Shopbots, programs that automatically search the Internet for advertised goods or services on behalf of consumers, herald a future in which autonomous agents become an increasingly essential component of nearly every facet of electronic commerce [1, 4, 10, 11, 19]. In response to a consumer's expressed interest in a specified good or service, a typical shopbot can query several dozen web sites, and then collate and sort the available information for the user -- all within seconds. For example, www.shopper.com claims to compare 1,000,000 prices on 100,000 computer-oriented products! In addition, www.acses.com compares the prices of books offered for sale on-line, while www.jango.com and webmarket.junglee.com offer everything from apparel to gourmet groceries. Despite some important limitations [13], shopbots can outperform and out-inform even the most patient, determined consumers, for whom it would take hours to obtain far less coverage of available goods and services.
Shopbots deliver on one of the great promises of electronic commerce and the Internet: a radical reduction in the cost of obtaining and distributing information. It is generally recognized that freer flow of information will profoundly affect market efficiency, as economic friction will be reduced significantly [6, 12, 13]. Transportation costs, menu costs -- the costs to firms of evaluating, updating, and advertising prices -- and shopping costs -- the costs to consumers of seeking out optimal price and quality -- will all decrease, as a consequence of the digital nature of information as well as the presence of autonomous agents that find, process, collate, and disseminate that information at little cost.
In today's electronic marketplace, however, shopbots are a conundrum. On one hand, they are clearly a useful weapon for consumers -- armed with up-to-date information, consumers can demand that firms behave more competitively. Many of us would be happy to purchase our goods from the lowest-priced, highest-quality dealer, if only the cost and/or the effort of obtaining complete and accurate information were not so monumental. Some vendors have responded to this threat by deliberately blocking agents from their sites; other vendors welcome shopbots as a means of attracting consumers who otherwise might not have known about them, or might not have thought to purchase from them [13]. Some of the vendors in this latter class even sponsor shopbots, by paying for the opportunity for their products to be listed on shopping sites such as www.shopper.com.
As the XML standardization effort gains momentum [17],
one of the major barriers preventing the mass-production of shopbots
-- the headache associated with parsing the idiosyncracies of each
individual vendor's .html files -- is likely to be
overcome. 
The outcome of standardization may well be a greater proliferation of
shopbots emerging as representatives of all forms of goods and
services bought and sold on-line. What are the implications of the
widespread use of shopbots? What impact might shopbots have upon
markets? How might shopbots evolve?
DeLong and Froomkin [13] qualitatively investigate the ongoing emergence of shopbots; in particular, they note that short of violating anti-trust laws, firms will be hard pressed to prevent their competitors from sponsoring shopbots, in which case those who do not do so will experience decreased sales. In this paper, we utilize quantitative techniques to address the aforementioned questions; we propose, analyze, and simulate a simple economic model which captures the present role of shopbots as agents of economic change, particularly with regard to consumer preferences. Looking ahead several years into the future, we project that shopbots will evolve into economic entities in their own right, interacting with billions of other economically-motivated software agents. Accordingly, we study adaptive price-setting algorithms that profit-maximizing agents designed by firms might utilize in the face of a growing community of shopbots, in a full-fledged agent-based information economy.
This paper is organized as follows. The following section presents the model which is studied throughout. Section 3 analyzes the model in detail, ultimately deriving the unique Nash equilibrium. Section 4 describes various adaptive price-setting algorithms and the results of their simulation under the prescribed model. An underlying theme throughout is whether or not adaptive learning yields the computed game-theoretic solution. A discussion of the possible evolution of shopbots follows in Section 5. Finally, our concluding remarks, including ideas for future research, appear in Section 6.