Learning Through Imitation: An Experiment

Marina Agranov, Gabriel Lopez-Moctezuma, Philipp Strack and Omer Tamuz

Papers from arXiv.org

Abstract: We compare how well agents aggregate information in two repeated social learning environments. In the first setting agents have access to a public data set. In the second they have access to the same data, and also to the past actions of others. Despite the fact that actions contain no additional payoff-relevant information, and despite potential herd behavior, free riding and information overload issues, observing and imitating the actions of others leads agents to take the optimal action more often in the second setting. We also investigate the effect of group size, as well as a setting in which agents observe private data and others' actions.

Date: 2026-05, Revised 2026-05
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