Value of Evidence on Influence Diagrams

TL;DR

This paper develops methods for evidence propagation and quantifies the value of evidence in influence diagrams, enhancing decision analysis by enabling computation of outcome sensitivity, perfect information, and control value.

Contribution

It introduces evidence propagation operations and the concept of value of evidence for influence diagrams, facilitating decision analysis and inference in expert systems.

Findings

Evidence propagation operations enable efficient inference in influence diagrams.

The value of evidence can be computed directly for decision sensitivity analysis.

Implementation considerations include computational efficiency for practical applications.

Abstract

In this paper, we introduce evidence propagation operations on influence diagrams and a concept of value of evidence, which measures the value of experimentation. Evidence propagation operations are critical for the computation of the value of evidence, general update and inference operations in normative expert systems which are based on the influence diagram (generalized Bayesian network) paradigm. The value of evidence allows us to compute directly an outcome sensitivity, a value of perfect information and a value of control which are used in decision analysis (the science of decision making under uncertainty). More specifically, the outcome sensitivity is the maximum difference among the values of evidence, the value of perfect information is the expected value of the values of evidence, and the value of control is the optimal value of the values of evidence. We also discuss an implementation and a relative computational efficiency issues related to the value of evidence and the value of perfect information.