A game theoretical approach for solving winner determination problems

Determining the winners in combinatorial auctions to maximize the auctioneer's revenue is an np-complete problem. computing an optimal solution requires huge computation time in some instances. in this paper,we apply three concepts of the game theory to design an approximation algorithm: the stability of the nash equilibrium,the self-learning of the evolutionary game,and the mistake making of the trembling hand assumption. according to our simulation results,the proposed algorithm produces near-optimal solutions in terms of the auctioneer's revenue. moreover,reasonable computation time is another advantage of applying the proposed algorithm to the real-world services. © 2014 chen-kun tsung et al.