Article

Enforcing truthful strategies in incentive compatible reputation mechanisms

Authors:

Radu Jurca,

Boi FaltingsAuthors Info & Claims

WINE'05: Proceedings of the First international conference on Internet and Network Economics

Pages 268 - 277

https://doi.org/10.1007/11600930_26

Published: 15 December 2005 Publication History

Publisher Site

Abstract

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.

References

[1]

K. Aberer and Z. Despotovic. Managing Trust in a Peer-2-Peer Information System. In Proceedings of the Ninth International Conference on Information and Knowledge Management (CIKM), 2001.

Digital Library

Google Scholar

[2]

A. Birk. Learning to Trust. In R. Falcone, M. Singh, and Y.-H. Tan, editors, Trust in Cyber-societies, volume LNAI 2246, pages 133-144. Springer-Verlag, Berlin Heidelberg, 2001.

Digital Library

Google Scholar

[3]

V. Conitzer and T. Sandholm. Complexity Results about Nash Equilibria. In Proceedings of the IJCAI, Acapulco, Mexico, 2003.

Digital Library

Google Scholar

[4]

R. Cooke. Experts in Uncertainity: Opinion and Subjective Probability in Science. Oxford University Press: New York, 1991.

Google Scholar

[5]

C. Dellarocas. Goodwill Hunting: An Economically Efficient Online Feedback. In J. Padget and et al., editors, Agent-Mediated Electronic Commerce IV. Designing Mechanisms and Systems, volume LNCS 2531, pages 238-252. Springer Verlag, 2002.

Digital Library

Google Scholar

[6]

R. Jurca and B. Faltings. An Incentive-Compatible Reputation Mechanism. In Proceedings of the IEEE Conference on E-Commerce, Newport Beach, CA, USA, 2003.

Google Scholar

[7]

R. Jurca and B. Faltings. "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.

Digital Library

Google Scholar

[8]

D. M. Kreps, P. Milgrom, J. Roberts, and R. Wilson. Rational Cooperation in the Finitely Repeated Pisoner's Dilemma. J. of Economic Theory, 27:245-252, 1982.

Google Scholar

[9]

N. Miller, P. Resnick, and R. Zeckhauser. Eliciting Informative Feedback: The Peer-Prediction Method. Forthcoming in Management Science, 2005.

Digital Library

Google Scholar

[10]

T. G. Papaioannou and G. D. Stamoulis. An Incentives' Mechanism Promoting Truthful Feedback in Peer-to-Peer Systems. In Proceedings of IEEE/ACM CCGRID 2005, 2005.

Digital Library

Google Scholar

[11]

B. Yu and M. Singh. Detecting Deception in Reputation Management. In Proceedings of the AAMAS, Melbourne, Australia, 2003.

Digital Library

Google Scholar

Cited By

View all

Xu SZhang YResnick PSchoenebeck GChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Spot Check Equivalence: An Interpretable Metric for Information Elicitation MechanismsProceedings of the ACM Web Conference 202410.1145/3589334.3645679(276-287)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645679
Zarkoob HFu HLeyton-Brown KEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Report-Sensitive Spot-Checking in Peer-Grading SystemsProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398944(1593-1601)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398944
Zarkoob HFu HLeyton-Brown KElkind EVeloso MAgmon NTaylor M(2019)Report-Sensitive Spot-checking in Peer Grading SystemsProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3332093(2306-2308)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3332093
Show More Cited By

Recommendations

Incentive-Compatible Learning of Reserve Prices for Repeated Auctions
How can an auctioneer optimize revenue by learning the reserve prices from the bids in the previous auctions? How should the long-term incentives and strategic behavior of the bidders be taken into account? Motivated in part by applications in online ...
Large fractions of online advertisements are sold via repeated second-price auctions. In these auctions, the reserve price is the main tool for the auctioneer to boost revenues. In this work, we investigate the following question: how can the auctioneer ...
Incentive compatible budget elicitation in multi-unit auctions
SODA '10: Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete algorithms

In this paper, we consider the problem of designing incentive compatible auctions for multiple (homogeneous) units of a good, when bidders have private valuations and private budget constraints. When only the valuations are private and the budgets are ...
Incentive-compatible escrow mechanisms
AAAI'11: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence

The most prominent way to establish trust between buyers and sellers on online auction sites are reputation mechanisms. Two drawbacks of this approach are the reliance on the seller being long-lived and the susceptibility to whitewashing. In this paper, ...

Comments

Information & Contributors

Information

Published In

WINE'05: Proceedings of the First international conference on Internet and Network Economics

December 2005

1102 pages

ISBN:3540309004

Editors:
Xiaotie Deng
Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon , Hong Kong
,
Yinyu Ye
Department of Computer Science, Stanford University, Tat Chee Avenue, Kowloon , Stanford, CA

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 15 December 2005

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Xu SZhang YResnick PSchoenebeck GChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Spot Check Equivalence: An Interpretable Metric for Information Elicitation MechanismsProceedings of the ACM Web Conference 202410.1145/3589334.3645679(276-287)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645679
Zarkoob HFu HLeyton-Brown KEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Report-Sensitive Spot-Checking in Peer-Grading SystemsProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398944(1593-1601)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398944
Zarkoob HFu HLeyton-Brown KElkind EVeloso MAgmon NTaylor M(2019)Report-Sensitive Spot-checking in Peer Grading SystemsProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3332093(2306-2308)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3332093
Ito KOhsawa STanaka H(2018)Information Diffusion Enhanced by Multi-Task Peer PredictionProceedings of the 20th International Conference on Information Integration and Web-based Applications & Services10.1145/3282373.3282410(96-104)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3282373.3282410
Li ZZhang LFang ZLi J(2018)A Two-Stage Mechanism for Ordinal Peer AssessmentAlgorithmic Game Theory10.1007/978-3-319-99660-8_16(176-188)Online publication date: 11-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-319-99660-8_16
Shnayder VFrongillo RParke D(2016)Measuring performance of peer prediction mechanisms using replicator dynamicsProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060832.3060986(2611-2617)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060832.3060986
Shnayder VAgarwal AFrongillo RParkes DConitzer VBergemann DChen Y(2016)Informed Truthfulness in Multi-Task Peer PredictionProceedings of the 2016 ACM Conference on Economics and Computation10.1145/2940716.2940790(179-196)Online publication date: 21-Jul-2016
https://dl.acm.org/doi/10.1145/2940716.2940790
Dasgupta AGhosh ASchwabe DAlmeida VGlaser HBaeza-Yates RMoon S(2013)Crowdsourced judgement elicitation with endogenous proficiencyProceedings of the 22nd international conference on World Wide Web10.1145/2488388.2488417(319-330)Online publication date: 13-May-2013
https://dl.acm.org/doi/10.1145/2488388.2488417
Zhao HYang XLi X(2012)An incentive mechanism to reinforce truthful reports in reputation systemsJournal of Network and Computer Applications10.1016/j.jnca.2011.03.01135:3(951-961)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1016/j.jnca.2011.03.011
Witkowski J(2009)Eliciting honest reputation feedback in a Markov settingProceedings of the 21st International Joint Conference on Artificial Intelligence10.5555/1661445.1661498(330-335)Online publication date: 11-Jul-2009
https://dl.acm.org/doi/10.5555/1661445.1661498
Show More Cited By

Abstract

References

Cited By

Recommendations

Incentive-Compatible Learning of Reserve Prices for Repeated Auctions

Incentive compatible budget elicitation in multi-unit auctions

Incentive-compatible escrow mechanisms

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Share

Share this Publication link

Share on social media

Affiliations