Muddling Through: Noisy Equilibrium Selection

We examine an evolutionary model in which the primary source of "noise" that moves the model between equilibria is not random, arbitrarily improbable mutations but mistakes in learning. We find conditions under which the payoff-dominant equilibrium in a 2x2 game is selected by the model as well as conditions under which the risk-dominant equlibrium is selected. The relevant risk-dominance considerations, however, arise not in the original game but in a "fitness game" derived from the process by which payoffs in the original game are translated into evolutionary fitnesses. We also find that waiting times until the limiting distribution is reached can be shorter than in a mutation-driven model. To explore the robustness of the results to the specification of the model, we present a number of comparative static results as well as a "two-tiered" evolutionary model in which the rules by which agents learn to play the game are themselves subject to evolutionary pressure.