Fast Convergence in Evolutionary Equilibrium Selection
AbstractStochastic 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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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 569.
Date of creation: 01 Sep 2011
Date of revision:
Stochastic stability; Logit learning; Markov chain; Convergence time;
Find related papers by JEL classification:
- C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-10-09 (All new papers)
- NEP-EVO-2011-10-09 (Evolutionary Economics)
- NEP-GTH-2011-10-09 (Game Theory)
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