On using discrete random models within decision support systems

Abstract

In this paper we review how models for discrete random systems may be used to support practical decision making. It will be demonstrated how organizational requirements determine to a large extent the type of model to be applied as well as the way in which the model should be applied. This demonstration is given via several practical examples of markov chain models, cohort models, and Markov decision models. The examples are drawn from various areas ranging from the purely technical to social applications. It is demonstrated that the models that are needed for supporting the decision making process may vary from purely descriptive models to optimization models. Similarly, the obvious way of application of a model vary from straightforward numerical analysis to interactive modelling procedures based upon managerial evaluation. It will also be demonstrated how the numerical methods to be used depend on the structure of the model as well as on applicational aspects. The numerical aspects is strongly related to the aforementioned aspects. Since the model choice heavily determines the computational possibilities.