Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253462 
Year of Publication: 
2020
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 15 [Issue:] 4 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2020 [Pages:] 1471-1508
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
While payoff-based learning models are almost exclusively devised for finite action games, where players can test every action, it is harder to design such learning processes for continuous games. We construct a stochastic learning rule, designed for games with continuous action sets, which requires no sophistication from the players and is simple to implement: players update their actions according to variations in own payoff between current and previous action. We then analyze its behavior in several classes of continuous games and show that convergence to a stable Nash equilibrium is guaranteed in all games with strategic complements as well as in concave games, while convergence to Nash occurs in all locally ordinal potential games as soon as Nash equilibria are isolated.
Subjects: 
Payoff-based learning
continuous games
stochastic approximation
JEL: 
C6
C72
D83
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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