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CS522: Algorithmic and Economic Aspects of the Internet

CS522: Algorithmic and Economic Aspects of the Internet. Instructors: Nicole Immorlica (nickle@microsoft.com) Mohammad Mahdian (mahdian@microsoft.com). Reputation Mechanisms. Mechanisms that collect, aggregate, and distribute information about the “reputation” of participants.

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CS522: Algorithmic and Economic Aspects of the Internet

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  1. CS522: Algorithmic and Economic Aspects of the Internet Instructors: Nicole Immorlica (nickle@microsoft.com) Mohammad Mahdian (mahdian@microsoft.com)

  2. Reputation Mechanisms • Mechanisms that collect, aggregate, and distribute information about the “reputation” of participants. • PageRank and HITS can be considered reputation mechanisms. • Today, we talk about feedback-based reputation mechanisms used in electronic marketplaces.

  3. Why is reputation important? • Trust in a long-term relationship • Classical example: repeated prisoner’s dilemma • If both players follow a “tit for tat” strategy, they will always cooperate (if  is close to 1). B A

  4. Why is reputation important? • The expectation of reciprocity or retaliation in future interactions creates an incentive for good behavior. • What about repeated interaction with different people? • Society enforces good behavior by keeping track of reputation. • Also, organizations like Better Business Bureau, Zagat, Consumer Reports.

  5. Reputation on the Internet • Connection between individuals is significantly weaker. • People can share their opinions through reputation mechanisms. • Information: Lower quality, higher quantity

  6. Without reputation… • Buyers will not pay the full price of a high-quality item. • Sellers with high-quality items will not accept discounted prices. • Eventually, only low-quality sellers remain. • “market for lemons” [George Akerlof]

  7. Impact of reputation on prices • Experiment by Resnick et al. • ~200 postcards; half sold by an ebay account with strong reputation, the other half sold by a new account operated by the same person • The price was 8.1% higher for the seller with strong reputation. • Many other experimental studies…

  8. Challenges • Eliciting information • People don’t bother to give feedbacks Solution proposed: payment for feedback • Hard to elicit negative feedback (fear of retaliation) • Ensuring honest reports

  9. Challenges, cont’d • Distributing information • Name change New comers must be distrusted until they pay their dues. alternatively, can try to prevent name change. • Portability from system to system (e.g., between amazon and ebay)

  10. Challenges, cont’d • Aggregating information • It’s not obvious what’s the best way to aggregate the information. • Current methods: number/percentage of +/- feedback • Perhaps should be weighted by the value of the transaction and/or reputation of the feedback provider.

  11. A game-theoretic model • Chris Dellarocas (2003) • One seller, many buyers • Each buyer buys once. • In each round, a number of buyers bid for an item, which can be of high quality, or low quality. Auction is 2nd price. • The quality depends on the level of effort the seller exerts.

  12. The model • Seller offers a unit of good of high quality • Buyers bid their expected valuation based on seller’s feedback profile. Let w1, w2 denote the highest and second highest valuation for a high-quality good. • Seller decides whether to exert high effort at cost c, or low effort at cost 0; corresponding probability that the quality is low is  and  (<). • Buyer receives the good and leaves feedback. The feedback profile of the seller is updated.

  13. Binary feedback mechanism • Buyer leaves either a positive, or a negative feedback. • The feedback profile of the seller is a collection of N recent feedbacks. • Every time a new buyer gives feedback, it replaces a random feedback in the profile. • Feedback profile can be summarized by the number x of negative feedbacks.

  14. Equilibrium play • If s(x) is the probability that the seller exerts high effort, then the expected valuation of the buyer i is [s(x)(1-)+(1-s(x))(1-)]wi, (wi is i’s valuation for a high quality good.) • )Revenue of the auction = G(x) = [s(x)(-)+(1-)]w2.

  15. Equilibrium play • V(x) = seller’s expected payoff starting from reputation x. • V(x) = G(x) + s(x).Ucoop(x) + (1-s(x)).Ucheat(x). • Ucoop(x) = -c +  [ (1-x/N) V(x+1) + ( x/N + (1-)(1-x/N)) V(x) + (1-)(x/N) V(x-1) ]. • Similar expression for Ucheat(x).

  16. Results • Theorem. If w2/c is large enough, then the seller’s optimal strategy is:

  17. Distribution at the equilibrium

  18. Efficiency as a function of w2/c

  19. Conclusion • A reasonably high (but not perfect) degree of efficiency can be achieved. • Model can be generalized to cases where buyers sometimes don’t leave feedback. • The model predicts that it is optimal to treat missing feedbacks as positive. • Open question: more than one seller?

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