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evolutionary game theory

PsychologySocial behaviorAltruismEvolution and behavior

Summary

Evolutionary game theory models how behavioral strategies spread in a population when the fitness payoff of a strategy depends on what others are doing. A strategy that no rare alternative can beat once it is common is an evolutionarily stable strategy (ESS). The exam uses it to explain cooperation, altruism and aggression.

Detail

Meaning: Individuals are the "players," and each uses a strategy, such as cooperate or defect, or fight or retreat. The payoff is reproductive fitness, not money. Strategies with higher payoffs tend to become more common over generations. Payoffs are frequency dependent, which means a strategy's success depends on how common the other strategies are. A strategy is an evolutionarily stable strategy (ESS) if, when most of the population uses it, a rare mutant using a different strategy does no better. Some stable outcomes are mixed populations in which several strategies coexist.

On the MCAT: A passage gives a payoff table or a scenario, often a prisoner's dilemma or a hawk-dove contest over a resource. You are asked which strategy spreads, which is stable, or why cooperation persists. Compare payoffs for a rare strategy in a population dominated by another. Cooperation is usually explained by repeated interactions (reciprocal altruism, such as tit-for-tat) or by relatedness (kin selection and inclusive fitness).

Common confusion: Students think the players consciously choose the best move. In this theory, selection acting on heritable behavior does the work, and the animals need not reason at all. Another error is assuming the stable strategy gives the best result for the group. In a one-shot prisoner's dilemma, defection can be stable even though mutual cooperation pays both players more.

Sources

  • Evolutionary Analysis
  • Evolution
  • Psychology

Written for the MCAT and checked before it was published. For study reference only. Spotted an error? Let us know.

evolutionary game theory: MCAT Glossary