Shopbots and Pricebots

Amy R. Greenwald and Jeffrey O. Kephart


Shopbots are software agents that automatically generate queries to multiple on-line vendors in order to gather information about price and quality of goods and services. Rapidly increasing in number and sophistication, shopbots are helping more and more buyers to minimize expenditure and maximize satisfaction. In response to this trend, it is anticipated that sellers will come to rely on pricebots, automated agents that employ price-setting algorithms in an attempt to maximize profits. In this paper, a simple economic model is proposed and analyzed, which is intended to characterize some of the likely impacts of a proliferation of shopbots and pricebots.

In addition to describing theoretical investigations, this paper also aims toward a practical understanding of the tradeoffs between profitability and computational and informational complexity of pricebot algorithms. A comparative study of a series of price-setting strategies is presented, including: game-theoretic (GT), myoptimal (MY), derivative following (DF), and no regret learning (NR). The dynamic behavior that arises among collections of pricebots and shopbot-assisted buyers is simulated, and it is found that game-theoretic equilibria can dynamically arise in our model of shopbots and pricebots.