Sensitivity analysis in decision circuits

Debarun Bhattacharjya, Ross D. Shachter
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:34-42, 2008.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-bhattacharjya08a, title = {Sensitivity analysis in decision circuits}, author = {Bhattacharjya, Debarun and Shachter, Ross D.}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {34--42}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/bhattacharjya08a/bhattacharjya08a.pdf}, url = {https://proceedings.mlr.press/r6/bhattacharjya08a.html}, abstract = {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.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T Sensitivity analysis in decision circuits %A Debarun Bhattacharjya %A Ross D. Shachter %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-bhattacharjya08a %I PMLR %P 34--42 %U https://proceedings.mlr.press/r6/bhattacharjya08a.html %V R6 %X 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. %Z Reissued by PMLR on 09 October 2024.
APA
Bhattacharjya, D. & Shachter, R.D.. (2008). Sensitivity analysis in decision circuits. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:34-42 Available from https://proceedings.mlr.press/r6/bhattacharjya08a.html. Reissued by PMLR on 09 October 2024.

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