The Adaptive Systems Theory of Social Orders (2009)

Review of General Psychology, 1997

This article examines applications of complexity theory within the behavioral and social sciences. Specific attention is given to the fundamental characteristics of complex adaptive systems (CAS)—such as individuals, groups, and societies—including the underlying structure of CAS, the internal dynamics of evolving CAS, and how CAS respond to their environment. Examples drawn from psychology, sociology, economics, and political science include attitude formation, majority–minority relations, social networks, family systems, psychotherapy, norm formation, organizational development, coalition formation, economic instabilities, urban development, the electoral process, political transitions, international relations, social movements, drug policy, and criminal behavior. The discussion also addresses the obstacles to implementing the CAS perspective in the behavioral and social sciences and implications for research methodology.

2006

Social systems must fulfil four basic functions to ensure their survival in competitive conditions. Social systems must provide for: (1) energy and other necessities of life, (2) security against external and internal threats, (3) identity and self-development, and (4) consistency and direction. These functions result in four more or less autonomous aspect systems; these aspect systems interact. Between the variables of basic functions and variables of aspect systems, a minimal level of consistency is required to facilitate growth and development, and to ensure the (future) survivability of the social system. Sooner or later, growth, change, and differentiated development result in inconsistencies within/between basic functions and aspect systems. These inconsistencies affect the effectiveness and efficiency of these social systems to provide for the basic functions. Periodically, deliberate or spontaneous corrective adjustments of social systems are required, to correct for these inconsistencies.

The seven criteria of complexity are applied to social systems, defined as systems in which dominant agents are humans, with a view to determining which types of social systems are complex. Complex social systems are further analysed and their key fea-tures, emergent intelligence, emergent creativity and emergent leadership are dis-cussed. The key argument of the paper is that complex social systems cannot be con-trolled and must be designed to exhibit self-organization. A practical approach to de-signing self-organizing social systems, based on author’s substantial experience, is outlined.

Human Development

Can the study of epigenetics, physiology and cognitive science contribute to the investigation and understanding of social-cultural systems while respecting the autonomy of social research? I present a developmental system theory (DST) approach, which takes the unit of analysis to be the system of self-sustaining interactions among multiple biological, psychological and social resources. On this view, the cybernetic architecture of the networks that constitute the system channels development, so that different trajectories lead to convergent end-states, accounting for the system’s developmental stability, as well as shedding light on the conditions that lead to departures from typical outcomes. Based on previous work, which is extended here, I suggest that Waddington’s epigenetic landscape metaphor, which was built to illustrate the relationship between genetic networks and embryological development is a useful tool for thinking about the temporal dynamics of social systems, capturi...

Proceedings of Intersymp 1999 the 11th International Conference on Systems Research Informatics and Cybernetics Baden Baden Germany, 1999

This paper assumes that the world we live in consists of many interwoven complex adaptive systems. In the literature such systems are characterized as comprising many moving parts and processes that interact significantly in a mostly non-linear manner. It is argued that the forces that act on such complex systems are not additive, and that their impact occurs along non-linear interactions within the system. In particular, many of these systems are adaptive in that they change their behavior (through their interactions) over time, so that if they are subjected to a similar force or event a second time they may react in a substantially different manner. Over the past several decades a number of researchers have been intrigued by the abundance of complex adaptive systems in both the natural and human world (Holland 1995, Cowan et al. 1994, Kauffman 1992). In the natural world these include the human brain, immune systems, ecologies, cells, and many others. In the human world, where it is well known that the interaction of just two persons can reach a high level of complexity, they include cultural and social systems (Figs. 1 and 2). The paper describes the behavioral characteristics of complex adaptive systems, as they have been identified in the literature, and briefly discusses how these characteristics appear to manifest themselves in the evolution of technology, biology, and the economy. The influence of complex adaptive system notions on the structure and operation of a business organization is addressed in respect to decentralization, diversification, communication, and organizational flexibility. Attention is drawn to the inadequacies of existing quantitative tools and the opportunities that exist for leveraging human behavioral characteristics and organizational capabilities in a complex adaptive systems environment.

