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Trick or treat
Author(s) -
Xi Alice Gao,
Andrew Mao,
Yiling Chen,
Ryan P. Adams
Publication year - 2014
Publication title -
digital access to scholarship at harvard (dash) (harvard university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2600057.2602865
Subject(s) - computer science , payment , mechanism (biology) , contrast (vision) , nash equilibrium , strategic dominance , mechanism design , markov chain , internet privacy , artificial intelligence , microeconomics , machine learning , economics , philosophy , epistemology , world wide web
Collecting truthful subjective information from multiple individuals is an important problem in many social and online systems. While peer prediction mechanisms promise to elicit truthful information by rewarding participants with carefully constructed payments, they also admit uninformative equilibria where coordinating participants provide no useful information. To understand how participants behave towards such mechanisms in practice, we conduct the first controlled online experiment of a peer prediction mechanism, engaging the participants in a multiplayer, real-time and repeated game. Using a hidden Markov model to capture players' strategies from their actions, our results show that participants successfully coordinate on uninformative equilibria and the truthful equilibrium is not focal, even when some uninformative equilibria do not exist or are undesirable. In contrast, most players are consistently truthful in the absence of peer prediction, suggesting that these mechanisms may be harmful when truthful reporting has similar cost to strategic behavior.

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