Muddling Through: Noisy Equilibrium Selection
Ken Binmore and
Discussion Paper Serie B from University of Bonn, Germany
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 payoff-dominant equilibrium in a 2x2 game is selected by the model as well as conditions under which the risk- dominant equilibrium is selected. The relevant risk-dominance considerations, however, arise not in the original game but in a "fitness game" derived from the process by which payoffs in the original game are translated into evolutionary fitnesses. We also find that waiting times until the limiting distribution is reached can be shorter than in a mutation-driven model. To explore the robustness of the results to the specification of the model, we present a number of 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.
JEL-codes: C70 (search for similar items in EconPapers)
References: Add references at CitEc
Citations View citations in EconPapers (30) Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Working Paper: Muddling Through: Noisy Equilibrium Section (1996)
Working Paper: Muddling Through: Noisy Equilibrium selection (1994)
Working Paper: Muddling Through:Noisy Equilibrium Selection (1994)
Working Paper: Muddling Through: Noisy Equilibrium Selection (1994)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bon:bonsfb:275
Access Statistics for this paper
More papers in Discussion Paper Serie B from University of Bonn, Germany Bonn Graduate School of Economics, University of Bonn, Adenauerallee 24 - 26, 53113 Bonn, Germany.
Series data maintained by BGSE Office ().