Muddling Through: Noisy Equilibrium Selection

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Abstract
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 risk-dominant equilibrium in a 2 x 2 game is selected by the model as well as conditions under which the payoff-dominant equilibrium is selected. We also find that waiting times until the limiting distribution is reached can be shorter than in a mutation-driven model. We present 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.
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