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Preprints, Working Papers, ... Year : 2022

Stationary social learning in a changing environment

Abstract

We consider social learning in a changing world. With changing states, societies can remain responsive only if agents regularly act upon fresh information, which drastically limits the value of observational learning. When the state is close to persistent, a consensus whereby most agents choose the same action typically emerges. However, the consensus action is not perfectly correlated with the state, because societies exhibit inertia following state changes. Phases of inertia may be longer when signals are more precise, even if agents draw large samples of past actions, as actions then become too correlated within samples, thereby reducing informativeness and welfare.

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hal-03837075 , version 1 (02-11-2022)

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Raphaël Levy, Marcin Pęski, Nicolas Vieille. Stationary social learning in a changing environment. 2022. ⟨hal-03837075⟩

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