Network and timing effects in social learning

Wade Hann-Caruthers, Minghao Pan and Omer Tamuz

Papers from arXiv.org

Abstract: We consider a group of agents who can each take an irreversible costly action whose payoff depends on an unknown state. Agents learn about the state from private signals, as well as from past actions of their social network neighbors, which creates an incentive to postpone taking the action. We show that outcomes depend on network structure: on networks with a linear structure patient agents do not converge to the first-best action, while on regular directed tree networks they do.

Date: 2024-12
New Economics Papers: this item is included in nep-gth, nep-mic and nep-net
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