Comparer des méthodes

	Théorie des jeux évolutionnistes	Équilibre de Nash bayésien
Domaine	Théorie des jeux	Théorie des jeux
Famille	Machine learning	Machine learning
Année d'origine	1973	1967
Auteur d'origine	John Maynard Smith, George Price	John Harsanyi
Type	algorithm	algorithm
Source fondatrice	Smith, J. M., & Price, G. R. (1973). The logic of animal conflict. Nature, 246(5427), 15-18.	Harsanyi, J. C. (1967). Games with incomplete information played by Bayesian players, Parts I, II, and III. Management Science, 14(3), 159-182.
Alias	ESS, Evolutionarily Stable Strategy, Replicator Dynamics	BNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium
Apparentées	4	4
Résumé	Evolutionary Game Theory applies game-theoretic reasoning to biological evolution and social dynamics, where populations of agents with different strategies interact repeatedly. Introduced by John Maynard Smith and George Price in 1973, the framework uses the concept of Evolutionarily Stable Strategies (ESS) to identify strategy distributions that cannot be invaded by mutant strategies. Replicator dynamics describe how strategy frequencies evolve over time when reproduction is proportional to payoff success.	Bayesian Nash Equilibrium (BNE) extends Nash Equilibrium to games with incomplete information, where players lack full knowledge of others' payoff functions. Introduced by John Harsanyi in 1967, BNE models strategic interaction under uncertainty by representing unknown payoffs as players' private types drawn from a probability distribution. Equilibrium is found by solving for type-contingent strategies that are best responses to all possible type realizations.