Stochastic Learning of the Optimum Bid in Auction Markets

Evolutionary algorithms based on stochastic programming are proposed for learning of the optimum bid in auction markets. Sellers and buyers are attempting to learn their optimum bids that maximize their individual utility functions in the next round of the game. Examples of second-price sealed-bid auction and double auctionmarkets are considered and good performance of this type of algorithm is confirmed by extensive simulations. The proposed algorithms need no assumptions about the stationary behavior of players, contrary to the needs of competitive algorithms in the class of fictitious play.