Process of decision making under uncertainty with an application to the theory of expert systems
The broad objective of this dissertation is to examine the process of decision making under uncertainty. First, within the framework of the economics of information, a theoretical model is developed that describes rational choice as a process of gathering information in a sequence of r > 1 actions, determining an appropriate stopping time for the information collection, and then making a final decision. The formulation of an a priori strategy is described where each information-gathering action, as well as the final decision, is contingent upon information signals received from previous actions. Second, the possible use of this, or some other decision-theoretical model, as a framework for computer-based decision-making system is discussed. Since very little work has been done on the decision-theoretic basis of expert systems as a subject of artificial intelligence, this approach is first compared to the approach taken in most current expert systems. The major advantage is seen to be the focus on the efficiency of the decision process and therefore necessarily on the trade-off between the correctness and the cost of making a decision. Then, some of the computational aspects of using the theoretical model as a basis for an expert system are discussed.
- Research Organization:
- Purdue Univ., Lafayette, IN (USA)
- OSTI ID:
- 5507042
- Resource Relation:
- Other Information: Thesis (Ph. D.)
- Country of Publication:
- United States
- Language:
- English
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