Shopbots and Pricebots

  • Amy R. Greenwald
  • Jeffrey O. Kephart
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1788)


Shopbots are software agents that automatically gather and collate information from multiple on-line vendors about the price and quality 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 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.


Nash Equilibrium Equilibrium Price Price Strategy Repeated Game Strategy Nash Equilibrium 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Amy R. Greenwald
    • 1
  • Jeffrey O. Kephart
    • 1
  1. 1.IBM Institute for Advanced CommerceIBM Thomas J. Watson Research CenterYorktown HeightsUSA

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