Wisdom of the Crowds vs. Groupthink: Learning in Groups and in Isolation

International Journal of Game Theory 42 (3):695-723 (2013)

Authors
David Danks
Carnegie Mellon University
Kevin Zollman
Carnegie Mellon University
Conor Mayo-Wilson
University of Washington
Abstract
We evaluate the asymptotic performance of boundedly-rational strategies in multi-armed bandit problems, where performance is measured in terms of the tendency (in the limit) to play optimal actions in either (i) isolation or (ii) networks of other learners. We show that, for many strategies commonly employed in economics, psychology, and machine learning, performance in isolation and performance in networks are essentially unrelated. Our results suggest that the appropriateness of various, common boundedly-rational strategies depends crucially upon the social context (if any) in which such strategies are to be employed.
Keywords Social Epistemology  Networks  Bayesian Learning  Game Theory
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