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Paper Detail

Paper IDG.2.3
Paper Title Observational Learning with Fake Agents
Authors Pawan Poojary, Randall Berry, Northwestern University, United States
Session G.2: Game Theory
Presentation Lecture
Track Graphs, Games, Sparsity, and Signal Processing
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
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.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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