Learning and Selfconfirming Equilibria in Network Games

Pierpaolo Battigalli (), Fabrizio Panebianco and Paolo Pin

No 637, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University

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. Each time, 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. We characterize the structure of the set of selfconfirming equilibria in the given class of network games, we relate selfconfirming and Nash equilibria, and we analyze simple conjectural best-reply paths whose limit points are selfconfirming equilibria. JEL classification codes: C72, D83, D85. Keywords: Learning; Selfcon?rming equilibrium; Network games; Observability by active players; Shallow conjectures.

Date: 2018
New Economics Papers: this item is included in nep-evo, nep-gth and nep-mic
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Journal Article: Learning and selfconfirming equilibria in network games (2023)
Working Paper: Learning and Selfconfirming Equilibria in Network Games (2022)
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