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Complex Adaptive Systems: An Introduction To Co...



A complex adaptive system is a system that is complex in that it is a dynamic network of interactions, but the behavior of the ensemble may not be predictable according to the behavior of the components. It is adaptive in that the individual and collective behavior mutate and self-organize corresponding to the change-initiating micro-event or collection of events.[1][2][3] It is a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in order to adapt to the changing environment and increase their survivability as a macro-structure.[1][2][4] The Complex Adaptive Systems approach builds on replicator dynamics.[5]




Complex Adaptive Systems: An Introduction to Co...



The study of complex adaptive systems, a subset of nonlinear dynamical systems,[6] is an interdisciplinary matter that attempts to blend insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents, phase transition, and emergent behavior.[7]


The term complex adaptive system was coined in 1968 by sociologist Walter F. Buckley[19][20] who proposed a model of cultural evolution which regards psychological and socio-cultural systems as analogous with biological species.[21] In the modern context, complex adaptive system is sometimes linked to memetics,[22] or proposed as a reformulation of memetics.[23] Michael D. Cohen and Robert Axelrod however argue the approach is not social Darwinism or sociobiology because, even though the concepts of variation, interaction and selection can be applied to modelling 'populations of business strategies', for example, the detailed evolutionary mechanisms are often distinctly unbiological.[24] As such, complex adaptive system is more similar to Richard Dawkins's idea of replicators.[24][25][26]


What distinguishes a CAS from a pure multi-agent system (MAS) is the focus on top-level properties and features like self-similarity, complexity, emergence and self-organization. A MAS is defined as a system composed of multiple interacting agents; whereas in CAS, the agents as well as the system are adaptive and the system is self-similar. A CAS is a complex, self-similar collectivity of interacting, adaptive agents. Complex Adaptive Systems are characterized by a high degree of adaptive capacity, giving them resilience in the face of perturbation.


Turner and Baker synthesized the characteristics of complex adaptive systems from the literature and tested these characteristics in the context of creativity and innovation.[29] Each of these eight characteristics had been shown to be present in the creativity and innovative processes:


Living organisms are complex adaptive systems. Although complexity is hard to quantify in biology, evolution has produced some remarkably complex organisms.[43] This observation has led to the common misconception of evolution being progressive and leading towards what are viewed as "higher organisms".[44]


This special issue is both the outcome of a strong competition among the papers submitted after the conference and the result of a thematic focus of the editors on a core issue of evolutionary economics namely co-evolution and complex adaptive systems. The contributions selected show the scope of analysis in evolutionary economics as well as the explanatory power with respect to economic dynamics and long term economic development.


All contributions to this special issue join the focus on complex adaptive systems as an adequate framework for economic analysis. The contributions make clear how far the evolutionary complex methodology is developed and how rich the explanatory power of economic analysis can be with the right instruments: Changes of the system like innovation-driven economic development or economic crisis become endogenous phenomena which can be analyzed immediately without exogenous shocks and/or the application of restrictive assumptions.


Introduction: Despite over two decades of international experience and research on health systems integration, integrated care has not developed widely. We hypothesized that part of the problem may lie in how we conceptualize the integration process and the complex systems within which integrated care is enacted. This study aims to contribute to discourse regarding the relevance and utility of a complex-adaptive systems (CAS) perspective on integrated care.


Discussion and conclusion: One possible explanation for the lack of systems change towards integration is that we have failed to treat the healthcare system as complex-adaptive. The data suggest that future integration initiatives must be anchored in a CAS perspective, and focus on building the system's capacity to self-organize. We conclude that integrating care requires policies and management practices that promote system awareness, relationship-building and information-sharing, and that recognize change as an evolving learning process rather than a series of programmatic steps.


