Titlebook: Bayesian Analysis and Uncertainty in Economic Theory; Richard M. Cyert,Morris H. DeGroot Book 1987 Richard M. Cyert and Morris H. DeGroot

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Introduction,. Most previous attempts to deal with uncertainty have been designed to finesse the problem. Uncertainty is put into the problem, and an assumption is then made that allows the problem to be turned into one of certainty. Economists in particular have shown a talent for bringing every problem back to

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Bayesian Decision Theory,ory deals with the development of methods and techniques that are appropriate for making decisions in an optimal fashion. In fact, statistics itself is sometimes described as the science of decision making under uncertainty. Although decision theory may not encompass the entire field of statistics,

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Bayesian Analysis and Duopoly Theory,1952). Nevertheless, the problem has proved to be a frustrating one for economists. The obvious reason for the difficulty is the uncertainty that characterizes the problem. The specific uncertainty revolves around the interrelationship of the two firms and the fact that the decisions of one of them

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Multiperiod Decision Models with Alternating Choice as a Solution to the Duopoly Problem,rized by the fact that an infinite number of models can be generated by assuming different values for the conjectural variations term (Cohen and Cyert 1975; Kamien and Schwartz 1983). No general solution exists and there is no basis, either empirical or theoretical, for preferring one of the models

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Cooperation and Learning in a Duopoly Context,ies have been developed for the firms. In the model discussed in chapter 5, an equilibrium position for the two firms was ultimately established. The particular equilibrium that will be attained can be deduced in advance from the fixed behavioral assumptions that were built into the model. A model i

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Sequential Strategies in Dual Control Problems,cular, we will apply our methodology to some problems in control theory. As indicated in chapter 3, we believe that control theory is appropriate for building mathematical models of the firm. We will develop an extension of the usual linear-quadratic, stochastic control problems in discrete time (Si