Al fine di migliorare la tua esperienza di navigazione, questo sito utilizza i cookie di profilazione di terze parti. Chiudendo questo banner o accedendo ad un qualunque elemento sottostante acconsenti all’uso dei cookie.

Learning and Selfconfirming Equilibria in Network Games

14 ottobre 2020 ore 12:30 - 13:30

Aula Luiss Research Seminars, Sede di Luiss

Speaker: Pierpaolo Battigalli, Università Bocconi

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 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
network games, we relate selfconfirming and Nash equilibria, and we analyze
simple conjectural best reply dynamics whose limit points are selfconfirming
equilibria.