Machine learningGame-theoretic
Bayesian Nash Equilibrium
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.
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Sources
- Harsanyi, J. C. (1967). Games with incomplete information played by Bayesian players, Parts I, II, and III. Management Science, 14(3), 159-182. DOI: 10.1287/mnsc.14.3.159 ↗
- Harsanyi, J. C. (1968). Games with incomplete information played by Bayesian players. Management Science, 14(7), 486-502. DOI: 10.1287/mnsc.14.7.486 ↗