Previous research suggests that organizations may apply two opposite complexity mechanisms to cope with environmental uncertainty: absorption and reduction. However, except for some anecdotal evidence, there is no theoretical skeleton established to integrate these two opposite mechanisms in one framework and to prescribe the contingent conditions for employing them. This paper deconstructs organizational complexity at the organizational elemental level and establishes framework that incorporates three dimensions—organizational complexity, organizational dynamism, and organizational variability. This paper also discusses the environmental conditions for applying absorption and reduction mechanisms as well as the benefits and costs of applying these mechanisms. This dimensionality perspective provides a new avenue for researchers and practitioners to understand and handle organizational structuration issues.
This article describes research into the discovery and modelling of emergent temporal phenomena in social networks. It summarizes experimental results that bring together two views in contemporary science: Bayesian analysis and link prediction, to enhance the current understanding of emergent temporal patterns in social network analysis (SNA), particularly in value creation through social connectedness—an important, and growing, discipline within management science. Traditional link prediction methods use the values of metrics in a graph to determine where new links are likely to arise, and little work has been done on analyzing long-term graph trends. We have found that existing graph generation models are unrealistic in their prediction, and can be complemented through the use of temporal metrics, in the study of some networks. To date, no temporal information has been used in link prediction research, thereby excluding valuable temporal trends that emerge in sociogram sequences and also lowering the accuracy of the link prediction. We extracted information from the Pussokram online dating network dataset, and 9,939 cases of each class were formed. Logistic regression in the Weka data mining system was used to perform link prediction. Our results show that temporal metrics are an extremely valuable new contribution to link prediction, and should be used in future applications. In addition to using metrics to measure the local behaviors of participants in social networks, we used Bayesian networks to model the interrelationships between the metrics as local behaviors and links forming between individuals as emergent behaviors (social complexity). We also explored how the metrics evolve over time using Dynamic Bayesian Networks (DBN).
This article presents some prototypes of conflict situations that follow from different pathways to chaos. The substance of the conflicts can be extracted from empirical analysis using orbital decomposition (symbolic dynamics), nonlinear regression, or simulations, depending on the nature of the problem. Examples from the political science literature are presented. A distinction is also made between conflicts that are centered in chaos and those that are more similar to the hysteresis feature of catastrophe models.
The author identifies a Law of Requisite Cognitive Capacity in human communication, conflict resolution, and cooperation solicitation. Based on Ashby’s Law of the Requisite Variety and Jaques’s theory of cognitive capacity and by combining the author’s previous work on the cognitive model of improving communication efficiency, a quantitative limitation for people to understand each other can be identified. On the Jaquesian Cognitive Capacity Strata, it is necessary for the person on a higher stratum to make extra efforts to explain/translate his/her mental model for the person (or P-individual) on a lower stratum, using the language/mental model available at the lower stratum. Without such explanation/translation, the person on a lower stratum cannot cognize the mental model being used and will misunderstand, therefore effective communication cannot be achieved. The existence of such limitation explains a number of interesting social and organizational phenomena.
Structure of organizations tends to affect the complexity of their behavior during the process of organizational transformation. As a result, organizations that are more complex structurally tend to transform in the environment that is more conflict-prone. We suggest that by affecting the structure of an organization during the process of organizational transformation, its behavior and conflict environment can be controlled. This paper examines the process of organizational transformation from the perspective of the complex systems theory and chaos theory. It offers insights and implications that could lead to better strategies for managing a conflict environment of organizations.
The evolution of cooperative, pro-social behavior under circumstances in which individual interests are at odds with common interests—circumstances characterized as social dilemmas—remains a largely unsolved, multidisciplinary puzzle. Approaches to these types of problems have, for the most part, been applications of evolutionary game theory. While the study of networks, complex systems, and nonlinear dynamics has pervaded most scientific disciplines, the application of related tools to the study of social dilemmas represents a very new, but extremely promising means of shedding light on the quandary of cooperation. In this work, we situate agents engaging in social dilemma games on complex social networks, allowing us to more fully investigate the impact of average degree and degree variance, or heterogeneity of degree, on the evolution of pro-social behavior. Our results suggest that increasing homogeneity of degree produces network effects that make the emergence of pro-social behaviors more likely thereby increasing overall social welfare. As such, homogeneity of degree is properly thought of as a collective good.
Industrial ecology is a rapidly developing field of research and practice in which the sustainability of industrial systems is thought to be improved through closing of material and energy loops among firms. In this paper, I look at the developing practice around this concept from a self-organization perspective. A central question is the extent to which closing of material loops has to be planned and guided by governmental agencies. Based on a longitudinal case study of industrial ecology development in the Rotterdam harbor area (the Netherlands), the interplay between self-organization, external control, and vision development is analyzed.
