Minimum payments that reward honest reputation feedback

Jurca, Radu; Faltings, Boi

doi:10.1145/1134707.1134728

Cited by 69 publications

(57 citation statements)

References 18 publications

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“…According to Prelec (2004) the premise behind this is as follows: If people have a certain belief they tend to believe that this belief is more common than it actually is. We for our part use the peer-prediction method; it assigns scores for each rating based on its probability compared to the reference rating (Jurca et al, 2006).…”

Section: Weighting Schemementioning

confidence: 99%

“…For each rating Participant j has posted, he receives a score based on the probability distribution of the given rating and the scoring function used by the peer prediction method (Miller, Resnick, Zeckhauser, 2005). In our implementation, the scoring function is the linear programming function proposed by Jurca (2006). It maximizes the payoff when a participant is honest.…”

Section: Formulae and Notationmentioning

confidence: 99%

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A New Approach to Large-Scale Deliberation

Tanasijevic

Böhm

2013

2013 International Conference on Cloud and Green Computing

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Abstract. In this article, we propose a novel approach for identifying and evaluation of different solutions to discussed issues in online settings, based on the structure of a discussion of the topic in question. Our approach consists of three steps: (1) assigning weights to participants based on formal criteria such as degree of engagement in the discussion; (2) assigning scores to comments, taking the weights of authors and raters into account; (3) assigning scores to proposals, based on the scores of the pro and contra arguments. So an important idea is that individuals whose behavior is in line with our formal criteria have a higher influence on the decisions. Having built a respective online platform, we have evaluated the proposed model by means of an experiment with more than 100 participants who have discussed several topics relevant to them and a subsequent survey. In the survey, the majority of participants has expressed satisfaction with our forum model, including our weighting scheme. In particular, they have been fond of it regarding respect of the opinions of others.

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Section: Weighting Schemementioning

confidence: 99%

Section: Formulae and Notationmentioning

confidence: 99%

A New Approach to Large-Scale Deliberation

Tanasijevic

Böhm

2013

2013 International Conference on Cloud and Green Computing

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“…Computational trust models usually ignore the fact that many participants have economic incentives and behave rationally. The second solution category comprises the mechanism design approaches that use reputation as a sanctioning tool to enforce cooperation and establish trust among rational participants by penalizing bad peers and rewarding good ones [Dellarocas 2005a;Jurca and Faltings 2006;Miller et al 2005]. Rational peers are opportunistic and adapt their behaviors strategically to maximize their expected lifetime utilities.…”

Section: A Short Version Of This Article Is Published In Proceedings mentioning

confidence: 99%

Effective Usage of Computational Trust Models in Rational Environments

Aberer

2011

ACM Trans. Auton. Adapt. Syst.

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Computational reputation-based trust models using statistical learning have been intensively studied for distributed systems where peers behave maliciously. However practical applications of such models in environments with both malicious and rational behaviors are still very little understood. In this article, we study the relation between their accuracy measures and their ability to enforce cooperation among participants and discourage selfish behaviors. We provide theoretical results that show the conditions under which cooperation emerges when using computational trust models with a given accuracy, and how cooperation can still be sustained while reducing the cost and accuracy of those models.Specifically, we propose a peer selection protocol that uses a computational trust model as a dishonesty detector to filter out unfair ratings. We prove that such a model with reasonable misclassification error bound in identifying malicious ratings can effectively build trust and cooperation in the system, considering rationality of participants. These results reveal two interesting observations. First, the key to the success of a reputation system in a rational environment is not a sophisticated trust-learning mechanism, but an effective identity-management scheme to prevent whitewashing behaviors. Second, given an appropriate identity-management mechanism, a reputation-based trust model with a moderate accuracy bound can be used to effectively enforce cooperation in systems with both rational and malicious participants. As a result, in heterogeneous environments where peers use different algorithms to detect misbehavior of potential partners, cooperation may still emerge. We verify and extend these theoretical results to a variety of settings involving honest, malicious, and strategic players through extensive simulation. These results will enable a much more targeted, cost-effective and realistic design for decentralized trust management systems, such as needed for peer-to-peer, electronic commerce, or community systems.

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“…A second interesting direction is to filter out the reports that are very far from the common distribution [9]. Intuitively, these reports are either erroneous, or intentionally try to introduce significant perturbations towards desired values.…”

Section: Incentives For Truthful Re-portingmentioning

confidence: 99%

Reliable QoS monitoring based on client feedback

Jurca

Faltings

Binder

2007

Proceedings of the 16th International Conference on World Wide Web

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114

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Service-level agreements (SLAs) establish a contract between service providers and clients concerning Quality of Service (QoS) parameters. Without proper penalties, service providers have strong incentives to deviate from the advertised QoS, causing losses to the clients. Reliable QoS monitoring (and proper penalties computed on the basis of delivered QoS) are therefore essential for the trustworthiness of a service-oriented environment. In this paper, we present a novel QoS monitoring mechanism based on quality ratings from the clients. A reputation mechanism collects the ratings and computes the actual quality delivered to the clients. The mechanism provides incentives for the clients to report honestly, and pays special attention to minimizing cost and overhead.

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Minimum payments that reward honest reputation feedback

Cited by 69 publications

References 18 publications

A New Approach to Large-Scale Deliberation

A New Approach to Large-Scale Deliberation

Effective Usage of Computational Trust Models in Rational Environments

Reliable QoS monitoring based on client feedback

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