Fast Convergence in Evolutionary Equilibrium Selection
Stochastic learning models provide sharp predictions about equilibrium selection when the noise level of the learning process is taken to zero.� The difficulty is that, when the noise is extremely small, it can take an extremely long time for a large population to reach the stochastically stable equilibrium.� An important exception arises when players interact locally in small close-knit groups; in this case convergence can be rapid for small noise and an arbitrarily large population.� We show that a similar result holds when the population is fully mixed and there is no local interaction.� Selection is sharp and convergence is fast when the noise level is 'fairly' small but not extremely small.
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