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- 461 creator arpita-ghosh.
- 461 creator patrick-hummel.
- 461 type InProceedings.
- 461 label "Implementing Optimal Outcomes in Social Computing: A Game-Theoretic Approach".
- 461 sameAs 461.
- 461 abstract "In many social computing applications such as online Q&A forums, the best contribution for each task receives some high reward, while all remaining contributions receive an identical, lower reward irrespective of their actual qualities. Suppose a mechanism designer (site owner) wishes to optimize an objective that is some function of the number and qualities of received contributions. When potential contributors are strategic agents, who decide whether to contribute or not to selfishly maximize their own utilities, is such a "best contribution" mechanism, M_B, adequate to implement an outcome that is optimal for the mechanism designer? We present a model to address this question, and prove the following implementation result: for any reasonable objective, the rewards for the best and remaining contributions in M_B can always be chosen so that the strategies used by agents in the unique symmetric equilibrium of M_B maximize the mechanism designer's expected utility. This optimal implementation property is not unique to M_B; we show how it can be extended to a broader family of mechanisms. We next address the setting where the mechanism designer does not know the parameters of the agents' utilities, as might be the case in practice, and show how she can learn the values of these optimal rewards for M_B. Finally, we ask whether this unique symmetric equilibrium implementing the optimal outcome is also the unique equilibrium of the game. Here we show that for a broad family of mechanisms that includes M_B, there also exist asymmetric equilibria that may not maximize the mechanism designer's utility.".
- 461 hasAuthorList authorList.
- 461 isPartOf proceedings.
- 461 keyword "crowdsourcing".
- 461 keyword "game theory".
- 461 keyword "social computing".
- 461 keyword "user-generated content".
- 461 title "Implementing Optimal Outcomes in Social Computing: A Game-Theoretic Approach".