Eliciting Informative Feedback: The Peer-Prediction Method

Miller, Nolan; Resnick, Paul; Zeckhauser, Richard

doi:10.1287/mnsc.1050.0379

Cited by 404 publications

(316 citation statements)

References 24 publications

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“…We describe the JF mechanism [Jurca and Faltings 2009], which include the first peer prediction mechanism [Miller et al 2005] as a special case.…”

Section: The Jurca and Faltings Mechanismmentioning

confidence: 99%

“…These difficulties led to the development of peer prediction mechanisms, pioneered by Miller et al [2005]. To incentivize truthful reports, these mechanisms leverage the stochastic correlation of participants' information and reward each participant by comparing the participant's report with those of his peers.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, such uninformative equilibria are unavoidable in general [Jurca and Faltings 2009;Waggoner and Chen 2013]. Although Miller et al [2005] argue that the truthful equilibrium is likely focal due to limited communication or ethical preferences of participants, there is generally little theoretical or empirical support for this conjecture.…”

Section: Introductionmentioning

confidence: 99%

“…There have been considerable theoretical developments on peer prediction mechanisms. The first peer prediction mechanism was proposed by Miller et al [2005] and later generalized by Jurca and Faltings [2009] to counter collusions. Subsequently, several mechanisms [Prelec 2004;Witkowski and Parkes 2012b,a;Radanovic and Faltings 2013] were proposed to relax the strong common knowledge assumptions of the earlier mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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Trick or treat

Gao

Mao

Adams

2014

Proceedings of the Fifteenth ACM Conference on Economics and Computation

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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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“…We describe the JF mechanism [Jurca and Faltings 2009], which include the first peer prediction mechanism [Miller et al 2005] as a special case.…”

Section: The Jurca and Faltings Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Trick or treat

Gao

Mao

Adams

2014

Proceedings of the Fifteenth ACM Conference on Economics and Computation

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“…Researchers have been developing incentive mechanisms to elicit truthful ratings from buyers. We survey three types of incentive mechanisms for electronic marketplaces, including side payment mechanisms (Jurca and Faltings 2003;Miller, Resnick, and Zeckhauser 2005), credibility mechanisms (Jurca and Faltings 2004;Papaioannou and Stamoulis 2005), and trust revelation mechanisms (Braynov and Sandholm 2002;Dash, Ramchurn, and Jennings 2004). We also point out certain shortcomings of these methods, some of which motivated our work.…”

Section: Related Workmentioning

confidence: 99%

Combining Trust Modeling and Mechanism Design for Promoting Honesty in E‐marketplaces

Zhang

Cohen

Larson

2012

Computational Intelligence

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In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e-marketplaces that is attractive to users, through its promotion of honesty.

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