The Interaction of Uncertainty and Information Lags in the Cournot Oligopoly Model

Abstract

The Cournot oligopoly model is one of the classical models of the economic dynamics literature. The pioneering work of Cournot (1838) initiated a large sequence of studies on static and dynamic models. Okuguchi (1976) discusses single-product models and also gives a comprehensive literature review, particularly of stability conditions. Okuguchi and Szidarovszky (1999) extend the analysis to allow for oligopolies consisting of multi-product firms. Many existing oligopoly models assume perfect knowledge by firms of the market demand functions as well as of the firm’s own cost function. While perfect knowledge of the cost functions seems to be a realistic assumption, that of the demand functions may be less so. There have been only some limited attempts at modelling inaccurate knowledge of the demand function in oligopolies. Cyert and DeGroot (1971, 1973) have examined duopoly models. Kirman (1975) considered differentiated products and linear demand functions which firms misspecify and attempt to estimate. The resulting process may converge to the full information equilibrium or to some other equilibria. He also analysed how the resulting equilibrium values are affected (Kirman, 1983). Gates et al. (1982) have considered linear demand functions and differentiated products and assumed some economically guided learning process. Shapiro (1989) has discussed the use of trigger price strategies. Szidarovszky and Okuguchi (1990) have analysed the asympotic stability of oligopolies with perceived marginal costs. Léonard and Nishimura (1999) considered a discrete dynamic duopoly with linear cost functions and illustrated how the fundamental dynamic properties of the model are drastically altered as a result of the lack of full information on the demand functions.KeywordsBifurcation DiagramDemand FunctionInverse Demand FunctionFirm CaseLinear Cost FunctionThese 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.