Non-Trivial Solutions to the N-Person Prisoners' Dilemma

We have developed a new agent-based simulation tool to model social dilemmas for the case of a large number of not necessarily rational decision-makers (Szilagyi and Szilagyi, 2000). The combination of various personalities with stochastic learning makes it possible to simulate the multi-person Prisoners' Dilemma game for realistic situations. A variety of personality profiles and their arbitrary combinations can be represented, including agents whose probability of cooperation changes by an amount proportional to its reward from the environment. For the case of such agents the game has non-trivial but remarkably regular solutions. We discuss a method and present an algorithm for making accurate advance predictions of these solutions. We also propose our model as a viable approach for the study of populations of cells, organisms, groups, organizations, communities, and societies. It may lead to better understanding of the evolution of cooperation in living organisms, international alliances, sports teams, and large organizations.