  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.
