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Title: Observational Learning with Negative Externalities
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.  more » « less
Award ID(s):
1908807
NSF-PAR ID:
10391544
Author(s) / Creator(s):
;
Date Published:
Journal Name:
2022 IEEE International Symposium on Information Theory
Page Range / eLocation ID:
1495 to 1496
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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