Collusive game solutions via optimization
成果类型：
Article
署名作者：
Harrington, JE; Hobbs, BF; Pang, JS; Liu, A; Roch, G
署名单位：
Johns Hopkins University; Johns Hopkins University; Rensselaer Polytechnic Institute; Johns Hopkins University
刊物名称：
MATHEMATICAL PROGRAMMING
ISSN/ISSBN：
0025-5610
DOI：
10.1007/s10107-005-0622-3
发表日期：
2005
页码：
407-435
关键词：
摘要：
A Nash-based collusive game among a finite set of players is one in which the players coordinate in order for each to gain higher payoffs than those prescribed by the Nash equilibrium solution. In this paper, we study the optimization problem of such a collusive game in which the players collectively maximize the Nash bargaining objective subject to a set of incentive compatibility constraints. We present a smooth reformulation of this optimization problem in terms of a nonlinear complementarity problem. We establish the convexity of the optimization problem in the case where each player's strategy set is unidimensional. In the multivariate case, we propose upper and lower bounding procedures for the collusive optimization problem and establish convergence properties of these procedures. Computational results with these procedures for solving some test problems are reported.
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