Observational Learning with Negative Externalities

Pawan Poojary, Randall Berry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Observational learning models seek to understand how distributed agents learn from observing the actions of others. In the basic model, agents seek to choose between two alternatives, where the underlying value of each alternative is the same for each agent. Agents do not know this value but only observe a noisy signal of the value and make their decision based on this signal and observations of other agents' actions. Here, instead we consider a scenario in which the choices faced by an agent exhibit a negative externality so that value of a choice may decrease depending on the history of other agents selecting that choice. We study the learning behavior of Bayesian agents with such an externality and show that this can lead to very different outcomes compared to models without such an externality.

Original languageEnglish (US)
Title of host publication2022 IEEE International Symposium on Information Theory, ISIT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1495-1500
Number of pages6
ISBN (Electronic)9781665421591
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Information Theory, ISIT 2022 - Espoo, Finland
Duration: Jun 26 2022Jul 1 2022

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2022-June
ISSN (Print)2157-8095

Conference

Conference2022 IEEE International Symposium on Information Theory, ISIT 2022
Country/TerritoryFinland
CityEspoo
Period6/26/227/1/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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