© 1985

Optimal Decisions Under Uncertainty

Methods, Models, and Management


Part of the Universitext book series (UTX)

Table of contents

  1. Front Matter
    Pages i-x
  2. Jati K. Sengupta
    Pages 1-16
  3. Jati K. Sengupta
    Pages 169-205
  4. Jati K. Sengupta
    Pages 206-274
  5. Jati K. Sengupta
    Pages 275-283
  6. Back Matter
    Pages 284-286

About this book


Understanding the stochastic enviornment is as much important to the manager as to the economist. From production and marketing to financial management, a manager has to assess various costs imposed by uncertainty. The economist analyzes the role of incomplete and too often imperfect information structures on the optimal decisions made by a firm. The need for understanding the role of uncertainty in quantitative decision models, both in economics and management science provide the basic motivation of this monograph. The stochastic environment is analyzed here in terms of the following specific models of optimization: linear and quadratic models, linear programming, control theory and dynamic programming. Uncertainty is introduced here through the para­ meters, the constraints, and the objective function and its impact evaluated. Specifically recent developments in applied research are emphasized, so that they can help the decision-maker arrive at a solution which has some desirable charac­ teristics like robustness, stability and cautiousness. Mathematical treatment is kept at a fairly elementary level and applied as­ pects are emphasized much more than theory. Moreover, an attempt is made to in­ corporate the economic theory of uncertainty into the stochastic theory of opera­ tions research. Methods of optimal decision rules illustrated he re are applicable in three broad areas: (a) applied economic models in resource allocation and economic planning, (b) operations research models involving portfolio analysis and stochastic linear programming and (c) systems science models in stochastic control and adaptive behavior.


Decisions Uncertainty linear optimization operations research optimization scheduling

Authors and affiliations

  1. 1.Department of EconomicsUniversity of CaliforniaSanta BarbaraUSA

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