Complex adaptive system

About: Complex adaptive system is a research topic. Over the lifetime, 3190 publications have been published within this topic receiving 111947 citations. The topic is also known as: Complex adaptive system, CAS.

A Complex Systems Approach to Understanding and Combating the Obesity Epidemic

Abstract: Publisher Summary Obesity has become an important public health crisis worldwide. The obesity epidemic urgently requires well-crafted policy interventions but also represents an especially challenging problem for study and for policy design due to its complexity. Many of its features—breadth of scale, diversity in actors, and multiplicity of mechanisms—are hallmarks of a complex adaptive system. The lessons and tools of complexity science can help to understand and to combat the obesity epidemic, but only if complexity is taken seriously and the problem of obesity is approached from a systems viewpoint. Agent-based computational modeling (ABM) is an especially promising avenue for future research and for policy exploration. This is a powerful and relatively new approach in which complex dynamics are modeled by constructing “artificial societies” on computers. In an ABM, every individual actor (or “agent”) in the system is explicitly represented in computer code. These agents are placed in a spatial context with specified starting conditions and are given a set of adaptive rules for interaction with each other and with their environment. The agents' decision processes and their interactions produce the output for agents themselves and for the system as a whole. In this way, the computer simulation “grows” macro-level patterns and trends from the bottom up.

How do Co-agents Actively Regulate their Collective Behavior States?

Abstract: Collective capability of producing patterned collective behaviors is one important field of research in work psychology (e.g., shared cognition approach, Fiore and Salas, 2004; interactive team cognition approach, Cooke et al., 2013), neurosciences (e.g., social neuromarkers, Tognoli et al., in press; neurological mirroring, Waldman et al., 2015), sociology (Miller, 2013), or human movement science (e.g., joint movement, Schmidt and Richardson, 2008; team behavior, Araujo and Bourbousson, 2016). Within this stream of research, one neglected topic has been to conceptualize how interactors regulate online their dynamic involvement in collective activity, which is the individual skillful activity of adjusting online to the needs of the collective behavior. Grounded in of the hypothesis that collective behavior emerges from a self-organized complex system, the present opinion discusses the nature of the active regulation of the interactions performed by the co-agents. A deeper grasp of this regulation process is needed to understand how and why interpersonal co-ordination forms, stabilizes and/or is destroyed, leading to the emergence of high order phenomena at the team scale that are not fully predictable from the individual activities that compose the social system under study. Collective behavior is deemed here to constitute the property of a social system composed of living entities. In research that has considered collective behavior as emerging from a self-organized complex system, an important focus has been on the between-agents' interactions, supported by an informational flow that binds agents (e.g., Schmidt et al., 1990). In this stream of research, information is defined as an ambient energy that disturbs the agent, depending on his current activity (Varela et al., 1991). From the (interpersonal) informational flow, individual activities can be entrained, mutually affected by others' movements, so that the emerging collective behavior cannot be conceived out of either the nature or the content (i.e., being non-representational) of such a flow (e.g., Kelso, 1994; Lagarde and Kelso, 2006; Richardson et al., 2007). However, while between-agents informational flow has been considered the main binding mechanism that makes collective behavior emerge, we aim at pointing out that the way individuals manage their interaction in the real-time mainly has been theoretically presupposed rather than empirically investigated. We will use empirical and logical evidence to highlight shortcomings in the actual theorizations of the way individual movements merge into a collective unit. In our opinion, current research should restrict the importance of the co-regulation and the local couplings hypotheses. Both hypotheses appear unsatisfactory to us, and might probably be refined through a further consideration of the social system's size effects as a main topic.

Reciprocity and Its Limits: Considerations for a Study of the Pre-Hispanic Pueblo World

Abstract: Reciprocity is an ancient and important social practice that evolved in very small-scale societies. In this paper we present an abstract model of the way systems work that are organized through balanced reciprocity (Sahlins 1972:185-275). This model will help us understand the general nature of the problems that must be overcome by societies organized through reciprocity as they grow in size. We then suggest that, in the late pre-Hispanic period, the eastern and western Pueblo worlds took different paths to overcoming these difficulties. Finally, at the invitation of the editor, we discuss the other papers in this volume that deal with the pre-Hispanic Pueblo world in light of this model. The first part of the paper draws on theory from complex adaptive systems research, specifically the random Boolean network (RBN) model (Kauffman 1993 and elsewhere). Complex adaptive systems (CAS) research is the study of how many interacting and often adaptive agents, each of which may have access to only local information and each of which may be responding to quite simple rules, as an ensemble produce higher-order patterns and structures. Wills et al. (1994) provide an overview of several major strands of CAS theory from a southwestern archaeological perspective (see also Kohler 1993). Much recent anthropology attempts to generate thicker and more detailed descriptions of particular local societies to achieve a deeper understanding of their nature and operation. CAS approaches, on the other hand, are unabashedly abstract; they attempt to view adaptive systems of all kinds from a distance to perceive their similarities and differences. Typically, they locate causality in the manner in which entities simultaneously interact. Although such models strive for generality rather than for the realism or precision that are more traditionally honored in our field, we believe they can be used productively nonetheless. To appear in Alternative Leadership Strategies in the Greater Southwest, edited by Barbara Mills. University of Arizona Press, 2000.

Incorporating Complex Adaptive Systems Theory into Strategic Planning: The Sierra Nevada Conservancy

Abstract: Conservation organizations rely increasingly on integrated planning approaches that explicitly address social and economic goals while pursuing ecological conservation. Moreover, the spatial and temporal scale at which these organizations operate is growing. The Sierra Nevada Conservancy, established as a new state agency by California legislation in 2004 to pursue social, economic and ecological sustainability across a 25 million acre region, exemplifies this large-scale, integrated approach. Therefore, the new agency faces a complex set of policy objectives that must be pursued across a widely varying geography of social, economic and ecological conditions. Using the Conservancy's fire management program area as an example, the paper illustrates how application of an analytic framework from complex adaptive systems theory can guide the Conservancy to deploy its resources more effectively than broader-scale application of a single, agency-wide strategy relying on a more static model. Therefore, ...