The Use of Artificial Neural Networks for Estimation of Decision Surfaces in First Price Sealed Bid Auctions

Abstract

Artificial neural networks, optimized using genetic algorithms, are used to estimate bid functions for first price sealed bid auctions. Data generated in experimental markets is used for two means of estimating the bid function. First, the neural network provides a best fit to the data, thus estimating the bid function that subjects were using. Alternative objective functions are used for the neural network to demonstrate the effect on the resultant bid function. Second, the neural network is optimized using profit maximization as the objective function to identify the optimal bid function given the bids of the experimental subjects.