Complexity and Anthropology

Abstract

This chapter discusses the application of “complexity” into anthropology studies. By tracing patterns of interaction among the elements of a system, one can sometimes discover emergent properties at a higher level. This phenomenon is common to all of the examples, from semiotics to agency, innovation, cultural evolution and human-environmental interactions. But, until recently, mathematical tools were not well suited to investigate emergence, or other properties of out-of-equilibrium dynamical systems. Recently, it has been argued that social scientists who wish to take advantage of mathematics have the choice of only two approached. The first is essentially Newtonian and is best represented by general equilibrium theories, for example in economics. Such theories take the form of systems of differential equations describing the behavior of simple homogeneous social actors. Change occurs as a result of perturbations and merely leads from one equilibrium state to another. The second type of theory is statistical. If one cannot write the equations to define a dynamical system, it may yet be possible to observe statistical regularities in social phenomena. Both approaches have obvious weaknesses: the assumption of equilibrium is forced by the mathematics, not by the observation of social behavior, and sifting for patterns with descriptive statistics is at best an indirect method for discovering causal or developmental relationships. The landscape looks very different today. Out-of-equilibrium systems are the main topic of complexity research, and there has been a proliferation of new analytical methods to investigate them. Of particular interest to anthropologists are the models of “artificial societies”, of which the “Bali Model” is an example. Artificial societies are computational models comprising populations of social agents that flourish in artificial environments.