Abstract

Shopbots are software agents that automatically query multiple sellers on the Internet to gather information about prices and other attributes of consumer goods and services. Rapidly increasing in number and sophistication, shopbots are helping more and more buyers minimize expenditure and maximize satisfaction. In response at least partly 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. This paper reaches toward an understanding of strategic pricebot dynamics. More specifically, this paper is a comparative study of four candidate price-setting strategies that differ in informational and computational requirements: gametheoretic pricing (GT), myoptimal pricing (MY), derivative following (DF), and Q-learning (Q). In an effort to gain insights into the tradeoffs between practicality and pro tability of pricebot algorithms, the dynamic behavior that arises among homogeneous and heterogeneous collections of pricebots and shopbot-assisted buyers is analyzed and simulated. In homogeneous settings -- when all pricebots use the same pricing algorithm -- DFs outperform MYs and GTs. Investigation of heterogeneous collections of pricebots, however, reveals an incentive for individual DFs to deviate to MY or GT. The Q strategy exhibits superior performance to all the others since it learns to predict and account for the long-term consequences of its actions. Although the current implementation of Q is impractically expensive, techniques for achieving similar performance at greatly reduced computational cost are under investigation.

Citations