Los Alamos National Laboratory Report, 2013

How do collective processes in decentralized, self-organizing systems respond to environmental change? What are the contrasting roles of collective structures and innovative components (variation, diversity, entropy), and how do these roles change with different rates of environmental change? To answer these questions, a simple self-organizing system is examined – a simulation of foraging for food by ants in the presence of environmental change. This model system has been argued to be similar in dynamics to decentralized components of many collective systems - ecologies, economies, knowledge systems, societies, political systems, etc. The simulations are first shown to illustrate a developmental view of evolving systems, captured by the developmental stages of Formative, Co-Operational and Condensed. The effects of different rates of environmental change are then presented. For small rates of change, the system productivity is largely unchanged. As the rate increases, innovative information becomes more important to sustaining productivity. As the rate further increases, episodic failure is observed as stabilizing collective structures fail, and the system regresses to earlier developmental stages. The collective structures are shown to inhibit the performance of the system as a whole in rapidly changing environments. A quantitative measure is developed that captures the efficacy of the collective structure. A variation of the system with a mechanism for sustaining collective structures is found to be more sensitive to environmental change, duplicating the decline in productivity observed in aging systems. NetLogo example available.

Annales d'Economie et de …, 2007

the paper first introduces a general dynamic setup that embodies abstract formulations of the following two forces: (a) homophily, i.e. the idea that networking is favored by similarity in behavior; (b) conformity, i.e. the pressure towards similar behavior induced by interaction. this general framework is then specialized into three alternative directions, corresponding to different specific manifestations of those two forces. For each such particularization of the general model, we find that (i) sharp transitions, (ii) hysteresis, and (iii) equilibrium multiplicity are salient characteristics of the long-run social dynamics. since features (i)-(iii) are often reported for a variety of social (network) phenomena where (a)-(b) play an important role, we sugget that the former may indeed be the result of some common mechanism at work that relies on the interplay of the latter.

Iss 2 numbers') and the development of quantitative mechanistic theory to explain how these structures evolved, theory that must be derived from the first principles of physics, chemistry, and biology, and so internally consistent across the sciences. Now that we understand the ubiquity of network structures in human social organization, we need to explore what this means for understanding the ecological and evolutionary dynamics of human systems, and the role of more fundamental scientific processes in these dynamics.

2020

This text discusses ideas about the relationship between the DAAI paradigm and the concept of complex systems as described in the book of Miller and Page (2007) [MP07]. Miller1 and Page2 are both rooted in the mind-web of the Santa Fe Institute.3. In this review the methodological aspects of the theory will be analyzed and discussed. This includes chapters 1-7. Another important part of the book the examples illustrating the main theoretical view from chapter 7 11 will not be discussed here. ∗Copyright 2019-2020 by eJournal uffmm.org, ISSN 2567-6458, Publication date:October1, 2019 until February-1 2020 https://www.cmu.edu/dietrich/sds/people/faculty/john-miller.html https://sites.lsa.umich.edu/scottepage/ For the history and vision of the Santa Fe Institute see https://www.santafe.edu/ about/history

Discrete Dynamics in Nature and Society - DISCRETE DYN NAT SOC, 2001

Computation of complexity in Social Dependence Networks is an interesting research domain to understand evolution processes and group exchange dynamics in natural and artificial intelligent Multi-Agent Systems. We perform an agent-based simulation by NET-PLEX (Conte and Pistolesi, 2000), a new software system able both to build interdependence networks tipically emerging in Multi-Agent System scenarios and to investigate complexity phenomena, i.e., unstability and state-transitions like Hopf b!['urcation (Nowak and Lewenstein, 1994), and to describe social self-organization phenomena emerging in these artificial social systems by means of complexity measures similar to those introduced by Hubermann and Hogg (1986). By performing analysis of complexity in these kind of artificial societies we observed interesting phenomena in emerging organizations that suggest state-transitions induced by critical configurations of parameters describing the social system similar to those observed in many studies on state-transitions in bifurcation chaos (Schuster, 1988; Ruelle, 1989).