A Bayesian Multilevel Model for Time Series Applied to Learning in Experimental Auctions

Abstract: Establishing what variables affect learning rates in experimental auctions can be valuable in determining how competitive bidders in auctions learn. This study aims to be a foray into this field. The differences, both absolute and actual, between participant bids and optimal bids are evaluated in terms of the effects from a variety of variables such as age, sex, etc. An optimal bid in the context of an auction is the best bid a participant can place to win the auction without paying more than the value of the item, thus maximizing their revenues. This study focuses on how two opponent types, humans and computers, affect the rate at which participants learn to optimize their winnings. A Bayesian multilevel model for time series is used to model the learning rate of actual bids from participants in an experimental auction study. The variables examined at the first level were auction type, signal, round, interaction effects between auction type and signal and interaction effects between auction type and round. At a 90% credibility interval, the true value of the mean for the intercept and all slopes falls within an interval that also includes 0. Therefore, none of the variables are deemed to be likely to influence the model. The variables on the second level were age, IQ, sex and answers from a short quiz about how participants felt when the y won or lost auctions. The posterior distributions of the second level variables also found to be unlikely to influence the model at a 90% credibility interval. This study shows that more research is required to be able to determine what variables affect the learning rate in competitive bidding auction studies