Internet auctions with artificial adaptive agents: A study on market design

We develop a model of internet auctions with the aim of understanding how rules for ending such auctions (a “hard”- or “soft”-close) affect bidding behavior. We model bidding strategies using finite automata and report results from simulations involving populations of artificial bidders who update their strategies using a genetic algorithm. Our model is shown to deliver late or early bidding behavior, depending on whether the auction has a hard- or soft-close rule in accordance with the empirical evidence. We report on other interesting properties of our model and offer some conclusions from a market design point of view.