Paper Info
Title
Co-Adaptive Strategies For Sequential Bargaining Problems With Discount Factors And Outside Options
Abstract
Bargaining is fundamental in social activities. Game-theoretic methodology has provided theoretic solutions for certain abstract models. Even for a simple model, this method demands substantial human intelligent effort in order to solve game-theoretic equilibriums. The analytic complexity increases rapidly when more elements are included in the models. In our previous work, we have demonstrated how coevolutionary algorithms can be used to find approximations to game-theoretic equilibriums of bargaining models that consider bargaining costs only. In this paper, we study more complicated bargaining models, in which outside option is taken into account besides bargaining cost. Empirical studies demonstrate that evolutionary algorithms are efficient in finding near-perfect solutions. Experimental results reflect the compound effects of discount factors and outside options upon bargaining outcomes. We argue that evolutionary algorithm is a practical tool for generating reasonably good strategies for complicated bargaining models beyond the capability of game theory.
Year
Venue
Keywords
2006
2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6
evolutionary computation,game theory,evolutionary algorithm,empirical study
Field
DocType
Citations 
Mathematical economics,Mathematical optimization,Adaptive strategies,Evolutionary algorithm,Computer science,Evolutionary computation,Game theory,Empirical research,Bargaining problem
Conference
7
PageRank 
References 
Authors
0.58
3
2
Name
Order
Citations
PageRank
Nanlin Jin1518.70
Edward P. K. Tsang289987.77