Efficient Value of Information Computation
This work provides incremental improvements for researchers and practitioners in decision analysis and sensitivity analysis.
The paper tackles the problem of efficiently computing the value of information in decision analysis by introducing extensions to previous algorithms, enabling faster calculations on rooted cluster trees used in solving decision problems.
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.