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Reputation Systems

Join Paul Resnick, Associate Professor at the University of Michigan, for an engaging guest lecture on reputation systems. Explore the theory behind reputation systems, when and why they function effectively, and the design challenges they pose. Understand real-world applications through case studies like NPAssist and eBay, and learn about ongoing research in feedback provision and trust dynamics. This lecture aims to provide participants with a comprehensive overview of reputation systems, their benefits, limitations, and future opportunities in diverse settings.

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Reputation Systems

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  1. Reputation Systems Guest Lecture Paul Resnick Associate Professor Univ. of Michigan School of Information presnick@umich.edu

  2. Learning Objectives • Understand • What a reputation system is • Theory about when and why it should work • Open research questions • Participate in design • Recognize situations when it might be helpful • Raise some of the difficult design challenges

  3. Outline • What is a reputation system? • Theory: when/why they should work • Empirical results • Design space • Case study: NPAssist recommender

  4. Definition • A Reputation System… • Collects • Distributes • Aggregates • …information about behavior

  5. Examples • BBB • Bizrate • eBay • Expertise sites • Epinions “top reviewers” • Slashdot karma system

  6. Why Reputation Systems • Interacting with strangers • Sellers (Exchange Partners) Vary • Skill • Effort • Ethics

  7. Other Trust-Inducing Mechanisms in E-commerce • Insurance • Escrow • Fraud Prosecution

  8. How Reputation Systems Should Work • Information • Incentive • Self-selection

  9. Some Issues • Anonymity • Name changes • Name trades • Lending reputations • Eliciting evaluation • Honesty of evaluations

  10. Anonymity Analysis

  11. 1L Pseudonyms • Third-party issues pseudonyms • No cost • Not replaceable • Reveal name to third party • Don’t reveal mapping of name to pseudonym

  12. Empirical Results: eBay • Feedback is provided • It’s almost all positive • Reputations are informative • Reputation benefits • Effect on probability of sale • Effect on price

  13. Provision of Feedback • Negatives: paid but did not receive; seller cancelled; not as advertised; communication • Neutrals: slow shipping, not as advertised, communication

  14. Feedback Profiles of Buyers and Sellers

  15. Predicting Problematic Transactions • Logistic Regressionf(0,0) = 1.91% f(100,0) = .18% f(100,3) = .53% N = 36233 Beginning Block Number 0. Initial Log Likelihood Function -2 Log Likelihood 2194.3468 -2 Log Likelihood 2075.420 Dependent Variable.. NEGNEUT ---------------------- Variables in the Equation ----------------------- Variable B S.E. Wald df Sig R Exp(B) LNNPOS .7712 .1179 42.7907 1 .0000 .1363 2.1624 LNPOS -.5137 .0475 116.8293 1 .0000 -.2288 .5983 Constant -3.9399 .1291 931.3828 1 .0000

  16. Predictive Value

  17. Some Recently Completed Work • Experiment: does reputation affect profit? • Many positives: Yes, but only a little (8.1%) • One or two negatives: No • Incentives for quality feedback provision • Can pay based on agreement among raters

  18. Studies Currently Underway • Feedback provision (empirical) • Reciprocation, altruism, and free riding • Dynamics: learning and selection (empirical) • Geography: trust and trustworthiness by state

  19. Design Space • Rating scales • Aggregation of ratings • Who rates? • Incentives for raters • Identification/Anonymity • Exchange partners • Evaluation providers

  20. Case Study • Goal: help Michigan non-profits select consultants and other service providers • Is this a good candidate for a reputation system?

  21. Case Study • Goal: help Michigan non-profits select consultants and other service providers • Is this a good candidate for a reputation system? • Interacting with strangers • Sellers (Exchange Partners) Vary • Skill • Effort • Ethics

  22. Case Study Design Choices • Rating scales • Aggregation of ratings • Who rates? • Incentives for raters • Identification/Anonymity • Exchange partners • Evaluation providers

  23. Summary • RS inform, incent, select • Opportunity for RS: interactions with strangers • Design space • Scales, aggregation, raters, incentives, anonymity

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