This research will explore the applicability of genetic algorithms and classifier systems to the analysis of social interaction, seeking to provide a formally-based understanding. The forms of stable interaction range from highly-scripted, instrumentalized social action to informal group processes sustained by the rationality of individual participants. The techniques will be applied to three domains: social exchange, collective action, and action organization. A key advantage of this research is the power of the computer techniques to represent all forms of interaction in terms of an evolving population of genotypical action plans and thus to provide the basis for an integrated theory of social interaction systems that covers the full range of commonly recognized system types. Sociology has been slow to make use of several new approaches in computer technology, and this research will be the first substantial sociological application of the related techniques of genetic algorithms and classifier systems. Genetic algorithms are a way of arriving at a solution to a problem through successive approximation, with the great advantage that it develops many potential solutions simultaneously, successively combining features of different solutions and selecting the best solutions for further development. In the context of this research, classifer systems read computerized strings of symbols, such as the units produced by genetic algorithms, and interpret them as rules for human action under specified circumstances.
