Do We Learn from Mistakes of Others? A Test of Observational Learning in the Bandit Problem

31 Pages Posted: 13 Jan 2016 Last revised: 10 Dec 2016

Date Written: December 9, 2016

Abstract

I experimentally investigate observational (social) learning in the simple two-armed bandit framework where the models based on Bayesian reasoning and non-Bayesian reasoning (count heuristics) have different predictions. The results contradict the predictions of the Bayesian rationality e.g. Bayesian Nash Equilibrium, Naıve herding model (BRTNI): Subjects follow the choices that contain no information about the state of the world, follow the coinciding choices of others (though it is empirically suboptimal), sustain losses making every first choice and cascade early then Bayesian-based models predict, but not in a random way. In addition, the Quantal Response Equilibrium is tested and the robustness of the theory questioned.

Keywords: Observational learning, information cascade, experiment

JEL Classification: C92, D82, D83

Suggested Citation

Asanov, Igor, Do We Learn from Mistakes of Others? A Test of Observational Learning in the Bandit Problem (December 9, 2016). Available at SSRN: https://ssrn.com/abstract=2714315 or http://dx.doi.org/10.2139/ssrn.2714315

Igor Asanov (Contact Author)

University of Kassel ( email )

Fachbereich 05
Nora-Platiel-Straße 1
34109 Kassel, Hessen 34127
Germany

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