Alex Gershkov

Optimal voting schemes with costly information acquisition

A group of individuals with identical preferences must make a decision under uncertainty about which decision is best. Before the decision is made, each agent can privately acquire a costly and imperfect signal. We discuss how to design a mechanism for eliciting and aggregating the collected information so as to maximize ex-ante social welfare. We first show that, of all mechanisms, a sequential one is optimal and works as follows. At random, one agent at a time is selected to acquire information and report the resulting signal. Agents are informed of neither their position in the sequence nor of other reports. Acquiring information when called upon and reporting truthfully is an equilibrium. We next characterize the ex-ante optimal scheme among all ex-post efficient mechanisms. In this mechanism, a decision is made when the precision of the posterior exceeds a cut-off that decreases with each additional report. The restriction to ex-post efficiency is shown to be without loss when the available signals are sufficiently imprecise. On the other hand, ex-post efficient mechanisms are shown to be suboptimal when the cost of information acquisition is sufficiently small.

On the Equivalence of Bayesian and Dominant Strategy Implementation

We consider a standard social choice environment with linear utilities and independent, one-dimensional, private types. We prove that for any Bayesian incentive compatible mechanism there exists an equivalent dominant strategy incentive compatible mechanism that delivers the same interim expected utilities for all agents and the same ex ante expected social surplus. The short proof is based on an extension of an elegant result due to Gutmann, Kemperman, Reeds, and Shepp (1991). We also show that the equivalence between Bayesian and dominant strategy implementation generally breaks down when the main assumptions underlying the social choice model are relaxed or when the equivalence concept is strengthened to apply to interim expected allocations.

Tournaments with Midterm Reviews

In many tournaments investments are made over time. The question whether to conduct a review once at the end, or additionally at points midway through the tournament, is a strategic decision. If the latter course is chosen, then the designer must establish both a rule for aggregating the results of the different reviews and a rule for determining compensations. We first study the case of a fixed, exogenously given prize and then extend the analysis to the case where the prize is not fixed but may vary with the tournaments outcome. It is shown that (1) it is always optimal to assign a higher weight to the final review; (2) this weight increases with the dominance of the first-stage effort in determining the final reviews outcome. When the prize is not fixed, the optimal design generates an asymmetric tournament in the second stage that favors the winner of the midterm review.

Efficient sequential assignment with incomplete information

We study the welfare maximizing assignment of several heterogeneous, commonly ranked objects to impatient agents with privately known characteristics who arrive sequentially according to a Poisson or renewal process. There is a deadline after which no more objects can be allocated. We first show that the dynamically efficient allocation, characterized by Albright [Albright, S.C., 1974. Optimal sequential assignments with random arrival times. Manage. Sci. 21 (1), 60-67], is implementable by the dynamic version of VCG mechanism. We then obtain several properties of the welfare maximizing policy using stochastic dominance measures of increased variability and majorization arguments. We also propose redistribution mechanisms that 1) implement the efficient allocation, 2) satisfy individual rationality, 3) never run a budget deficit, 4) may run a budget surplus that vanishes asymptotically.

Revenue maximization in the dynamic knapsack problem

We analyze maximization of revenue in the dynamic and stochastic knapsack problem where a given capacity needs to be allocated by a given deadline to sequentially arriving agents. Each agent is described by a two-dimensional type that reflects his capacity requirement and his willingness to pay per unit of capacity. Types are private information. We first characterize implementable policies. Then we solve the revenue maximization problem for the special case where there is private information about per-unit values, but capacity needs are observable. After that we derive two sets of additional conditions on the joint distribution of values and weights under which the revenue maximizing policy for the case with observable weights is implementable, and thus optimal also for the case with twodimensional private information. In particular, we investigate the role of concave continuation revenues for implementation. We also construct a simple policy for which per-unit prices vary with requested weight but not with time, and we prove that it is asymptotically revenue maximizing when available capacity and time to the deadline both go to infinity. This highlights the importance of nonlinear as opposed to dynamic pricing.

Optimal search, learning and implementation☆

We characterize the incentive compatible, constrained efficient policy (“second-best”) in a dynamic matching environment, where impatient, privately informed agents arrive over time, and where the designer gradually learns about the distribution of agentsʼ values. We also derive conditions on the learning process ensuring that the complete-information, dynamically efficient allocation of resources (“first-best”) is incentive compatible. Our analysis reveals and exploits close, formal relations between the problem of ensuring implementable allocation rules in our dynamic allocation problems with incomplete information and learning, and between the classical problem, posed by Rothschild (1974) [20], of finding optimal stopping policies for search that are characterized by a reservation price property.

Efficient Tournaments within Teams

We analyze incentive problems in team and partnership structures where the only available information to condition a contract on is a partial and noisy ranking which specifies who comes first in efforts among the competing partners. This enables us to ensure both first-best efficient effort levels for all partners and the redistribution of output only among partners. Our efficiency result is obtained for a wide range of cost and production functions. Copyright (c) 2009, RAND.

Dynamic allocation and pricing: A mechanism design approach

This paper illustrates the benefits of applying mechanism design techniques to questions in revenue management, in particular to dynamic allocation and pricing problems. It is demonstrated that the solution to a sequential stochastic assignment problem under complete information can also be implemented under incomplete information by a variation of the Vickrey–Clarke–Groves mechanism. More generally, we argue that the mechanism design focus on implementable allocations rather than on prices yields many valuable insights about dynamic RM models. Finally, we also briefly survey some of the recent literature on dynamic mechanism design.

When Queueing is Better than Push and Shove

We address the scheduling problem of reordering an existing queue into its efficient order through trade. To that end, we consider individually rational and balanced budget direct and indirect mechanisms. We show that this class of mechanisms allows us to form efficient queues provided that existing property rights for the service are small enough to enable trade between the agents. In particular, we show on the one hand that no queue under a fully deterministic service schedule such as first-come, first-serve can be dissolved efficiently and meet our requirements. If, on the other hand, the alternative is service anarchy (ie. a random queue), every existing queue can be transformed into an efficient order.

Revenue Maximizing Mechanisms with Strategic Customers and Unknown Demand

A designer allocates several indivisible objects to a stream of randomly arriving agents. The long-lived agents are privately informed about their value for an object, and about their arrival time to the market. The designer learns about future arrivals from past arrivals, while agents strategically choose when to make themselves available for trade. We characterize revenue maximizing direct mechanism and offer a simple indirect mechanism that captures a substantial part of the revenues of the revenue maximizing mechanism.