A Game Theoretical Approach for Solving Winner Determination Problems

Abstract EN

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.