Obtaining Reliable Feedback for Sanctioning Reputation Mechanisms

Reputation mechanisms offer an effective alternative to verification authorities for building trust in electronic markets with moral hazard. Future clients guide their business decisions by considering the feedback from past transactions; if truthfully exposed, cheating behavior is sanctioned and thus becomes irrational. It therefore becomes important to ensure that rational clients have the right incentives to report honestly. As an alternative to side-payment schemes that explicitly reward truthful reports, we show that honesty can emerge as a rational behavior when clients have a repeated presence in the market. To this end we describe a mechanism that supports an equilibrium where truthful feedback is obtained. Then we characterize the set of pareto-optimal equilibria of the mechanism, and derive an upper bound on the percentage of false reports that can be recorded by the mechanism. An important role in the existence of this bound is played by the fact that rational clients can establish a reputation for reporting honestly.

[1]  Paul R. Milgrom,et al.  Predation, reputation, and entry deterrence☆ , 1982 .

[2]  R. Cooke Experts in Uncertainty: Opinion and Subjective Probability in Science , 1991 .

[3]  Chrysanthos Dellarocas,et al.  Reputation Mechanism Design in Online Trading Environments with Pure Moral Hazard , 2005, Inf. Syst. Res..

[4]  G. Mailath,et al.  Repeated Games and Reputations: Long-Run Relationships , 2006 .

[5]  D. Fudenberg,et al.  Reputation and Equilibrium Selection in Games with a Patient Player , 1989 .

[6]  Julian Padget,et al.  Agent-Mediated Electronic Commerce IV , 2002 .

[7]  D. Fudenberg,et al.  Digitized by the Internet Archive in 2011 with Funding from Working Paper Department of Economics the Folk Theorem with Imperfect Public Information , 2022 .

[8]  George D. Stamoulis,et al.  An incentives' mechanism promoting truthful feedback in peer-to-peer systems , 2005, CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005..

[9]  Sandip Debnath,et al.  Limiting deception in groups of social agents , 2000, Appl. Artif. Intell..

[10]  Michael R. Baye,et al.  The Economics of the Internet and E-commerce , 2002, Advances in Applied Microeconomics.

[11]  Paul Resnick,et al.  Eliciting Informative Feedback: The Peer-Prediction Method , 2005, Manag. Sci..

[12]  E. Friedman,et al.  The Social Cost of Cheap Pseudonyms , 2001 .

[13]  Andreas Birk,et al.  Learning to Trust , 2000, Trust in Cyber-societies.

[14]  J. Wooders,et al.  Reputation in Auctions: Theory, and Evidence from Ebay , 2006 .

[15]  David M. Kreps,et al.  Reputation and imperfect information , 1982 .

[16]  Michael Rovatsos,et al.  Using trust for detecting deceitful agents in artificial societies , 2000, Appl. Artif. Intell..

[17]  Tuomas Sandholm,et al.  Incentive compatible mechanism for trust revelation , 2002, AAMAS '02.

[18]  David M. Kreps,et al.  Rational cooperation in the finitely repeated prisoners' dilemma , 1982 .

[19]  Chrysanthos Dellarocas,et al.  Goodwill Hunting: An Economically Efficient Online Feedback Mechanism for Environments with Variable Product Quality , 2002, AMEC.

[20]  Boi Faltings,et al.  Minimum payments that reward honest reputation feedback , 2006, EC '06.

[21]  Munindar P. Singh,et al.  Detecting deception in reputation management , 2003, AAMAS '03.

[22]  Eric Maskin,et al.  Renegotiation in Repeated Games , 1987 .

[23]  Munindar P. Singh,et al.  An evidential model of distributed reputation management , 2002, AAMAS '02.

[24]  Debraj Ray,et al.  Collective Dynamic Consistency in Repeated Games , 1989 .

[25]  Klaus M. Schmidt Reputation and Equilibrium Characterization in Repeated Games with Conflicting Interests , 1993 .

[26]  R. Selten The chain store paradox , 1978 .

[27]  Paul Resnick,et al.  Eliciting Informative Feedback: The Peer-Prediction Method , 2005, Manag. Sci..

[28]  E. Stacchetti,et al.  Towards a Theory of Discounted Repeated Games with Imperfect Monitoring , 1990 .