Constructing Speculative Demand Functions in Equilibrium Markets

In computational markets utilizing algorithms that establish a general equilibrium, competitive behavior is usually assumed: each agent makes its demand (supply) decisions so as to maximize its utility (profit) assuming that it has no impact on market prices. However, there is a potential gain from strategic behavior via speculating about others because an agent does affect the market prices, which affect the supply/demand decisions of others, which again affect the market prices that the agent faces. Determining the optimal strategy when the speculator has perfect knowledge about the other agents is a well known problem which has been studied in oligopoly theory in economics. We describe the computation of such a strategy, and focus on an issue that has received little attention in economics, but which is of fundamental importance in computational markets: the revelation of demand strategies that drive the market to the desired equilibrium. The more realistic setting where the speculator has imperfect information about the other agents is more delicate. We demonstrate how speculation under biased beliefs about the other agents can result in considerable losses if traditional oligopoly strategies for perfect information are used. Furthermore, we show how the optimal demand is computed from probability distributions on the other agents’ supply/demand functions. We also theoretically show when an optimal revealed demand function can be constructed independently of the probability distributions. Some pragmatics of choosing a demand function in the case of imperfect information (particularly useful for construction of computational agents for equilibrium markets) are given, and we show—with some empirical support—that it can be relatively easy to construct demand functions that results in a gain from speculation, even when estimation errors are large. Finally, game theoretic issues related to multiple agents counterspeculating simultaneously are discussed. *. This material is based upon work supported by the National Science Foundation under CAREER Award IRI-9703122, Grant IRI-9610122, Grant IIS-9800994, NUTEK’s Promodis program, and EnerSearch’s owners: ABB, ECN, Iberdrola, IBM, PreussenElektra, and Sydkraft. A short early version containing some of the results of this article appeared at the International Joint Conference on Artificial Intelligence (Sandholm & Ygge, 1997).