We analyze an evolutionary model with a finite number of players and with noise or mutations. The expansion and contraction of strategies is linked--as usual--to their current relative success, but mutations--which perturb the system away from its deterministic evolution--are present as well. Mutations can occur in every period, so the focus is on the implications of ongoing mutations, not a one-shot mutation. The effect of these mutations is to drastically reduce the set of equilibria to what we term "long-run equilibria." For $2 \times 2$ symmetric games with two symmetric strict Nash equilibria the equilibrium selected satisfies (for large populations) Harsanyi and Selten's (1988) criterion of risk-dominance. In particular, if both strategies have equal security levels, the Pareto dominant Nash equilibrium is selected, even though there is another strict Nash equilibrium.
MLA
Mailath, George J., et al. “Learning, Mutation, and Long Run Equilibria in Games.” Econometrica, vol. 61, .no 1, Econometric Society, 1993, pp. 29-56, https://www.jstor.org/stable/2951777
Chicago
Mailath, George J., Michihiro Kandori, and Rafael Rob. “Learning, Mutation, and Long Run Equilibria in Games.” Econometrica, 61, .no 1, (Econometric Society: 1993), 29-56. https://www.jstor.org/stable/2951777
APA
Mailath, G. J., Kandori, M., & Rob, R. (1993). Learning, Mutation, and Long Run Equilibria in Games. Econometrica, 61(1), 29-56. https://www.jstor.org/stable/2951777
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