Optimal Mechanisms with Finite Agent Types

In some mechanism design problems, finite-type spaces may be natural ones to consider, yet the current literature is dominated by analyses of continuous-type spaces. This probably derives from the intellectual dominance of the early work in this area. Here we present an analysis of the finite-state case that unifies and generalizes current understanding of these problems. We analyze general quasi-linear utility functions among asymmetric agents with an arbitrary number of finite types, in the context of incentive-compatible direct revelation games. A key part of the analysis is the relationship between expected benefit functions that feature alternative forms of supermodularity that translate into relaxations or restrictions of the original problem. The required features can be attained with a range of assumptions on the model primitives, each of which can support the results. This unified approach can suggest a range of alternative assumption combinations, often more general than their counterparts in the continuous-space literature, and each of which can reduce the problem to a more tractable form. Also, mechanism design problems with finite-type spaces can require conscious attention to how the principal handles ties, which are probability-zero events, and hence innocuous in continuous spaces.

[1]  Guofu Tan,et al.  Entry and R & D in procurement contracting , 1992 .

[2]  Dilip Mookherjee,et al.  Contract Complexity, Incentives, and the Value of Delegation , 1997 .

[3]  R. Myerson Incentive Compatibility and the Bargaining Problem , 1979 .

[4]  John. Moore,et al.  Contracting between two parties with private information , 1984 .

[5]  Dilip Mookherjee,et al.  A theory of responsibility centers , 1992 .

[6]  S. Dasgupta,et al.  Competition for Procurement Contracts and Underinvestment , 1990 .

[7]  E. Maskin,et al.  The Implementation of Social Choice Rules: Some General Results on Incentive Compatibility , 1979 .

[8]  Charles H. Kriebel,et al.  Asymmetric Information, Incentives and Intrafirm Resource Allocation , 1982 .

[9]  E. Maskin,et al.  Monopoly with Incomplete Information , 1984 .

[10]  Roger B. Myerson,et al.  Optimal Auction Design , 1981, Math. Oper. Res..

[11]  J. Laffont,et al.  The Theory of Incentives: The Principal-Agent Model , 2001 .

[12]  Contents , 2016, European Neuropsychopharmacology.

[13]  Michele Piccione,et al.  Cost-reducing investment, optimal procurement and implementation by auctions , 1996 .

[14]  H. Cheng Optimal Auction Design with Discrete Bidding , 2004 .

[15]  Donald M. Topkis,et al.  Minimizing a Submodular Function on a Lattice , 1978, Oper. Res..

[16]  Igor Vaysman,et al.  A model of cost-based transfer pricing , 1996 .

[17]  Ying Li,et al.  Strategic Capacity Investments and Competition for Supply Contracts , 2007 .

[18]  M. Harris,et al.  ALLOCATION MECHANISMS AND THE DESIGN OF AUCTIONS , 1981 .

[19]  Lawrence M. Ausubel,et al.  The Optimality of Being Efficient , 1999 .

[20]  O. Hart Optimal Labour Contracts Under Asymmetric Information: An Introduction (Now published in Review of Economic Studies, (January 1983).) , 1982 .

[21]  R. Tyrrell Rockafellar,et al.  Convex Analysis , 1970, Princeton Landmarks in Mathematics and Physics.

[22]  K. Talluri,et al.  The Theory and Practice of Revenue Management , 2004 .

[23]  Dilip Mookherjee,et al.  Dominant Strategy Implementation of Bayesian incentive Compatible Allocation Rules , 1992 .