We study robustly optimal mechanisms for selling multiple items. The seller maximizes revenue against a worst-case distribution of a buyer’s valuations within a set of distributions, called an “ambiguity” set. We identify the exact forms of robustly optimal selling mechanisms and the worst-case distributions when the ambiguity set satisfies various moment conditions on the values of subsets of goods. The analysis reveals general properties of the ambiguity set that justifies categorical bundling, which includes separate sales and pure bundling as special cases.

We develop a methodology for analyzing the revenue and efficiency performance of auctions when buyers have private information about their willingness to pay and ability to pay. We then apply the framework to scenarios involving standard auction mechanisms. In the simplest case, where bidders face absolute spending limits, first-price auctions yield higher expected revenue and social surplus than second-price auctions. The revenue dominance of first-price auctions over second-price auctions carries over to the case where bidders have access to credit. These rankings are explained by differences in the extent to which financial constraints bind in different auction formats.

This article studies how a recommender system may incentivize users to learn about a product collaboratively. To improve the incentives for early exploration, the optimal design trades off fully transparent disclosure by selectively overrecommending the product (or “spamming”) to a fraction of users. Under the optimal scheme, the designer spams very little on a product immediately after its release but gradually increases its frequency; she stops it altogether when she becomes sufficiently pessimistic about the product. The recommender’s product research and intrinsic/naive users “seed” incentives for user exploration and determine the speed and trajectory of social learning. Potential applications for various Internet recommendation platforms and implications for review/ratings inflation are discussed.

The holdup problem arises when parties negotiate to divide the surplus generated by their relationship specific investments. We study this problem in a dynamic model of bargaining and investment which, unlike the stylized static model, allows the parties to continue to invest until they agree on the terms of trade. The investment dynamics overturns the conventional wisdom dramatically. First, the holdup problem need not entail underinvestment when the parties are sufficiently patient. Second, inefficiencies can arise unambiguously in some cases, but they are not caused by the sharing of surplus per se but rather by a failure of an individual rationality constraint. Copyright The Econometric Society 2004.

We consider a decision maker (DM) who, before taking an action, seeks information by allocating her limited attention dynamically over different news sources that are biased toward alternative actions. Endogenous choice of information generates rich dynamics: the chosen news source either reinforces or weakens the prior, shaping subsequent attention choices, belief updating, and the final action. The DM adopts a learning strategy biased toward the current belief when the belief is extreme and against that belief when it is moderate. Applied to consumption of news media, observed behavior exhibits an “ echo-chamber” effect for partisan voters and a novel “ anti-echo-chamber” effect for moderates.

The main difference in CG was the "discontinuous" effect of the cap.CG assumed c(x) = x for all x.When there was a cap equal to m, costs became c(x) = x for x ≤ m and c(x) = ∞ for x >

Procurement of an innovation often requires substantial effort by potential suppliers. Motivating effort may be difficult if the level of effort and quality of the resulting innovation are unverifiable, if innovators cannot benefit directly by marketing their innovations, and if the buyer cannot extract up-front payments from suppliers. We study the use of contests to procure an innovation in such an environment. An auction in which two suppliers are invited to innovate and then bid their prizes is optimal in a large class of contests. If contestants are asymmetric, it is optimal to handicap the most efficient one.

We study efficient and stable mechanisms in matching markets when the number of agents is large and individuals’ preferences and priorities are drawn randomly. When agents’ preferences are uncorrelated, then both efficiency and stability can be achieved in an asymptotic sense via standard mechanisms such as deferred acceptance and top trading cycles. When agents’ preferences are correlated over objects, however, these mechanisms are either inefficient or unstable, even in an asymptotic sense. We propose a variant of deferred acceptance that is asymptotically efficient, asymptotically stable, and asymptotically incentive compatible. This new mechanism performs well in a counterfactual calibration based on New York City school choice data.