A brief overview of the place of complexity in the historical development of SES, however, shows that this was not always the case. In summarizing the development of the concept of resilience, Folke (2006, 2016) explains how early work during the 1980s in the field of adaptive ecosystem management, initially relied on dynamical systems theory to argue in favor of more dynamic models to analyze ecosystem structures and behavior. This was an attempt to understand how institutions and the people associated with them should be organized and managed. However, during the 1990s, a series of publications introduced the idea that ecological systems should be reframed as being complex and adaptive in nature.


During the years that followed, the argument that social and ecological systems can be seen as interactive, linked systems was developed and strengthened by drawing on a comprehensive understanding of the characteristics and dynamics of CAS (Berkes and Folke 1998, Berkes et al. 2003, Gunderson and Holling 2002). Traditional conceptualizations in ecology ignored the fact that living systems operate under far-from-equilibrium conditions (Holling 1973, Prigogine and Stengers 1984) and viewed human systems only as external drivers of ecosystems, and conversely, economics and other social sciences generally viewed natural systems as nondynamic resources for extracting capital gains or providing the basis for livelihoods. The gradual conceptual development from separate environmental and human systems to an understanding of intertwined complex adaptive SES has changed how these relations and interactions are viewed and subsequently studied, modeled, and governed (Folke et al. 2005, Duit and Galaz 2008). The perception of SES as integrated systems has become the basis of a mainstream approach in the pursuit of addressing the challenges of navigating toward more just and sustainable futures for humanity and the Earth (Folke et al. 2011, 2016, Biggs et al. 2012, Levin et al. 2013, Fischer et al. 2015).


The six organizing principles contribute to a general CAS-based ontology for observing and studying SES and include the following underlying causal explanations of CAS features: (1) CAS are constituted relationally; (2) CAS have adaptive capacities; (3) CAS behavior comes about as a result of dynamic processes; (4) CAS are radically open; (5) CAS are determined contextually; and (6) novel qualities emerge through complex causality. The classification is based on an ontological reading of CAS to discern general patterns and underlying causal explanations (Gnoli and Poli 2004). The principles listed in Table 2, therefore, assume that the features and dynamics of CAS are a feature of the real world and not the result of our limited understanding of complex phenomena (Capra 2005, Poli 2013). Our conceptual classification aims to describe CAS in terms of a combination of their structure and causations (Poli and Seibt 2010). Structure includes the constituent parts (material and nonmaterial) as well as the forces that organize part-whole relations. Causations can be viewed as interactions that allow elements to act upon and influence one another. The six organizing principles are neither a new revised set of definitions of CAS, nor a new set of properties of observed (as opposed to modeled) systems or new normative guidelines concerning how SES should be analyzed.


Relational networks do not constitute only material structures (Capra 2005), but are functional networks of relationships that come about as the result of interactive patterns of processes. Systems can also be connected to, or nested in, other systems, representing hierarchies of relations at different scales (Holland 1995, Cilliers 1998, Levin 1999). Recognizing that relations form networks of causal effects that are generative of complex structures and processes, implies that CAS are brought about through process-dependent interactions on multiple scales. These interactions allow CAS to self-organize and produce adaptive, dynamic, and emergent behavioral patterns (Folke 2006).


These six organizing principles form the foundation for conceptualizing a CAS-based ontology and offer an alternative to that of Newtonian metaphysics by giving ontological legitimacy to the relations and emergent, nonlinear organizing processes that constitute CAS. Ontological complexity implies that emergent properties and patterns of behavior are real and do not exist independently from the parts or agents that constitute these phenomena (Casti 1997, Preiser and Cilliers 2010). A complexity-based ontology recognizes both the functional and relational dependency between structurally integrated components of the system and the systemic environment or context. The properties of CAS come about and change because of the interplay between the adaptive responses of the components, the emergent properties of the whole, and the context in which they operate. Simultaneous bottom-up, top-down, multilevel interactions between different spatial, as well as temporal scales, result in the codetermination of CAS structures and patterns of behavior that emerge over time (Levin et al. 2013). 041b061a72


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