Strategic Pricebot Dynamics
Amy R. Greenwald, Jeffrey O. Kephart, and Gerald J. Tesauro
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: game-theoretic pricing (GT), myoptimal
pricing (MY), derivative following (DF), and Q-learning (Q). In an
effort to gain insights into the tradeoffs between practicality and
profitability 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.