Abstract

Abundant evidence exists that expected utility theory does not adequately describe decision making under risk. Although prospect theory is a popular alternative, it is rarely applied in strategic situations in which risk
arises through individual interactions. This study fills this research gap by incorporating prospect theory preferences into a dynamic game theoretic model. Using a large field data set from multiple online pay-per-bid auction
sites, the authors empirically show that their proposed model with prospect theory preferences makes a better out-of-sample prediction than a corresponding expected utility model. Prospect theory also provides a unified
explanation for two behavioral anomalies: average auctioneer revenues above current retail prices and the sunk cost fallacy. The empirical results indicate that bidders are loss averse and overweight small probabilities, such
that the expected revenue of the auction exceeds the current retail price by 25.46%. The authors illustrate and empirically confirm a managerial implication for how an auctioneer can increase revenue by changing the details
of the auction design.