Reasoning about the Value of Decision-Model Refinement: Methods and Application

TL;DR

This paper explores how extending decision models through various refinements can improve decision-making, providing methods to identify the most valuable model extensions for analysts and automated systems.

Contribution

It introduces a framework for evaluating the expected value of different types of decision-model refinements, guiding focus on the most beneficial extensions.

Findings

Quantitative, conceptual, and structural refinements can significantly impact decision quality.

Analysts can prioritize model extensions based on expected value analysis.

The approach aids automated reasoning systems in focusing on high-value model improvements.

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.