Xiang Yan
ABSTRACT
A reputation system should incentivize users to obtain and reveal estimates of content quality. It should also aggregate these estimates to establish content reputation in a way that counters strategic manipulation. Mechanisms have been proposed in recent literature that offer financial incentives to induce these desirable outcomes. In this paper, to systematically study what we believe to be fundamental characteristics of these mechanisms, we view them as information markets designed to assess content quality, and refer to them as reputation markets. Specifically, we develop a rational expectations equilibrium model to study how incentives created by reputation markets should influence community behavior and the accuracy of assessments. Our analysis suggests that reputation markets offer a number of desirable features:
- As the quality of information improves or the cost of information acquisition decreases, reputation assessments become increasingly robust to manipulation.
- If users can pay to acquire information, errors in reputation assessments do not depend on uncertainty in the manipulator's intent.
- Reputation distortion incurs cost to the manipulator, resulting in cash transfers to other users.
- Pseudonyms do not help a manipulator distort reputations.
- G. Adomavicius and A. Tuzhilin. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6): 734--749, 2005. Google Scholar
Digital Library
- C. Avery, P. Resnick, and R. Zeckhauser. The Market for Evaluations. American Economic Review, 89(3):564--584, 1999.Google ScholarCross Ref
- R. Bhattacharjee and A. Goel. Algorithms and incentives for robust ranking. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM, 2007. Google ScholarDigital Library
- C. Dellarocas. Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms. Management Science, 52(10): 1577--1593, 2006. Google ScholarDigital Library
- S. Grossman and J. Stiglitz. On the Impossibility of Informationally Efficient Markets. The American Economic Review, 70(3): 393--408, 1980.Google Scholar
- R. Hanson and R. Oprea. Manipulators increase information market accuracy. Working paper. http://hanson.gmu.edu/biashelp.pdf.Google Scholar
- R. Hanson, R. Oprea, and D. Porter. Information Aggregation and Manipulation in an Experimental Market. Journal of Economic Behavior and Organization, 60(4): 449--459.Google ScholarCross Ref
- R. Jurca and B. Faltings. Collusion-resistant, incentive-compatible feedback payments. In Proceedings of the Seventh ACM Conference on Electronic Commerce. ACM, 2007. Google ScholarDigital Library
- N. Miller, P. Resnick, and R. Zeckhauser. Eliciting Informative Feedback: The Peer-Prediction Method. Management Science}, 51(9): 1359--1373, 2005. Google ScholarDigital Library
Index Terms
- Reputation markets
Recommendations
Strategic behavior in online reputation systems: evidence from revoking on eBay
This study examines how sellers respond to changes in the design of reputation systems on eBay. Specifically, we focus on one particular strategic behavior on eBay's reputation system: sellers' explicit retaliation against negative feedback provided by ...
Reputation and Pricing Dynamics in Online Markets
We study the economic interactions among sellers and buyers in online markets. In such markets, buyers have limited information about the product quality, but can observe the sellers’ reputations which depend on their past transaction histories and ...
On Robustness of Forward-looking in Sponsored Search Auction
We analyze the robustness of pure Nash Equilibria of sponsored search auctions, based on the forward-looking response function. To consider the robustness of such refined solution concept, we first study the convergence property of several adjustment ...
Comments