Regret, Learning and Risk Aversion in First Price Sealed Bid Auctions

Understanding bidding behavior in Sealed Bid First Price (SBFP) auctions for a single unit in the independent private value setting is important because it can provide relevant insights into what to expect in complex real world settings. Laboratory experiments are used to test existing theory, and provide insights for developing new theory. One fundamental puzzle about SBFP auctions that received a great deal of attention in the experimental economics literature is why do participants generally bid above the Risk Neutral Nash Equilibrium (RNNE). This result has been replicated numerous times by different researchers at different laboratories and under a variety of environments, but the reason for overbidding has never been fully understood. According to Kagel (1995), ". . . risk aversion is one element, but far from the only element, generating bidding above the RNNE." (p. 525). Engelbrecht-Wiggans (1989) suggested that the desire to avoid regret may be motivating bidders in SBFP auctions. Although the theory of regret has been used to explain some well-known anomalies in expected utility theory, this notion has never been applied to auctions prior to Engelbrcht-Wiggans (1989), and has never been tested in the auction context. The aim of our proposal is to systematically test the Engelbrecht-Wiggans (1989) theory of regret in auctions, with the specific view of separating the role of regret from that of risk aversion. As we gain a better understanding of behavior in SBFP auctions, we will also extend and refine the theory to incorporate the new insights and to explain some of the existing puzzles. In the sequence of experiments we propose, human bidders will compete against computerized bidders programmed to play risk-neutral Nash equilibrium (RNNE). Human bidders will have the same values for a number of rounds, thus allowing them to learn, and bidding strategies will be restricted in a variety of ways. It is our conjecture that this setting will be more conducive to learning than the traditional setting, and therefore, if overbidding relative the RNNE is due to errors, we should see a substantial trend towards lower bids. If we find that overbidding persists, we will conduct the second study, aimed at separating the regret and the risk-aversion explanations by having bidders bid in a number of auctions simultaneously while restricting their actual bids to be the same for all auctions. Risk averse bidders should overbid substantially less when they are paid based on the outcome of several auctions than when they are paid based on the outcome of a single auction. But bidders who are motivated by the desire to avoid regret should continue to overbid relative to RNNE because regret is cumulative and does not cancel out. Implications of our work go beyond the laboratory because over the past two decades auction theory has been used extensively to help design real world auctions and exchanges. Examples of these new auctions include the FCC auctions for spectrum licenses, electric power auctions, auctions for transportation services, and different types of procurement auctions. These mechanisms are complex, and designers must make decisions about many aspects of auction rules that are likely to have a profound effect on the auction's efficiency,the sellers' revenue and the buyers' profits. To make good design decisions in the real world we need to further our knowledge about the basic forces behind bidding behavior, and understanding why people bid above RNNE in SBFP auctions is an important step towards bridging the gap between auction theory and the practice or market design.