An interactive organizational choice processing system to support decision making by using a prescriptive garbage can model.

Abstract

This thesis discusses and implements an interactive decision support system using a Prescriptive Garbage Can Model. The fundamental presumtion is that if the choice-outcome relationships in an organization can be observed and evaluated, it is possible to extract predictiveness from uncertain streams, and allow the organization to shift to a less random strategy. Solving organizational problems consists of selecting those choices that lead the organization in a direction towards the ideal state. Thus, it is convenient to model the organizational state transitions as a Markovian process with stationary properties. The purpose of a Prescriptive Garbage Can Model is to advise the participants of the choices available in a current situation, and to present choice policies leading the highest potential benefits. Also a method of interfacing the current system with an expert system for intelligent decision making is examined.