Price and Niche Wars in a Free-Market Economy of Software Agents

Abstract

One scenario of the future of computation populates the Internet with vast numbers of software agents providing, trading, and using a rich variety of information goods and services in an open, free-market economy. An essential task in such an economy is the retailing or brokering of information: gathering it from the right producers and distributing it to the right consumers. This article investigates one crucial aspect of brokers' dynamical behavior, their price-setting mechanisms, in the context of a simple information-filtering economy. We consider only the simplest cases in which a broker sets its price and product parameters based solely on the system's current state, without explicit prediction of the future. Analytical and numerical results show that the system's dynamical behavior in such "myopic" cases is generally an unending cycle of disastrous competitive "wars" in price/product space. These in turn are directly attributable to the existence of multiple peaks in the brokers' profitability landscapes, a feature whose generality is likely to extend far beyond our model.

Dynamic pricing by software agents

“…In our model of an information ®ltering economy [12,18,19], consumers experience a processing cost P C for each article that they receive, and pay an additional fee P b when they decide to consume an article oered by a broker b. Consumers hold a relevant article to be worth V, and an irrelevant one to be worth nothing.…”

“…A more practical variant of the myoptimal strategy [19] replaces the exhaustive search with a limited search in which a ®xed number of hypothetical price/product values are considered, and the one yielding the highest expected pro®t is selected. Candidate price/product values are generated in two ways, neither of which uses information about any of the consumers or other brokers: either by incremental changes to current parameter values or (less frequently) by choosing values completely at random.…”

“…Eventually, the period of calm and prosperity will be disturbed when a broker in a less pro®table niche discovers that it can improve its pro®ts (temporarily) by abandoning its niche and undercutting another broker. After a tumultuous round of competition, the brokers again settle into a niche-monopoly, and peace and prosperity reign again for a while [19].…”

Theory of Collective Intelligence

“…Such computational economies can be used to investigate real economies and biological systems 32,36,37,149]. They can also be used to design distributed computational systems.…”

Software agents and the route to the information economy

“…In these models, buyers are assisted by automated purchasing algorithms that continually monitor prices and product attributes and purchase an item from the vendor that best satisfies the individual buyer's utility function. Several distinct variants of this model have been explored: a market for a simple commodity (in which some buyers perform only a limited search among the set of sellers) (14,15); a vertically differentiated market (in which the good may be offered at different levels of quality by different sellers) (16); and information filtering (17,18) and information bundling models (19) (in which the seller sets both the price and the parameters that determine the product's configuration).…”