Strategic Uncertainty and the Genetic Algorithm Adaptation

  • Jasmina Arifovic
Part of the Advances in Computational Economics book series (AICE, volume 6)

Abstract

This paper studies the behavior of the genetic algorithm in coordination games with strategic uncertainty that are characterized by the multiplicity of equilibria. The main objectives are to examine whether the genetic algorithm adaptation leads to coordination and what the long run properties of the dynamics are. The genetic algorithm behavior is compared to the evidence from the experiments with human subjects. The adaptation of genetic algorithm players results in coordination on an equilibrium. Which equilibrium is selected depends on the group size. The analysis of the long-run properties of the dynamics show that, as a result of the continuing effects of the genetic drift, the genetic algorithm can reach any of the equilibria of the game regardless of the group size. Simulations with large group treatments spend most of the time in Pareto-inferior equilibria, while simulations with small group treatments spend most of the time in Pareto-superior equilibria of the game.

Keywords

Genetic Algorithm Nash Equilibrium Binary String Average Effort Evolutionary Stable Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Jasmina Arifovic

There are no affiliations available

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