Application of the Iterative Policy Model for the Solution of Serial Decision Processes of Markovian Nature to Decision Making in National Systems: Introduction

Abstract

The purpose of this paper is to investigate decision processes that have been developed in other fields -especially Operations Research- and apply them to national systems decision making in order to stimulate the study of new approaches to the solution of old problems. Specifically, we deal here with processes of a Markovian nature that require ''sequential'' solutions, i.e., a series of decisions, each one following a previous one. In general terms, what we are trying to obtain is a series of decisions to be made under different circumstances or ''states'', which would improve the probabilities of the system in question to achieve its objectives. First, fundamental concepts such as the Markov process and sequential decisions will be reviewed, and then the ''iterative policy'' method will be explained for the cases of one and several ''recurrent chains''. Examples in the context of national systems will also be discussed.