In recent years, noncooperative game theory, and especially Nash equilibrium analysis, has been used in the study of many economic situations. Along with the many and varied applications has come persistent criticism: Why or when is equilibrium analysis appropriate? Where do equilibria come from? How does one choose among many equilibria in making predictions? This project addresses these questions using two different approaches. In the first, players are assumed to follow ad-hoc behavioral rules in the spirit of models of bounded rationality. In the second, the players' forecasts and behavior are obtained as the solution to a dynamic optimization problem given their exogenous prior beliefs about the opponents' strategies. A common theme to both parts is that learning need not lead to a Nash equilibrium even when players are long-lived, unless they also engage in a sufficient amount of experimentation. The learning-based justification of equilibrium analysis developed in this project is a very significant contribution. It provides a better foundation for widely-used analytic tools, identifies new and promising lines of research in economic theory and motivates research in economic theory with the tests of existing theories in experimental economics.//