This article approaches the spatial development of the port of Rotterdam in the Netherlands from a coevolutionary point of view. We use two main concepts within coevolutionary framework; bounded instability and punctuated equilibrium, to understand the relationship between Dutch spatial policies and actual developments in the port of Rotterdam. We observe that the actual port system is generally more diverse than the public policy that governs it, and that the policy appears to simply follow and codify port developments. This result negates the assumption that spatial developments in the port of Rotterdam are steered and planned through public policy and raises several questions on the role of such policy initiatives.
This paper sets out how models from natural science can be used within the management domain. We contend that this transformation between domains is best served by agent-based models, where the agent behavior is important, not the specifics of the agent type. We also note that these models are useful for exploring complexity and extending the research that has been performed within management to date. We demonstrate this with two models: the NK model, a theoretical biology model that has had 10 years of development within the strategy field, and the Forest Fire model, a model from physics that is at an early stage within its application within the management domain. In doing so, we also focus on the specific issues that need to be addressed when applying and extending these models to management studies due to the ontological differences between the realms of natural science and social science.
Organizations can be—and, have been—modeled as rule-based systems. On a reductive view, the resulting models depict organizations as cellular automata (CA) that carry out computations whose inputs are the initial and boundary conditions of a lattice of elements co-evolving according to deterministic interaction rules and whose outputs are the final states of the CA lattice. We use such models to refine the notion of the complexity of an organizational phenomenon and entertain the notion of an organization as a universal computer that can support a wide variety of CA to suggest ways in which CA-derived insights can inform organizational analysis. We examine the informational and computational properties of CA rules and the implications of the trade-off between their informational and computational complexity to the problem of ‘organizational design’ and show how the discovery of operational rules could proceed in the context of an empirical framework.
The traditional view of conflict, as a problematic condition always requiring reduction or elimination and whose conditions or outcomes can be predicted, is incompatible with a complex adaptive systems view of organizations. Thus, conventional approaches to reducing conflict are often futile because the fundamental properties of complex adaptive systems are the source of much organizational ‘conflict.’ In this paper we offer an alternative view of conflict as pattern fluctuations in complex adaptive systems. Rather than needing reduction or elimination, conflict is the fuel that drives system growth and enables learning and adaptive behaviors, making innovation possible. Instead of focusing on conflict reduction, managers are advised to encourage mindfulness, improvisation, and reconfiguration as responses to conflict that enable learning and effective adaptation.
This paper explores the application of new approaches in organizational development and institutional economics to a communicative design process with application in design of social systems. Theory from four authors is investigated and applied to a generalized case study.
Several studies have suggested that it is difficult to manage projects using the traditional model of project management. Researchers have proposed multiple perspectives to identify and manage such projects. This paper provides a perspective based on a complexity theory framework. Since a project exhibits the characteristics of a complex system, we postulate that the method to manage such a project is embedded in its contextual history. Such a method cannot be predicted a priori but will rather emerge from the interactions between the project elements and the environment.
This paper addresses the issue of change in organizations in the new conditions of the contemporary world. We argue that linear theories and models still dominant in organizational sciences are inadequate to understand different modalities of change today. We deploy Prigogine’s concept of far-from-equilibrium dynamics, Heisenberg’s Uncertainty Principle, and Zadeh’s fuzzy logic, to develop more complex and adequate ideas of change in organizations. We show the value of these ideas for organization studies and theories of the “postmodern” world, illustrating their explanatory power by analyzing aspects of the success and failure of Enron, as a case study of organizational change in a chaotic world.
This paper explores the concepts of organizational knowledge and intelligence from the perspective of new systems theory. It draws particularly on Niklas Luhmann’s theory of social systems, George Spencer-Brown’s calculus of distinctions, and Dirk Baecker’s applications of the two to questions of management. According to this view, knowledge can be conceptualized as a structure that determines the way in which information is dealt with. In other words, knowledge is a structure that determines whether a difference makes a difference and, if so, what difference it makes. Knowledge thus means selection; and selection implies contingency — one could have selected differently. The selectivity of knowledge, however, remains latent. That, and what knowledge excludes, is not included in the knowledge. Knowledge, thus, inevitably implies nonknowledge as its other, or “dark,” side. Intelligence can be conceptualized in relation to knowledge. It can be understood as the ability to deal with the other side of knowledge — to deal with nonknowledge. According to this view, an organization is intelligent to the extent that it is aware of its nonknowledge and takes account of this nonknowledge in its operations. In terms of Spencer-Brown’s theory, intelligence appears as the re-entry of nonknowledge into knowledge. Three examples of forms of organizational intelligence are presented in this paper: inter-organizational networks, heterarchy, and organizational interaction.
