We survey the recent literature on the role of information in mechanism design. First, we discuss an emerging literature on the role of endogenous payoff and strategic information for the design and the efficiency of the mechanism. We specifically consider information management in the form of acquisition of new information or disclosure of existing information. Second, we argue that in the pre...

We study an adverse selection model in which a principal will allocate a set of nonidentical objects among privately informed agents. Combinatorial auctions provide an important class of examples. Agents have private information that is parametrized by a one dimensional type. The principal collects type reports from the agents, computes their valuations for di¤erent sets, and then decides on an...

Article history: Received 27 November 2005 Received in revised form 10 June 2008 Accepted 11 June 2008 Available online 28 June 2008

We study Bayesian mechanism design in situations where agents’ information may be multi-dimensional, concentrating on mechanisms that lead to efficient allocations. Our main result is that a generalization of the well-known Vickrey-Clarke-Groves mechanism maximizes the planner’s “revenue” among all efficient mechanisms. This result is then used to study multiple object auctions in situations wh...

This report emerged from the seminar Mechanism Design held in the summer term 2007 at the ITI Sanders, Universität Karlsruhe (TH). It is based on the paper Algorithmic Mechanism Design of Noan Nisan and Amir Ronen [5]. Within this report we try to present their results in an illustrative manner using several additional examples. Mechanism Design is a subfield of game theory. It deals with probl...

Computational issues in mechanism design are important, but have received insufficient research interest until recently. Limited computing hinders mechanism design in several ways, and presents deep strategic interactions between computing and incentives. On the bright side, the vast increase in computing power has enabled better mechanisms. Perhaps most interestingly, limited computing of the ...

We put forward a modeling and algorithmic framework to design and optimize mechanisms in dynamic industrial environments where a designer can make use of the data generated in the process to automatically improve future design. Our solution, coined reinforcement mechanism design, is rooted in game theory but incorporates recent AI techniques to get rid of nonrealistic modeling assumptions and t...

We study the problem of welfare maximization in a novel setting motivated by the standard stochastic two-stage optimization with recourse model. We identify and address algorithmic and game-theoretic challenges that arise from this framework. In contrast, prior work in algorithmic mechanism design has focused almost exclusively on optimization problems without uncertainty. We make two kinds of ...

This paper studies a model of mechanism design when agents’ preferences over transfers need not be quasilinear. In a general model of non-quasilinearity, we characterize dominant strategy incentive compatible mechanisms using a monotonicity property. We also establish a revenue uniqueness result: for every dominant strategy implementable allocation rule, there is a unique payment rule that can ...

We consider algorithmic problems in a distributed setting where the participants cannot be assumed to follow the algorithm but rather their own self-interest. As such participants, termed agents, are capable of manipulating the algorithm, the algorithm designer should ensure in advance that the agents’ interests are best served by behaving correctly. Following notions from the field of mechanis...