The Evolution of Fuzzy Rules as Strategies in Two‐Player Games

This paper describes simulations using fuzzy rules that show how Nash equilibrium behavior can be achieved by boundedly rational agents in two‐player games with infinite strategy spaces. That is, we show how agents using simple “rules of thumb” can achieve near‐equilibrium outcomes without any overt computation of the equilibrium. This is accomplished by using a genetic algorithm to approximate repeated play. Two games of differing complexities, both with analytic solutions, are examined: a repeated linear‐demand Cournot game and a contestable rent game. When fuzzy rules used only the most recent information, the games we examined converged to outcomes similar to their respective Coumot‐Nash equilibrium outcomes. When fuzzy rules “remembered” play from the more distant past, we found that the games converged more slowly, if at all.