Shopbots and Pricebots
Amy R. Greenwald and Jeffrey O. Kephart
Abstract
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.