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.