In this paper, we provide a novel approach to analyze how incomplete information about the network affects behavior and learning processes. We propose a ...
[PDF] Learning and Selfconfirming Equilibria in Network Games - CSEF
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Following Battigalli et al. (2015), we call “selfconfirming equilibria” the steady states of learning processes when static or dynamic games are played ...
In this section, we analyze the set of selfconfirming equilibria and the learning paths in linear- quadratic network games with just observable payoffs and ...
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 ...
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 ...
Analysis of interaction in networks, main ingredients: Strategic interaction (explicit): Individual incentives to act, local externalities (with neighbors) ...
We study a dynamic game in which a group of players attempt to coordinate on a desired, but only partially known, outcome. The desired outcome is represented by ...
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Mar 20, 2019 · We characterize the structure of the set of selfconfirming equilibria in network games and we relate selfconfirming and Nash equilibria. Thus, ...
This chapter investigates theoretical models of learning in games. It proposes a variety of learning models, with different motivations.
The stable points of these learning processes are self-confirming equilibria, a weaker solution concept than Nash equilibria.