Shopbot Economics
Jeffrey O. Kephart and Amy R. Greenwald
Abstract
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.