A theory based upon random search within a fixed population of technological possibilities is used to explain the manufacturing progress function. The theory is consistent with the power function relation between unit costs and cumulative output that has frequently been observed. It is also consistent with initial rates of improvement smaller than those predicted later by the power function relation, the eventual cessation of cost reduction, and an irregularity of improvements. Existing theories in the literature either fail to agree with the main empirical phenomena or else assume precisely what they attempt to explain.

In order to explain fairly simply how expectations are formed, we advance the hypothesis that they are essentially the same as the predictions of the relevant economic theory. In particular, the hypothesis asserts that the economy generally does not waste information, and that expectations depend specifically on the structure of the entire system. Methods of analysis, which are appropriate under special conditions, are described in the context of an isolated market with a fixed production lag. The interpretative value of the hypothesis is illustrated by introducing commodity speculation into the system. 1. INTRODUCTION THAT EXPECTATIONS of economic variables may be subject to error has, for some time, been recognized as an important part of most explanations of changes in the level of business activity. The "ex ante" analysis of the Stockholm School-although it has created its fair share of confusion-is a highly suggestive approach to short-run problems. It has undoubtedly been a

An application of linear decision rules to production and employment scheduling was described in the last issue of this journal [Holt, C. C., F. Modigliani, H. A. Simon. 1955. A linear decision rule for production and employment scheduling. Management Sci. (October).]. The hypothetical performance of these rules represented a significant improvement over the actual company performance as measured by independent cost estimates and other managerial measures of efficiency. The quadratic cost function which was used should be applicable to production and employment scheduling decisions in many other situations. Also the general approach of approximating decision criteria with quadratic functions and obtaining linear decision rules can usefully be extended to many decision problems. In the present paper we will demonstrate (a) how optimal (i.e., minimum expected cost) decision rules may be derived for a quadratic cost function involving inventory, overtime, and employment costs, and (b) how the numerical coefficients of the rules may be computed for any set of cost parameters.