Three new sensitivity analysis methods for influence diagrams
This work provides incremental improvements for researchers and practitioners in decision analysis, focusing on specific applications like strategy comparison and risk aversion sensitivity.
The paper tackles the problem of sensitivity analysis in influence diagrams by introducing three non-linear methods that leverage existing partial derivative information, eliminating the need for multiple re-evaluations of decision situations.
Performing sensitivity analysis for influence diagrams using the decision circuit framework is particularly convenient, since the partial derivatives with respect to every parameter are readily available [Bhattacharjya and Shachter, 2007; 2008]. In this paper we present three non-linear sensitivity analysis methods that utilize this partial derivative information and therefore do not require re-evaluating the decision situation multiple times. Specifically, we show how to efficiently compare strategies in decision situations, perform sensitivity to risk aversion and compute the value of perfect hedging [Seyller, 2008].