 Observational learning with position uncertaintyIgnacio Monzon and Michael Rapp Journal of Economic Theory, 2014, vol. 154, issue C, 375-402 Abstract: Observational learning is typically examined when agents have precise information about their position in the sequence of play. We present a model in which agents are uncertain about their positions. Agents sample the decisions of past individuals and receive a private signal about the state of the world. We show that social learning is robust to position uncertainty. Under any sampling rule satisfying a stationarity assumption, learning is complete if signal strength is unbounded. In cases with bounded signal strength, we provide a lower bound on information aggregation: individuals do at least as well as an agent with the strongest signal realizations would do in isolation. Finally, we show in a simple environment that position uncertainty slows down learning but not to a great extent. Keywords: Social learning; Complete learning; Information aggregation; Herds; Position uncertainty; Observational learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:154:y:2014:i:c:p:375-402 DOI: 10.1016/j.jet.2014.09.012 Access Statistics for this article Journal of Economic Theory is currently edited by A. Lizzeri and K. Shell More articles in Journal of Economic Theory from Elsevier |
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