Bidding against the machine: preference elicitation in the presence of bots

Abstract

This study examines whether the presence of automated bidders (bots) affects behavior in second-price auctions and the resulting willingness-to-pay estimates. Using an online experiment with 1,101 U.S.-based participants, we vary the number of bots in auction groups and test outcomes in both induced-value and homegrown valuation environments. Theory predicts that in second-price auctions truthful bidding is a weakly dominant strategy, so bot presence should not alter equilibrium behavior. Preliminary results show that although participants deviate systematically from truthful bidding, these deviations do not differ across bot treatments, and willingness-to-pay estimates remain stable. The findings suggest that bots can be used to facilitate online auction implementation and group formation without compromising the behavioral validity of preference elicitation.

Andreas C. Drichoutis

Professor of Consumer Behavior

His research interests are focused on decision making across a broad spectrum of topics pertinent to agricultural/food economics. His contributions are notably diversified spanning the area of contingent valuation and experimental auctions methods, choice under risk, inter-temporal decision making and applied demand analysis. Most of his research applies experimental economics methods to answer questions relevant to agricultural economists and decision scientists.