Shopbot Economics

Jeffrey O. Kephart and Amy R. Greenwald


Shopbots are Internet agents that automatically search for information that pertains to the price and quality of goods and services. As the prevalence of shopbots in electronic commerce increases (i.e., as more buyers avail themselves of shopbots), the resultant reduction in economic friction due to decreased search costs could dramatically alter market behavior. This paper explores the potential impact of shopbots on market dynamics, by proposing, analyzing, and simulating a model of shopbot economics, which incorporates software agent representations of buyers and sellers. Although our model is similar in spirit to some that are studied by economists interested the phenomenon of price dispersion, the underlying assumptions and methodology of our approach are different, since ultimately our goal is to design economic software agents, rather than explain human economic behavior. In particular, we examine agent economies in which (i) search costs are nonlinear, (ii) some portion of the buyer population makes no use of search mechanisms, and (iii) shopbots are economically-motivated, strategically pricing their information services so as to maximize their own profits. Under these conditions, we find that markets can exhibit a variety of hitherto unobserved dynamical behaviors, including complex limit cycles and the co-existence of multiple buyer search strategies. We also demonstrate how a shopbot that charges buyers for price information can manipulate markets to its own advantage, sometimes inadvertently benefiting buyers and sellers.