Co-adaptive Strategies for Sequential Bargaining Problems with Discount Factors and Outside Options

Nanlin Jin, Edward Tsang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

11 Citations (Scopus)

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 co-evolutionary 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.
Original languageEnglish
Title of host publication2006 IEEE International Conference on Evolutionary Computation
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages2149-2156
ISBN (Print)0-7803-9487-9
DOIs
Publication statusPublished - 2006

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