Trajectories to Complexity in Artificial Societies: Rationality, Belief, and Emotions

Abstract

This chapter illustrates and discusses the use of agent-based artificial societies to explore possible trajectories into social complexity through the integration of ideas from both anthropology and agent technology. Particular attention is paid to the role of rational cooperation, collective belief, and emotional dynamics in these trajectories. Some methodological problems associated with the use of artificial societies to build social theory are also discussed, especially how best to reduce the impact of our own cultural preconceptions. Computer simulation work in archaeology and anthropology is more than 25 years old (see Doran and Hodson 1975, chapter 11; Doran 1990; and compare Halpin to appear). After a period of enthusiasm in the early 1980s interest waned, but recently there have been a number of important computer-based studies of (human) social phenomena using so-called agent-based modeling (e.g., Kohler et al. this volume) and agent-based artificial societies (e.g., Epstein and Axtell 1996), and more are in progress. Both types of study involve (software) agents, that is, according to a standard textbook definition, entities which perceive and act in an environment (Russell and Norvig 1995:49). Reactive agents are typically built around a small number of relatively simple situation-to-action rules. Deliberative agents are more complex, typically posting goals and then forming and executing plans to achieve them. It is this rapidly developing "agent technology," largely based upon artificial intelligence studies, that is the driving force behind the new work. The methodology associated with both agent-based modeling and agentbased artificial societies emphasizes the ability to address explicitly processes of cognition, and hence phenomena that previous models could not tackle, and also the ability to explore what could happen rather than what has happened or is happening. However, unlike agent-based modeling, artificial societies are, in essence, models without a specific target system, and it has been argued that this type of modeling permits the study of societies and their processes in the abstract (Epstein and Axtell 1996; Doran 1997). An underlying assumption is that it is possible and useful for social scientists to explore wide-ranging and abstract social theories and that these theories can be expressed in terms of computational processes.