Analyzing the economic efficiency of eBay-like online reputation reporting mechanisms

ABSTRACT

This paper introduces a model for analyzing marketplaces, such as eBay, which rely on binary reputation mechanisms for quality signaling and quality control. In our model sellers keep their actual quality private and choose what quality to advertise. The reputation mechanism is primarily used to determine whether sellers advertise truthfully. Buyers may exercise some leniency when rating sellers, which needs to be compensated by corresponding strictness when judging sellers'feedback profiles. It is shown that, the more lenient buyers are when rating sellers, the more likely it is that sellers will find it optimal to settle down to steady-state quality levels, as opposed to oscillating between good quality and bad quality. Furthermore, the fairness of the market outcome is determined by the relationship between rating leniency and strictness when assessing a seller's feedback profile. If buyers judge sellers too strictly (relative to how leniently they rate) then, at steady state, sellers will be forced to understate their true quality. On the other hand, if buyers judge too leniently then sellers can get away with consistently overstating their true quality. An optimal judgment rule, which results in outcomes where at steady state buyers accurately estimate the true quality of sellers, is analytically derived. However, it is argued that this optimal rule depends on several system parameters, which are difficult to estimate from the information that marketplaces, such as eBay, currently make available to their members. It is therefore questionable to what extent unsophisticated buyers are capable of deriving and applying it correctly in actual settings.

REFERENCES

• 1: Akerlof, G. (1970) The market for 'lemons': Quality uncertainty and the market mechanism. Quarterly Journal of Economics 84, pp. 488-500.
• 2: Blyth, C.R. and Still H.A. (1983) Binomial Confidence Intervals. Journal of the American Statistical Association 78, pp. 108-116.
• 3: Bresee, J.S., Heckerman, D., and Kadie, C. (1998) Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14 th Conference on Uncertainty in Artificial Intelligence (UAI-98), pp. 43-52, San Francisco, July 24-26, 1998.
• 4: Dewan, S. and Hsu, V. (2001) Trust in Electronic Markets: Price Discovery in Generalist Versus Specialty Online Auctions. Working Paper. January 31, 2001.
• 5: Kollock, P. (1999) The Production of Trust in Online Markets. In Advances in Group Processes (Vol. 16), eds. E.J. Lawler, M. Macy, S. Thyne, and H.A. Walker, Greenwich, CT: JAI Press.
• 6: Paul Resnick , Ko Kuwabara , Richard Zeckhauser , Eric Friedman, Reputation systems, Communications of the ACM, v.43 n.12, p.45-48, Dec. 2000 [doi>10.1145/355112.355122]
• 7: Resnick, P. and Zeckhauser, R. (2001) Trust Among Strangers in Internet Transactions: Empirical Analysis of eBay's Reputation System. Working Paper for the NBER workshop on empirical studies of electronic commerce. January 2001.
• 8: Rogerson, W.P. (1983) Reputation and product quality. Bell Journal of Economics, Vol. 14 (2), pp. 508-16.
• 9: Badrul Sarwar , George Karypis , Joseph Konstan , John Riedl, Analysis of recommendation algorithms for e-commerce, Proceedings of the 2nd ACM conference on Electronic commerce, p.158-167, October 17-20, 2000, Minneapolis, Minnesota, United States [doi>10.1145/352871.352887]
• 10: Schmalensee, R. (1978). Advertising and Product Quality. Journal of Political Economy, Vol. 86, pp. 485-503.
• 11: J. Ben Schafer , Joseph A. Konstan , John Riedl, E-Commerce Recommendation Applications, Data Mining and Knowledge Discovery, v.5 n.1-2, p.115-153, January-April 2001 [doi>10.1023/A:1009804230409]
• 12: Shapiro, C. (1982) Consumer Information, Product Quality, and Seller Reputation. Bell Journal of Economics 13 (1), pp 20-35, Spring 1982.
• 13: Smallwood, D. and Conlisk, J. (1979). Product Quality in Markets Where Consumers Are Imperfectly Informed. Quarterly Journal of Economics. Vol. 93, pp. 1-23.
• 14: Wilson, Robert (1985). Reputations in Games and Markets. In Game-Theoretic Models of Bargaining, edited by Alvin Roth, Cambridge University Press, pp. 27-62.

INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.1 Combinatorics
          Subjects: Combinatorial algorithms

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

  K. Computing Milieux
  K.1 THE COMPUTER INDUSTRY
      Subjects: Markets

General Terms:
Algorithms, Design, Performance

Keywords:
electronic commerce, reputation systems, trust in electronic markets