Observational Learning with Fake Agents

Pawan Poojary; Randall Berry

doi:10.1109/ISIT44484.2020.9174217

Observational Learning with Fake Agents

Pawan Poojary, Randall Berry

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

It is common in online markets for agents to learn from other's actions. Such observational learning can lead to herding or information cascades in which agents eventually "follow the crowd". Models for such cascades have been well studied for Bayes-rational agents that choose pay-off optimal actions. In this paper, we additionally consider the presence of fake agents that seek to influence other agents into taking one particular action. To that end, these agents take a fixed action in order to influence the subsequent agents towards their preferred action. We characterize how the fraction of such fake agents impacts behavior of the remaining agents and show that in certain scenarios, an increase in the fraction of fake agents in fact reduces the chances of their preferred outcome.

Original language	English (US)
Title of host publication	2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1373-1378
Number of pages	6
ISBN (Electronic)	9781728164328
DOIs	https://doi.org/10.1109/ISIT44484.2020.9174217
State	Published - Jun 2020
Event	2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States Duration: Jul 21 2020 → Jul 26 2020

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
Volume	2020-June
ISSN (Print)	2157-8095

Conference

Conference	2020 IEEE International Symposium on Information Theory, ISIT 2020
Country/Territory	United States
City	Los Angeles
Period	7/21/20 → 7/26/20

Funding

This work was supported in part by the NSF under grants CNS-1701921 and CNS-1908807.

ASJC Scopus subject areas

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

Access to Document

10.1109/ISIT44484.2020.9174217

Cite this

Poojary, P., & Berry, R. (2020). Observational Learning with Fake Agents. In 2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings (pp. 1373-1378). Article 9174217 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2020-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT44484.2020.9174217

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title = "Observational Learning with Fake Agents",

abstract = "It is common in online markets for agents to learn from other's actions. Such observational learning can lead to herding or information cascades in which agents eventually {"}follow the crowd{"}. Models for such cascades have been well studied for Bayes-rational agents that choose pay-off optimal actions. In this paper, we additionally consider the presence of fake agents that seek to influence other agents into taking one particular action. To that end, these agents take a fixed action in order to influence the subsequent agents towards their preferred action. We characterize how the fraction of such fake agents impacts behavior of the remaining agents and show that in certain scenarios, an increase in the fraction of fake agents in fact reduces the chances of their preferred outcome.",

author = "Pawan Poojary and Randall Berry",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Symposium on Information Theory, ISIT 2020 ; Conference date: 21-07-2020 Through 26-07-2020",

year = "2020",

month = jun,

doi = "10.1109/ISIT44484.2020.9174217",

language = "English (US)",

series = "IEEE International Symposium on Information Theory - Proceedings",

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Poojary, P & Berry, R 2020, Observational Learning with Fake Agents. in 2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings., 9174217, IEEE International Symposium on Information Theory - Proceedings, vol. 2020-June, Institute of Electrical and Electronics Engineers Inc., pp. 1373-1378, 2020 IEEE International Symposium on Information Theory, ISIT 2020, Los Angeles, United States, 7/21/20. https://doi.org/10.1109/ISIT44484.2020.9174217

Observational Learning with Fake Agents. / Poojary, Pawan; Berry, Randall.
2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1373-1378 9174217 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2020-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - It is common in online markets for agents to learn from other's actions. Such observational learning can lead to herding or information cascades in which agents eventually "follow the crowd". Models for such cascades have been well studied for Bayes-rational agents that choose pay-off optimal actions. In this paper, we additionally consider the presence of fake agents that seek to influence other agents into taking one particular action. To that end, these agents take a fixed action in order to influence the subsequent agents towards their preferred action. We characterize how the fraction of such fake agents impacts behavior of the remaining agents and show that in certain scenarios, an increase in the fraction of fake agents in fact reduces the chances of their preferred outcome.

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Observational Learning with Fake Agents

Abstract

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ASJC Scopus subject areas

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