Learning and selfconfirming equilibria in network games

Pierpaolo Battigalli (), Fabrizio Panebianco and Paolo Pin

Journal of Economic Theory, 2023, vol. 212, issue C

Abstract: Consider a set of agents who play a network game repeatedly. Agents may not know the network. They may even be unaware that they are interacting with other agents in a network. Possibly, they just understand that their optimal action depends on an unknown state that is, actually, an aggregate of the actions of their neighbors. In each period, every agent chooses an action that maximizes her instantaneous subjective expected payoff and then updates her beliefs according to what she observes. In particular, we assume that each agent only observes her realized payoff. A steady state of the resulting dynamic is a selfconfirming equilibrium given the assumed feedback.

Keywords: Learning; Selfconfirming equilibrium; Network games; Observability by active players; Shallow conjectures (search for similar items in EconPapers)
JEL-codes: C72 D83 D85 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Learning and Selfconfirming Equilibria in Network Games (2022)
Working Paper: Learning and Selfconfirming Equilibria in Network Games (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:212:y:2023:i:c:s0022053123000960

DOI: 10.1016/j.jet.2023.105700

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