Designing Reputation Mechanisms for Efficient Trade

24 Pages Posted: 27 Apr 2010

See all articles by Christina Aperjis

Christina Aperjis

Hewlett-Packard Enterprise - Social Computing Lab

Ramesh Johari

Stanford University

Date Written: January 27, 2010

Abstract

A seller in an online marketplace with an effective reputation mechanism should expect that dishonest behavior results in higher payments now, while honest behavior results in higher reputation – and thus higher payments – in the future. We study two widely used classes of reputation mechanisms. First, we show that weighting all past ratings equally gives sellers an incentive to falsely advertise. This result supports eBay's recent decision to base the Positive Feedback percentage on the past 12 months of feedback, rather than the entire lifetime of the seller. We then study reputation mechanisms that weight recent ratings more heavily. We characterize conditions under which it is optimal for the seller to advertise truthfully, and relate seller truthfulness to returns to reputation. If there is no reputation premium for a low value item, we show the following dichotomy: under increasing returns to reputation the optimal strategy of a sufficiently patient and sufficiently high quality seller is to always advertise honestly, while under decreasing returns to reputation the seller will not always be honest. Finally, we suggest approaches for designing a reputation mechanism that maximizes the range of parameters for which it is optimal for the seller to be truthful. We show that mechanisms that use information from a larger number of past transactions tend to provide incentives for patient sellers to be more truthful, but for higher quality sellers to be less truthful.

Keywords: reputation mechanisms, ratings, online markets

Suggested Citation

Aperjis, Christina and Johari, Ramesh, Designing Reputation Mechanisms for Efficient Trade (January 27, 2010). Available at SSRN: https://ssrn.com/abstract=1596839 or http://dx.doi.org/10.2139/ssrn.1596839

Christina Aperjis (Contact Author)

Hewlett-Packard Enterprise - Social Computing Lab ( email )

1501 Page Mill Road
Palo Alto, CA 9434
United States

Ramesh Johari

Stanford University ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

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