On the basis of the author’s latest book, Systemic Planning, this paper addresses systems thinking and complexity in the context of planning. Specifically, renewal of planning thinking on this background is set out as so-called systemic planning (SP). The principal concern of SP is to provide principles and methodology that can be helpful for planning under circumstances characterized by complexity and uncertainty. It is argued that compared to conventional planning — referred to as systematic planning — there is a need for a wider, more systemic approach to planning that is better suited to current real-world planning problems, often characterized by complex issues.
In cases of flooding many authorities and organizations become involved and it can be a problem to take in the whole situation and have a common picture when many incidents are happening at the same time. There is also a lack of efficient tools that show critical buildings and constructions in combination with actual and forecasted water levels. When handling critical situations people face challenges of complexity, uncertainty, and unpredictably. Such management and decision-making activities are normally supported by various models and support tools. However, complexity is normally not explicitly addressed in such models and tools. In this paper we analyze and discuss different kinds of complexity, which are a challenge in critical situations caused by flooding.
Healthcare can be characterized as a complex adaptive system. New Zealand is recognized as having one of the highest rates of enmeshed clinical information and communication technology within this complex system. This paper describes the implementation of an integrated series of electronic clinical health knowledge management systems in a large New Zealand District Health Board. In combination with standard project management, the core implementation team utilized an action research reflective learning approach to enhance their capability to cope with emergent issues, and plan for each subsequent project stage. The emergent focus on “process” issues of connectedness, competency, and control were not the “technical” concerns the principal author was initially expecting, but can be understood through an appreciation of individual and group dynamics, system and complexity theories. In particular, mutual empathy for both self and others was identified as a core capability requirement to cope with the inherent ambiguity within complex systems.
Peer pressure can induce sudden, unexpected changes in the behavior of a group. With agent-based simulations, we study the impact of one individual on the behavior of a social network of people. We find that an individual with the largest benefit dominates the group behavior. If that individual happens to have a leadership role, the impact is particularly strong. The model suggests that even if the average benefit for the group changes slowly, the average participation changes suddenly but with a delay. The delay is shorter if the network is subject to large, unpredictable outside influences. Further, we find that incentives that target leaders are more effective than unspecific incentives. We discuss applications of the model to the dynamics of membership in an agricultural youth organization.
This paper presents a study of leadership using a method of computational modeling by exploiting the analysis of data and behaviors gathered in a human subject experiment. We incorporate some of the observed behaviors in agents’ schemata and build an agent-based model. Analyzing the different interactions, we can examine under what conditions individuals may emerge as effective leaders.
In recent times, the study of complex systems and complex-systems thinking has influenced research and approaches to research in business. While business has benefited from this influence, this paper suggests that ideas from the study of management can, and should, be applied to the study of complex systems. In particular, much of the complex-system literature is oriented around self-organization, in which individual agents in a system organize themselves, with no external influence, in such a way as to produce interesting and useful emergent system behaviors. In most business organizations, however, central organization plays a significant role in the organization’s performance. It is suggested here through numerous examples and a naïve mathematical model that the study of complex systems could benefit from examining the role and impact of central organization, and leadership in particular.
The postmodern organization has a design paradox in which leaders are concerned with efficiency and control as well as complex functioning. Traditional leadership theory has limited applicability to postmodern organizations as it is mainly focused on efficiency and control. As a result, a new theory of leadership that recognizes the design paradox has been proposed: complexity leadership theory. This theory conceptualizes the integration of formal leadership roles with complex functioning. Our particular focus is on leadership style and its effect as an enabler of complex functioning. We introduce dynamic network analysis, a new methodology for modeling and analyzing organizations as complex adaptive networks. Dynamic network analysis is a methodology that quantifies complexity leadership theory. Data was collected from a real-world network organization and dynamic network analysis used to explore the effects of leadership style as an enabler of complex functioning. Results and implications are discussed in relation to leadership theory and practice.
Introduction It is something of a marvel to recognize just how much of what is only now coming forth concerning leadership by means of complex systems research was already anticipated in the carefully considered insights published by Chester Bernard as far back as 1938. In his book, The Functions of the Executive, Barnard described an […]
The biological sciences have contributed an extensive number of studies of efforts to resolve chronic pain and an expanding body of research, focusing on the psycho-social aspects of chronic pain, is now also evident. Paradigms applied to chronic pain appear to compete and lack an integrative framework. This paper builds a case for framing chronic pain within a complex adaptive systems perspective. Characteristics of complex systems are illustrated with examples from within the experience of chronic pain. It is proposed that a complexity science paradigm can serve as a meta-framework, integrating theoretical models employed in chronic pain and reframing dissent and conflict as positive generative forces for change. Interventions, based on complexity science principles, can effect change in the highly interactive systems that constitute the chronic pain experience.