This sociological simulation uses the ideas of semiotics and symbolic interactionism to demonstrate how an appropriately developed associative memory in the minds of individuals on the microlevel can self-organize into macrolevel dissipative structures of societies such as racial cultural/economic classes, status symbols and fads. The associative memory used is based on an extension of the IAC neural network (the Interactive Activation and Competition network). Several IAC networks act together to form a society by virtue of their human-like properties of intuition and creativity. These properties give them the ability to create and understand signs, which lead to the macrolevel structures of society. This system is implemented in hierarchical object oriented container classes which facilitate change in deep structure. Graphs of general trends and an historical account of a simulation run of this dynamical system are presented.

This paper offers a novel approach to coevolution based on the sociological theory of symbolic interactionism. It provides a multi-agent computational model along with experimental results that suggest improved fitness, robustness, and knowledge due to emergent symbol systems. The main contribution of the symbolicinteractionist approach to coevolution is the concept of the emergence of a system in the abstract, where an interface between agents evolves. The interface is an emergent symbol system that focuses selective pressure among agents in ways that have been beneficial to agents as a whole in the past, creating a coevolving system that takes advantage of epistasis rather than having to prevent it. Global fitness thereby emerges from local, selfish interaction. The assignment of roles in this system is endogenous.

The first intelligent agent social model, created by the author in 1991, used tags with emergent meaning to simulate the emergence of institutions based on the principles of interpretive social science. This symbolic interactionist simulation program existed before Holland's Echo, however, Echo and subsequent programs with tags failed to preserve the autonomy of perception of the agents that displayed and read tags, as first program did. These subsequent tag programs include Epstein and Axtell; Axelrod; Hales; Hales and Edmonds; Riolo, Cohen and Axelrod, as well as the works on contagion originating in Carley, etc. The only exceptions are the author's 1995 SISTER program, and Axtell, Epstein, and Young's 2001 program on the emergence of social classes, which was influenced by the symbolic interactionist simulation program at George Mason University, and Steels' 1996 work. Axtell Epstein and Young's program has since been credited for strong emergence (Desalles et al). This paper explains that autonomy of perception is the essential difference in the symbolic interactionist implementation of tags that enables a strong emergence to occur, and that is why strong emergence has occurred in the works of Duong and of Axtell, Epstein and Young. This paper explains the important differences in existing tag models, pointing out the qualities that enable symbolic interactionist models to become social engines with strong emergence, and also introduces new work that puts the SISTER program in a spatial grid, and explores what happens to prices across the grid. In half of the runs, a standard of trade, or money, emerges.

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This paper discusses the central problems with creating valid computational social science simulations, and then suggests answers to these problems that involve co-evolution, autonomy, interpretation, and data processing under uncertainty. We also present the use of these techniques in the Nexus cognitive agent simulation, used at the US Department of Defense (DoD) in multiple major analyses of Irregular Warfare. We introduce the technique of data absorption, which leverages co-evolutionary pressure to reproduce the same dynamic structures that caused observable real word data in the simulation through the motivations of the agents.This technique gives a causal explanation for the data, and sets the stage for testing the effects of interventions never seen by the system on the system. By mimicking the state of equilibrium reached by the natural system, data absorption closes the gap between theory-centric simulations and data centric simulations.