Detecting Behavioral Biases in Mixed Human-Proxy Online Auction Markets

Abstract

Currently many auction websites directly or indirectly provide support for the use of automated proxies or agents. Buyers can use proxies to monitor auctions and bid at the appropriate time and with the appropriate bid price, sellers can use proxies to set prices or negotiate deals. Proxy complexity varies, however most proxies first require some input on the part of the human trader and then perform the trading task autonomously. This paper proposes and tests a theoretical model of human behavior that can be used to detect behavioral biases in electronic market environments populated by humans and software agents. The paper also quantifies the effect of these biases on individual and business profits.