Learning and Selfconfirming Equilibria in Network Games
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 payoffs depend 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 bestreply paths whose limit points are selfconfirming equilibria.
 Publication:

arXiv eprints
 Pub Date:
 December 2018
 DOI:
 10.48550/arXiv.1812.11775
 arXiv:
 arXiv:1812.11775
 Bibcode:
 2018arXiv181211775B
 Keywords:

 Economics  Theoretical Economics;
 Computer Science  Computer Science and Game Theory