case of ‘normal science: data drives the process. One \(a\) starts with a general macroscopic pattern and \(b\) a set of ontologically plausible micro-agents \(people, sensory, motor and/or computational neural centers in the human brain, organizational activity and routine sets\) and posits \(c\) a set of plausible interaction mechanisms among these entities, which together, will reproduce the macroscopic pattern, barring which \(d\) one adjusts the ‘search space’ by modifying either the entities or the interaction mechanisms. Moreover, one also \(e\) studies, using numerical experiments, the various micro-local processes by which the interacting micro-entities produce macroscopic behavior. In such cases one starts from ontologically plausible micro-agents and empirically plausible \(and testable\) micro-interaction rules to ‘look into the future’ of the macro-entity in question.)] TJ ET
BT 0.000 782.981 Td /F1 9.8 Tf [(Arguably, a such ‘new’ organization science is significantly more challenging than that which is currently practiced, but it has the benefit that it places the phenomena squarely in the foreground \(rather than leaving them as potential justifications for using a particular kind of theory\) and thus does not limit the horizon of the researcher to what we \(always-already?\) knew could be explained by the chosen model, theory or ‘framework’. ‘Challenging’ is meant to subsume both computational and non-computational difficulties. It is genuinely ‘harder’ in a computational sense to do data-driven organization science, and attempt to recover \(through numerical experimentation\) the micro-analytic rule patterns that produce a particular data pattern, than it is to start from a ‘well-grounded’ macro-analytic theory, produce a set of hypotheses by simple deductive steps, then search for the data that best exemplifies the theory in question \(i.e offers the most plausible prima facie testing ground for it\) in order to validate the derived hypotheses. It is also genuinely harder \(in an ontological sense\) to look for the right micro-analytic set of agents and interaction patterns that explain a particular macroscopic behavior, than it is to assume that the inherited theory has already latched onto the right ontological commitments. The ‘payoff’ for these greater \(and often sunk\) costs incurred by the researcher will be a science of organizations in which the explanandum \(the phenomenon\) will be far less determined by the choice of the explanans \(the theory\), and thus fewer phenomena will escape through the sparse ‘netting’ of a parsimonious theory.)] TJ ET
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BT 0.000 782.981 Td /F1 9.8 Tf [(Moldoveanu, M.C., Baum, J.A.C. and Rowley, T. \(2003\). “Information regimes, information strategies and the evolution of interfirm network topologies,” in F. Dansereau and M. Yammarino \(eds.\), Multi-Level Issues in Organizational Behavior and Strategy, ISBN 9780762310395, pp. 221-264.)] TJ ET
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BT 0.000 782.981 Td /F1 9.8 Tf [(Schelling, T. \(1960\). The Strategy of Conflict, ISBN 9780674840300.)] TJ ET
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