Enforcing Truthful Strategies in Incentive Compatible Reputation Mechanisms

  • Radu Jurca
  • Boi Faltings
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3828)


We commonly use the experience of others when taking decisions. Reputation mechanisms aggregate in a formal way the feedback collected from peers and compute the reputation of products, services, or providers. The success of reputation mechanisms is however conditioned on obtaining true feedback. Side-payments (i.e. agents get paid for submitting feedback) can make honest reporting rational (i.e. Nash equilibrium). Unfortunately, known schemes also have other Nash equilibria that imply lying. In this paper we analyze the equilibria of two incentive-compatible reputation mechanisms and investigate how undesired equilibrium points can be eliminated by using trusted reports.


Nash Equilibrium Equilibrium Strategy Service Failure Reporting Strategy Nash Equilibrium Strategy 
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  1. 1.
    Aberer, K., Despotovic, Z.: Managing Trust in a Peer-2-Peer Information System. In: Proceedings of the Ninth International Conference on Information and Knowledge Management, CIKM (2001)Google Scholar
  2. 2.
    Birk, A.: Learning to Trust. In: Falcone, R., Singh, M., Tan, Y.-H. (eds.) AA-WS 2000. LNCS (LNAI), vol. 2246, pp. 133–144. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Conitzer, V., Sandholm, T.: Complexity Results about Nash Equilibria. In: Proceedings of the IJCAI, Acapulco, Mexico (2003)Google Scholar
  4. 4.
    Cooke, R.: Experts in Uncertainity: Opinion and Subjective Probability in Science. Oxford University Press, New York (1991)Google Scholar
  5. 5.
    Dellarocas, C.: Goodwill Hunting: An Economically Efficient Online Feedback. In: Padget, J., Shehory, O., Parkes, D.C., Sadeh, N.M., Walsh, W.E. (eds.) AMEC 2002. LNCS (LNAI), vol. 2531, pp. 238–252. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Jurca, R., Faltings, B.: An Incentive-Compatible Reputation Mechanism. In: Proceedings of the IEEE Conference on E-Commerce, Newport Beach, CA, USA (2003)Google Scholar
  7. 7.
    Jurca, R., Faltings, B.: “CONFESS”. An Incentive Compatible Reputation Mechanism for the Online Hotel Booking Industry. In: Proceedings of the IEEE Conference on E-Commerce, San Diego, CA, USA (2004)Google Scholar
  8. 8.
    Kreps, D.M., Milgrom, P., Roberts, J., Wilson, R.: Rational Cooperation in the Finitely Repeated Pisoner’s Dilemma. J. of Economic Theory 27, 245–252 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Miller, N., Resnick, P., Zeckhauser, R.: Eliciting Informative Feedback: The Peer-Prediction Method. Forthcoming in Management Science (2005)Google Scholar
  10. 10.
    Papaioannou, T.G., Stamoulis, G.D.: An Incentives’ Mechanism Promoting Truthful Feedback in Peer-to-Peer Systems. In: Proceedings of IEEE/ACM CCGRID 2005 (2005)Google Scholar
  11. 11.
    Yu, B., Singh, M.: Detecting Deception in Reputation Management. In: Proceedings of the AAMAS, Melbourne, Australia (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Radu Jurca
    • 1
  • Boi Faltings
    • 1
  1. 1.Artificial Intelligence LaboratoryEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

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