Learning and Equilibrium
By Drew Fudenberg, David K. Levine. 2009
127 citations recorded by the source corpus.
The theory of learning in games explores how, which, and what kind of equilibria might arise as a consequence of a long-run nonequilibrium process of learning, adaptation, and/or imitation. If agents’ strategies are completely observed at the end of each round (and agents are randomly matched with a series of anonymous opponents), fairly simple rules perform well in terms of the agent’s worst-case payoffs, and also guarantee that any steady state of the system must correspond to an equilibrium. If players do not observe the strategies chosen by their opponents (as in extensive-form games), then learning is consistent with steady states that are not Nash equilibria because players can maintain incorrect beliefs about off-path play. Beliefs can also be incorrect because of cognitive limitations and systematic inferential errors.
Subfields: Management Science and Operations Research, Safety Research, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/3c557b013dd4046f298182130e8f2e3e8172df31
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In Management Science and Operations Research
Citations in the corpus
- The Social Bayesian Brain: Does Mentalizing Make a Difference When We Learn? (2014, background): “At the limit s?0, Equation 3 converges towards the (stationary) opponent’s choice frequency and Equations 1-3 essentially reproduce "fictitious play" strategies [16,17].”