Big History might be considered the study of an evolving, large, complex adaptive system with three very different phases progressing geometrically from the early universe to the present day. A geometrical progression rate would suggest transitions to life evolution beginning at about 5 billion years ago; to brain evolution around 5 million years ago; and further transition to technological civilization development about 5,000 years ago. Characteristic properties of complex adaptive systems include: (1) a resource which drives the level of complexity, such as energy flow; (2) new options at critical nonlinear decision points along development paths tied to levels of energy flow; and (3) continuous logistic learning as the options are explored; (4) scaling of other dimensions besides energy, such as length and time scales of important processes. This paper presents indications that these processes are occurring through historical trends in energy, environment, economics, and organization. The understanding of these phenomena could contribute to our ability to develop and anticipate potential future scenarios with more integrated, systemic, and effective approaches and expectations.
This paper applies systems thinking and emergence theory to present an understanding of sustainability in terms of the human actions and attitudes required for sustainability to emerge. Sustainability is viewed as an emergent quality that occurs when the interactions within the system, and between the system and its environment are nourishing. We suggest this conception is useful because it indicates the kinds of relationships individuals and groups need to engage in as actors; the responsibilities and importance of observers in recognising emergent patterns; and the significance of the relationship between the actor and observer scales. We aim to identify strategies in these three areas that can best facilitate the emergence of sustainability. Emergence theory is found to be a fruitful framework for generating solutions and stimulating new thinking about defining, monitoring, or acting for sustainability.
Complexity scholars have identified two distinct catalysts of emergence: (1) Far-from-equilibrium dynamics that trigger order creation, and (2) adaptive tension (McKelvey, 2004) which can push a system toward instability, leading to the emergence of new order. Each of these provides a necessary but incomplete explanation of the catalyst for emergent order. In particular, the far-from-equilibrium framework, when taken to its logical ends, would conclude that most dynamic and fluid organizations are the ones farthest-from-thermodynamic equilibrium—like Exxon or GM, for example. Adaptive tension on the other hand identifies an exogenous force of market change, but doesn’t explain how emergence is actually triggered. As a solution I propose “Opportunity Tension,” which integrates the endogenous intention of an entrepreneur to create a new venture to the exogenous changes that open up an entrepreneurial opportunity—a market that will exchange money for the value being created. Opportunity tension occurs in “pulses,” each cycle leading to a new dynamic state of the system. This model, which is consonant with the notion of “dynamic creation” (Chiles et al., 2010), contributes to a complexity science that is moves us beyond a far-from-equilibrium framework.
As more scholars join the conversation around complexity theory (CT), it seems a useful time to ask ourselves if we are talking about the “same thing?” This concern is highlighted by the present survey, which finds more conflict than agreement between definitions. In contrast to the conflict, a path toward common ground may be found by applying the idea of a “robust” theory. A robust theory is expected to be more effective in application and more reasonably falsifiable. In this paper, Reflexive Dimensional Analysis (RDA) is used to analyze existing definitions of CT. These definitions are deconstructed, redefined as scalar dimensions, combined, and investigated to identify co-causal relationships. The robustness of CT is identified as 0.56 on a scale of zero to one. Paths for advancing the theory are suggested, with important implications for complexity science.
We analyze four scenarios commonly encountered in social processes undergoing competitive pressures: resource depletion by individuals acting greedily (‘tragedy of the commons’), wasted opportunity due to over protective players (‘tragedy of the anti-commons’), crowd following (‘majority wins’) and competition for niches (‘minority wins’). We show that these scenarios are extremes of a continuous resource exploitation problem and that complex and counter-intuitive behaviors are found at the transitions between ‘pure’ scenarios. We discuss the likely community behaviors and under what conditions a centralised management intervention may play a role in the resource and community resilience.
Even in simple contexts, the dynamical interaction between agents creates complex features. The presence of agents of change affects dramatically the underlying social structure. Some agents seem to be important in shaping the evolution of interactions: traditionally, these agents have been referred to as leaders; nevertheless, recently scholarly interest has been attracted by social entrepreneurs. Do social leaders and social entrepreneurs act differently? Can a social entrepreneurship culture, one that aims for a large number of social entrepreneurs, be welcomed? This paper presents a model of interaction among agents in a community, and sheds light on the catalytic role that some individuals have on the social structure. The results provide some implications about the role of social entrepreneurs and the differences between social entrepreneurship and leadership.
