Reasoning about the Value of Decision-Model Refinement: Methods and Application
This work addresses the problem of optimizing decision-model refinement for analysts or automated reasoning systems, but it appears incremental as it builds on existing refinement concepts without introducing a new paradigm.
The paper investigates the value of refining decision models along quantitative, conceptual, and structural dimensions to determine which extensions yield the greatest expected value, aiming to guide analysts or automated systems in focusing their efforts effectively.
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.