Efficient Value of Information Computation
Ross D. Shachter
TL;DR
This paper introduces efficient algorithms for computing the value of information in decision analysis, leveraging extensions to existing methods to improve calculation speed within the rooted cluster tree framework.
Contribution
It presents novel extensions to previous algorithms that enable faster value of information computation in decision analysis models.
Findings
Enhanced algorithms reduce computational complexity.
Applicable to decision problems modeled with rooted cluster trees.
Improves efficiency of sensitivity analysis in decision analysis.
Abstract
One of the most useful sensitivity analysis techniques of decision analysis is the computation of value of information (or clairvoyance), the difference in value obtained by changing the decisions by which some of the uncertainties are observed. In this paper, some simple but powerful extensions to previous algorithms are introduced which allow an efficient value of information calculation on the rooted cluster tree (or strong junction tree) used to solve the original decision problem.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Data Management and Algorithms · AI-based Problem Solving and Planning