Recognition that the reductionist approach to science leaves great gaps in our understanding has led to the synthesis approach to further explain the world around us. The synthetic approach examines the inter-relationships of individual entities as they interact to create complex networks. This approach spawned the creation of a new science—the study of Complex Systems. This article takes the concepts of Complexity Theory and hypothesizes a process of simple steps iterated many times over that explains the emergence of new entities and the evolution of our Universe. The concept of systems, emergence, iteration and evolution is proposed to explain the process underlying our evolving Universe. This process would be expected to leave fractal patterns in its wake. The fractal patterns are related to the shared tendencies for self-organization found in complex networks. The principles apply to all networks irrespective of their component parts and include both inanimate and living systems.
‘Understood complexity’ is a term of Albert Hirschman (1976) whose economic-political theory of ‘exit’ (‘vote with your feet’) versus ‘voice’ (feedback or use your influence for change) (1970), has often been used to (try to) understand whistleblowing (Alford, 2001; Maclagen, 1998). Real complexity is not linear and cannot be adequately studied an model of ‘A causes B’. Complexity entails ‘A causes B’ in a situation wherein ‘B causes A’. Bateson in his ‘ecology of the mind’ understood the circularity of the hermeneutic of complexity; while Weick did not in his theory of sense-making. I argue in this article, via an examination of a play of Ibsen, that circular thinking spiraling towards new insight(s) is much more a possibility of literature (studies) than of social science. Social complexity theory needs (at least partially) I believe to methodologically merge with literary studies.
This paper qualitatively illustrates how and why interdependence becomes significant in building coherent and sustainable network systems based upon human flourishing. Ethnographic case data of an icon tourism destination is provided to examine the structure, process and patterns that are essential for understanding network organization. The notion of fractals has been applied to more deeply understand the multi-dimensionality of networks. Through the fractal characteristic self-similarity, the data revealed aspects of volume-filling, reciprocity and enfoldment that were central to the transforming power of network organization. Behind the divisible there is always something indivisible. Behind the disputable there is always something indisputable. Chuang-Tzu
In the region of self-organized criticality (SOC) interdependency between multi-agent system components exists and slight changes in near-neighbor interactions can break the balance of equally poised options leading to transitions in system order. In this region, frequency of events of differing magnitudes exhibits a power law distribution. The aim of this paper was to investigate whether a power law distribution characterized attacker-defender interactions in team sports. For this purpose we observed attacker and defender in a dyadic sub-phase of rugby union near the try line. Videogrammetry was used to capture players’ motion over time as player locations were digitized. Power laws were calculated for the rate of change of players’ relative position. Data revealed that three emergent patterns from dyadic system interactions (i.e., try; unsuccessful tackle; effective tackle) displayed a power law distribution. Results suggested that pattern forming dynamics dyads in rugby union exhibited SOC. It was concluded that rugby union dyads evolve in SOC regions suggesting that players’ decisions and actions are governed by local interactions rules.
Modern turbulent business environments are characterized by rapid change that make businesses unpredictable, which brings emergence to the core of modern organizations. Deriving factors facilitating organizational emergence has been undertaken by drawing on complex adaptive systems (CAS) and social autopoiesis theories. Social autopoiesis was particularly chosen as it focuses on social elements, such as communication, morale, trust, etc. and their relation to social emergence, whereas CAS theory concentrates more on adaptive mechanisms that make a CAS produce emergent order, such as inter-relations, interactions, edge of chaos, feedback, etc. This led to the identification of various factors facilitating emergence and the development of a framework for utilizing these factors that were organized into two dimensions. First the factors are classified as either tangible or intangible. Second, the factors are classified as either dynamic, i.e., realize emergent properties, or they are concerned with the enabling infrastructure, i.e., enable the dynamic factors to become effective, or they are controlling factors, i.e., they attempt to balance excessive change with stability to prevent descent into chaos. The framework was applied to an Information Systems Development (ISD) project which showed that it is applicable to any type of business sector. This framework is argued to be a step forward to realize organizational emergence based on complexity principles derived from literature. The split between factors facilitating emergence and generic principles of CAS is not clear in the complexity literature and it is argued to be an important contribution of the paper.
It is possible to understanding the spatial behavior and structure of cities based on urban morphology alone. The units of analysis are urban clusters, defined as contiguous built-up urban areas instead of municipalities defined by politically determined boundaries. By means of historic data of the Tel-Aviv metropolis we present analyses of urban cluster statistics from 1935 to 2000. We focus on the largest cluster which includes the city of Tel-Aviv and several surrounding municipalities. The results suggest anomalies in the years 1964 and 1985. Based on the character of cities as self organizing systems, our study suggests that the analysis of urban cluster dynamics is an efficient tool to study urban phenomena.
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.