In second-price auctions, we find that improved targeting via enhanced information disclosure decreases revenue when there are two bidders and increases revenue if there are at least four symmetric bidders with values drawn from a distribution with a monotone hazard rate. With asymmetries, improved targeting increases revenue if the most frequent winner wins less than 30.4% of the time under a model in which shares are well defined, but can decrease revenue otherwise. We derive analogous results for position auctions. Finally, we show that revenue can vary nonmonotonically with the number of bidders who are able to take advantage of improved targeting.
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 first show that in settings where a contribution's value is determined primarily by an agent's expertise, and agents only strategically choose whether to contribute or not, contests can implement optimal outcomes: for any reasonable objective, the rewards for the best and remaining contributions in M B can always be chosen so that the outcome in the unique symmetric equilibrium of M B maximizes the mechanism designer's utility. We also show how the mechanism designer can learn these optimal rewards when she does not know the parameters of the agents' utilities, as might be the case in practice. We next consider settings where a contribution's value depends on both the contributor's expertise as well as her effort, and agents endogenously choose how much effort to exert in addition to deciding whether to contribute. Here, we show that optimal outcomes can never be implemented by contests if the system can rank the qualities of contributions perfectly. However, if there is noise in the contributions' rankings, then the mechanism designer can again induce agents to follow strategies that maximize his utility. Thus imperfect rankings can actually help achieve implementability of optimal outcomes when effort is endogenous and influences quality.
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