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A data broker sells market segmentations created by consumer data to a producer with private production cost who sells a product to a unit mass of consumers with heterogeneous values. In this setting, I completely characterize the revenue-maximizing mechanisms for the data broker. In particular, every optimal mechanism induces quasi-perfect price discrimination. That is, the data broker sells the producer a market segmentation described by a cost-dependent cutoff, such that all the consumers with values above the cutoff end up buying and paying their values while the rest of consumers do not buy. The characterization of optimal mechanisms leads to additional economically relevant implications. I show that the induced market outcomes remain unchanged even if the data broker becomes more active in the product market by gaining the ability to contract on prices; or by becoming an exclusive retailer, who purchases both the product and the exclusive right to sell the product from the producer, and then sells to the consumers directly. Moreover, vertical integration between the data broker and the producer increases total surplus while leaving the consumer surplus unchanged, since consumer surplus is zero under any optimal mechanism for the data broker.

Several agents with privately known social values compete for a prize. The prize is allocated based on the claims of the agents, and the winner is subject to a limited penalty if he makes a false claim. If the number of agents is large, the optimal mechanism places all agents above a threshold onto a shortlist along with a fraction of agents below the threshold, and then allocates the prize to a random agent on the shortlist. When the number of agents is small, the optimal mechanism allocates the prize to the agent who makes the highest claim, but restricts the range of claims above and below. (JEL D63, D82)

I study a dynamic relationship where a principal delegates experimentation to an agent. Experimentation is modeled as a one-armed bandit that yields successes following a Poisson process. Its unknown intensity is high or low. The agent has private information, his type being his prior belief that the intensity is high. The agent values successes more than the principal does, so prefers more experimentation. The optimal mechanism is a cutoff rule in the belief space: the cutoff gives pessimistic types total freedom but curtails optimistic types’ behavior. Pessimistic types overexperiment while the most optimistic ones underexperiment. This delegation rule is time consistent. (JEL D23, D82, D83, O30)

We study decentralized mechanisms for allocating firms into search pools. The pools are created in response to noisy preference signals provided by consumers, who then browse the pools via costly random sequential search. Surplus-maximizing search pools are implementable in symmetric Nash equilibrium. Full extraction of the maximal surplus is implementable if and only if the distribution of consumer types satisfies a set of simple inequalities, which involve the relative fractions of consumers who like different products and the Bhattacharyya coefficient of similarity between their conditional signal distributions. The optimal mechanism can be simulated by a keyword auction with broad matching. (JEL C78, D44, D82)

Consider a principal with a good to allocate among a number of agents, each of whom wants the good. Each agent i knows the value the principal receives if he gives the good to i, but the principal does not. (This value need not coincide with the value to i of getting the good.) The principal can verify the agents’ private information at a cost, but cannot use transfers. There are a number of economic environments of interest that roughly correspond to this scenario; we discuss a few below. How does the principal maximize the expected gain from allocating the good less the costs of verification? We characterize optimal mechanisms for such settings. We construct an optimal mechanism with a particularly simple structure which we call a favored-agent mechanism. There is a threshold value and a favored agent, say i. If each agent other than i reports a value for the good below the threshold, then the good goes to the favored agent and no verification is required. If some agent other than i reports a value above the threshold, then the agent who reports the highest value is checked. This agent receives the good if and only if his claims are verified and the good goes to any other agent otherwise. In addition, we show that every optimal mechanism is essentially a randomization over optimal favored-agent mechanisms. In this sense, we can characterize the

This talk surveys three challenge areas for mechanism design and describes the role approximation plays in resolving them. Challenge 1: optimal mechanisms are parameterized by knowledge of the distribution of agent's private types. Challenge 2: optimal mechanisms require precise distributional information. Challenge 3: in multi-dimensional settings economic analysis has failed to characterize optimal mechanisms. The theory of approximation is well suited to address these challenges. While the optimal mechanism may be parameterized by the distribution of agent's private types, there may be a single mechanism that approximates the optimal mechanism for any distribution. While the optimal mechanism may require precise distributional assumptions, there may be approximately optimal mechanism that depends only on natural characteristics of the distribution. While the multi-dimensional optimal mechanism may resist precise economic characterization, there may be simple description of approximately optimal mechanisms. Finally, these approximately optimal mechanisms, because of their simplicity and tractability, may be much more likely to arise in practice, thus making the theory of approximately optimal mechanism more descriptive than that of (precisely) optimal mechanisms. The talk will cover positive resolutions to these challenges with emphasis on basic techniques, relevance to practice, and future research directions.

In an influential paper, Jean-Jacques Laffont and Jean Tirole (1986) formulated a principal– agent model of cost-based procurement and regulation and showed that the principal can implement the optimal mechanism by offering the agent a menu consisting of a continuum of linear contracts. Two related problems with applying this theory in practice have been that the economic logic and the underlying mathematics involved in calculating the optimal menu are quite complex, and the principal must be able to specify the agent’s entire disutility of effort function in order to calculate the optimal menu. As a result, the model has not been widely used in practice to either calculate actual incentive contracts or even to develop useful qualitative guidance about the nature of the optimal solution and how it is affected by various economic factors. The purpose of this paper is to show that dramatically simpler menus which are easy to understand and calculate and which have lower informational requirements, can, at least in some cases, capture a substantial share of the gains achievable by the fully optimal complex menu. In particular, this paper considers two-item menus where one item is a cost-reimbursement contract and the other item is a fixed-price contract. Such menus are called Fixed Price Cost Reimbursement (FPCR) menus. For the case where the agent’s utility is quadratic and the agent’s type is distributed uniformly, it is shown that the optimal FPCR menu always captures at least three-quarters of the gain that the optimal complex menu achieves (where the gain is measured relative to the result of using a cost-reimbursement contract). This paper’s research was originally motivated by a policy issue in defense procurement. In the United States, the Department of Defense (DOD) purchases almost all major weapons systems from sole-source contractors using contracting methods that essentially amount to using cost-reimbursement contracts. The best way to understand why DOD uses costreimbursement contracting methods is that they perform precisely the function identified in Laffont and Tirole’s principal–agent model; they guarantee, in a situation that amounts to a bilateral monopoly, that DOD and the firm can reach an agreement (i.e., they guarantee that the agent will always produce the good.) Of course the use of cost-reimbursement contracting methods