Display advertising has traditionally been sold via guaranteed contracts -a guaranteed contract is a deal between a publisher and an advertiser to allocate a certain number of impressions over a certain period, for a pre-specified price per impression. However, as spot markets for display ads, such as the RightMedia Exchange, have grown in prominence, the selection of advertisements to show on a given page is increasingly being chosen based on price, using an auction. As the number of participants in the exchange grows, the price of an impressions becomes a signal of its value. This correlation between price and value means that a seller implementing the contract through bidding should offer the contract buyer a range of prices, and not just the cheapest impressions necessary to fulfill its demand.Implementing a contract using a range of prices, is akin to creating a mutual fund of advertising impressions, and requires randomized bidding. We characterize what allocations can be implemented with randomized bidding, namely those where the desired share obtained at each price is a non-increasing function of price. In addition, we provide a full characterization of when a set of campaigns are compatible and how to implement them with randomized bidding strategies.

We model the economics of incentivizing high-quality user generated content (UGC), motivated by settings such as online review forums, question-answer sites, and comments on news articles and blogs. We provide a game-theoretic model within which to study the problem of incentivizing high quality UGC, in which contributors are strategic and motivated by exposure. Our model has the feature that both the quality of contributions as well as the extent of participation is determined endogenously in a free-entry Nash equilibrium.The model predicts, as observed in practice, that if exposure is independent of quality, there will be a flood of low quality contributions in equilibrium. An ideal mechanism in this context would elicit both high quality and high participation in equilibrium, with near-optimal quality as the available attention diverges, and should be easily implementable in practice. We consider a very simple elimination mechanism, which subjects each contribution to rating by some number A of viewers, and eliminates any contributions that are not uniformly rated positively. We construct and analyze free-entry Nash equilibria for this mechanism, and show that A can be chosen to achieve quality that tends to optimal, along with diverging participation, as the number of viewers diverges.

We investigate the design of mechanisms to incentivize high quality outcomes in crowdsourcing environments with strategic agents, when entry is an endogenous, strategic choice. Modeling endogenous entry in crowdsourcing markets is important because there is a nonzero cost to making a contribution of any quality which can be avoided by not participating, and indeed many sites based on crowdsourced content do not have adequate participation. We use a mechanism with monotone, rank-based, rewards in a model where agents strategically make participation and quality choices to capture a wide variety of crowdsourcing environments, ranging from conventional crowdsourcing contests with monetary rewards such as TopCoder, to crowdsourced content as in online Q&A forums.We begin by explicitly constructing the unique mixedstrategy equilibrium for such monotone rank-order mechanisms, and use the participation probability and distribution of qualities from this construction to address the question of designing incentives for two kinds of rewards that arise in the context of crowdsourcing. We first show that for attention rewards that arise in the crowdsourced content setting, the entire equilibrium distribution and therefore every increasing statistic including the maximum and average quality (accounting for participation), improves when the rewards for every rank but the last are as high as possible. In particular, when the cost of producing the lowest possible quality content is low, the optimal mechanism displays all but the poorest contribution. We next investigate how to allocate rewards in settings where there is a fixed total reward that can be arbitrarily distributed amongst participants, as in crowdsourcing contests. Unlike models with exogenous entry, here the expected number of participants can be increased by subsidizing entry, which could potentially improve the expected value of the best contribution. However, we show that subsidizing entry does not improve the expected quality of the best contribution, although it may improve the expected quality of the average contribution. In fact, we show that free entry is dominated by taxing entry-making all entrants pay a small fee, which is rebated to the winner along with whatever rewards were already assigned, can improve the quality of the best contribution over a winner-take-all contest with no taxes.

A large fraction of user-generated content on the Web, such as posts or comments on popular online forums, consists of abuse or spam. Due to the volume of contributions on popular sites, a few trusted moderators cannot identify all such abusive content, so viewer ratings of contributions must be used for moderation. But not all viewers who rate content are trustworthy and accurate. What is a principled approach to assigning trust and aggregating user ratings, in order to accurately identify abusive content?In this paper, we introduce a framework to address the problem of moderating online content using crowdsourced ratings. Our framework encompasses users who are untrustworthy or inaccurate to an unknown extent -that is, both the content and the raters are of unknown quality. With no knowledge whatsoever about the raters, it is impossible to do better than a random estimate. We present efficient algorithms to accurately detect abuse that only require knowledge about the identity of a single 'good' agent, who rates contributions accurately more than half the time. We prove that our algorithm can infer the quality of contributions with error that rapidly converges to zero as the number of observations increases; we also numerically demonstrate that the algorithm has very high accuracy for much fewer observations. Finally, we analyze the robustness of our algorithms to manipulation by adversarial or strategic raters, an important issue in moderating online content, and quantify how the performance of the algorithm degrades with the number of manipulating agents.

We consider a model of repeated online auctions in which an ad with an uncertain click-through rate faces a random distribution of competing bids in each auction and there is discounting of payoffs. We formulate the optimal solution to this explore/exploit problem as a dynamic programming problem and show that efficiency is maximized by making a bid for each advertiser equal to the advertiser's expected value for the advertising opportunity plus a term proportional to the variance in this value divided by the number of impressions the advertiser has received thus far. We then use this result to illustrate that the value of incorporating active exploration into a machine learning system in an auction environment is exceedingly small.