Reasoning about the value of decision-model refinement: methods and application

Authors:
Kim Lent Poh;Eric J. Horvitz
Affiliations:
Department of Industrial & Systems Engineering, National University of Singapore and Laboratory for Intelligent Systems, Department of Engineering-Economic Systems, Stanford University, CA;Palo Alto Laboratory, Rockwell International Science Center, Palo Alto, CA
Venue:
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Year:
1993

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Abstract

We investigate the value of extending the completeness of a decision model along different dimensions of refinement. Specifically, we analyze the expected value of quantitative, conceptual, and structural refinement of decision models. We illustrate the key dimensions of refinement with examples. The analyses of value of model refinement can be used to focus the attention of an analyst or an automated reasoning system on extensions of a decision model associated with the greatest expected value.