Evolutionary stability in finite stopping games under a fast best-reply dynamics
AbstractWe consider a fast evolutionary dynamic process on finite stopping games, where each player at each node has at most one move to continue the game. A state is evolutionarily stable if its long-run relative frequency of occurrence is bounded away from zero as the mutation rate decreases to zero. The fast dynamic process allows each individual in each population to change its strategy at every stage. We define a robustness index of backward induction and show examples where the backward induction equilibrium component is not evolutionarily stable for large populations. We show some sufficient conditions for evolutionary stability, which are different from the ones for the conventional evolutionary model. Even for this fast dynamic process, the transition between any two Nash equilibrium components may take very long time.
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Bibliographic InfoPaper provided by The Center for the Study of Rationality, Hebrew University, Jerusalem in its series Discussion Paper Series with number dp632.
Length: 33 pages
Date of creation: Jan 2013
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-03-16 (All new papers)
- NEP-EVO-2013-03-16 (Evolutionary Economics)
- NEP-GTH-2013-03-16 (Game Theory)
- NEP-MIC-2013-03-16 (Microeconomics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Sergiu Hart, 1999.
"Evolutionary Dynamics and Backward Induction,"
Game Theory and Information
9905002, EconWPA, revised 23 Mar 2000.
- M. Kandori & G. Mailath & R. Rob, 1999.
"Learning, Mutation and Long Run Equilibria in Games,"
Levine's Working Paper Archive
500, David K. Levine.
- Kandori, Michihiro & Mailath, George J & Rob, Rafael, 1993. "Learning, Mutation, and Long Run Equilibria in Games," Econometrica, Econometric Society, vol. 61(1), pages 29-56, January.
- Kandori, M. & Mailath, G.J., 1991. "Learning, Mutation, And Long Run Equilibria In Games," Papers 71, Princeton, Woodrow Wilson School - John M. Olin Program.
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