TY - JOUR
T1 - Belief-Informed Robust Decision Making (BIRDM)
T2 - Assessing changes in decision robustness due to changing distributions of deep uncertainties
AU - Ciullo, A.
AU - Domeneghetti, A.
AU - Kwakkel, J. H.
AU - De Bruijn, K. M.
AU - Klijn, F.
AU - Castellarin, A.
PY - 2022
Y1 - 2022
N2 - Robust Decision Making (RDM) is an established framework for decision making under deep uncertainty. RDM relies on the idea of scenario neutrality, namely that decision robustness is not affected by how scenarios are generated if these are uniformly distributed and span a sufficiently large range of future states of the world. Several authors have shown that scenario neutrality may not hold, but they did so by adopting either new or computationally expensive modeling. We introduce the Belief-Informed Robust Decision Making (BIRDM) framework to assess how robustness might change under an arbitrary large number of non-uniform distributions at virtually no additional costs with respect to RDM. We apply BIRDM to a flood management problem and find that alternative distributions change the robustness and ranking of measures. BIRDM allows identifying what distributions lead to these changes and under what set of distributions a measure has a specific robustness and rank.
AB - Robust Decision Making (RDM) is an established framework for decision making under deep uncertainty. RDM relies on the idea of scenario neutrality, namely that decision robustness is not affected by how scenarios are generated if these are uniformly distributed and span a sufficiently large range of future states of the world. Several authors have shown that scenario neutrality may not hold, but they did so by adopting either new or computationally expensive modeling. We introduce the Belief-Informed Robust Decision Making (BIRDM) framework to assess how robustness might change under an arbitrary large number of non-uniform distributions at virtually no additional costs with respect to RDM. We apply BIRDM to a flood management problem and find that alternative distributions change the robustness and ranking of measures. BIRDM allows identifying what distributions lead to these changes and under what set of distributions a measure has a specific robustness and rank.
KW - Flood risk management planning
KW - Robust decision making
KW - Uncertain probabilistic information
UR - http://www.scopus.com/inward/record.url?scp=85141949792&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2022.105560
DO - 10.1016/j.envsoft.2022.105560
M3 - Article
AN - SCOPUS:85141949792
VL - 159
JO - Environmental Modelling & Software
JF - Environmental Modelling & Software
SN - 1364-8152
M1 - 105560
ER -