An iterative multi-attribute auction mechanism based on co-evolutionary computation

Aimed at the problem that preference elicitation is time-consuming and costly in multi-attribute auctions, a novel iterative multi-attribute auction mechanism for reverse auction settings with one buyer and many sellers is proposed. The auctions support incremental preference elicitation and revelation for the buyer and the sellers. Co-evolutionary computation method is incorporated into the mechanism to support economic learning and strategies for the sellers. The strategy provided by it is in ex-post Nash equilibrium for sellers, assumed that the buyer takes a truthful strategy. Experimental results show that the co-evolutionary computation based iterative multi-attribute auction is a practical and nearly efficient mechanism.