Randomization as a strategy for sellers during price discrimination, and impact on bidders' privacy

A previous paper demonstrates that if a seller always uses auction bids to later price discriminate against losing bidders, his revenue decreases dramatically. In this paper, we examine whether the seller obtains an advantage if he randomizes his strategy -- that is, if he does not use privacy-infringing information all the time, but only with probability ?;. Using both Bayesian techniques and genetic algorithm experiments, we determine optimal strategies for bidders and sellers in a two stage game: Stage I is a first price auction used to elicit information on a bidder's valuation; Stage II is, with probability ?;, a price discrimination offer, and, a fixed price offer P; else. Our results show that the seller does not benefit from randomized price discrimination. Further, low valuation bidders benefit more from the seller's use of privacy-infringing information than do the high valuation ones, as they may wish to signal that they cannot afford a high second-stage offer. To our knowledge, our use of genetic algorithm simulations is unique in the privacy literature.