Sociocybernetics: Complexity, Autopoiesis, and Observation of Social Systems
R. Felix Geyer, J. van der Zouwen
Greenwood Press, Jan 1, 2001 - Business & Economics - 250 pages
In an effort to shed light on recent developments in sociocybernetic research, this volume represents recent and advanced thinking in this rapidly developing field. The authors address the core problems in social science caused by increasing societal complexity and analyze the inadequacy of many of the methodological tools still used for grappling with nonlinear, self-organizing systems. Together, the 18 contributors propose elements of a new methodology based on sociocybernetic principles aimed at describing and explaining the growth of societal complexity, the contribution of autopoiesis of societal subunits to more societal complexity, and the new simulation-based methodology needed to observe complex social systems. This unique volume contributes to a greater understanding of sociocybernetics and its uses as a method for researching modern problems of increasing complexity and interdependence.
The first part of the book deals with increasing societal complexity and contains chapters on its overall development, the complexity of brain-environment interaction loops, organizational change, the development of human values, and the increasing interpenetration of societal subsystems. The second part concentrates on a current issue in sociocybernetics: autopoiesis, or self-production. The chapters included in Part II concentrate on embodied cognition, on the applicability of autopoiesis to business firms, on its roots in Aristotelian philosophy, and on the possibility of societal control and steering in democratic societies. Part III, more focused on methodology, discusses the difficulties inherent in observing complex social systems. The chapters deal with the problems of cross-cultural comparative research, simulation of the evolution of social systems, longitudinal simulation of education systems, and the methodological difficulties associated with analyzing the unexpected complexities of mutually interacting nonlinear systems.
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