Sensitivity analysis in decision circuits
This work provides a method for sensitivity analysis in decision circuits, which is incremental as it builds on existing decision circuit frameworks for model analysis.
The paper tackles the problem of performing sensitivity analysis in sequential decision problems under uncertainty by leveraging decision circuits, which are built on arithmetic circuits for belief network inference, to compute the value of information and assess the effects of parameter changes on optimal strategies and values.
Decision circuits have been developed to perform efficient evaluation of influence diagrams [Bhattacharjya and Shachter, 2007], building on the advances in arithmetic circuits for belief network inference [Darwiche,2003]. In the process of model building and analysis, we perform sensitivity analysis to understand how the optimal solution changes in response to changes in the model. When sequential decision problems under uncertainty are represented as decision circuits, we can exploit the efficient solution process embodied in the decision circuit and the wealth of derivative information available to compute the value of information for the uncertainties in the problem and the effects of changes to model parameters on the value and the optimal strategy.