Standard
Multi-target Decision Making Under Conditions of Severe Uncertainty. /
Jansen, Christoph; Schollmeyer, Georg; Augustin, Thomas.
Modeling Decisions for Artificial Intelligence: 20th International Conference, MDAI 2023, Umeå, Sweden, June 19–22, 2023, Proceedings. ed. / Vicenç Torra; Yasuo Narukawa. Cham: Springer, 2023. p. 45-57 (Lecture Notes in Artificial Intelligence; Vol. 13890).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Jansen, C, Schollmeyer, G & Augustin, T 2023,
Multi-target Decision Making Under Conditions of Severe Uncertainty. in V Torra & Y Narukawa (eds),
Modeling Decisions for Artificial Intelligence: 20th International Conference, MDAI 2023, Umeå, Sweden, June 19–22, 2023, Proceedings. Lecture Notes in Artificial Intelligence, vol. 13890, Springer, Cham, pp. 45-57.
https://doi.org/10.1007/978-3-031-33498-6_2
APA
Jansen, C., Schollmeyer, G., & Augustin, T. (2023).
Multi-target Decision Making Under Conditions of Severe Uncertainty. In V. Torra, & Y. Narukawa (Eds.),
Modeling Decisions for Artificial Intelligence: 20th International Conference, MDAI 2023, Umeå, Sweden, June 19–22, 2023, Proceedings (pp. 45-57). (Lecture Notes in Artificial Intelligence; Vol. 13890). Springer.
https://doi.org/10.1007/978-3-031-33498-6_2
Vancouver
Jansen C, Schollmeyer G, Augustin T.
Multi-target Decision Making Under Conditions of Severe Uncertainty. In Torra V, Narukawa Y, editors, Modeling Decisions for Artificial Intelligence: 20th International Conference, MDAI 2023, Umeå, Sweden, June 19–22, 2023, Proceedings. Cham: Springer. 2023. p. 45-57. (Lecture Notes in Artificial Intelligence). doi: 10.1007/978-3-031-33498-6_2
Author
Jansen, Christoph ; Schollmeyer, Georg ; Augustin, Thomas. /
Multi-target Decision Making Under Conditions of Severe Uncertainty. Modeling Decisions for Artificial Intelligence: 20th International Conference, MDAI 2023, Umeå, Sweden, June 19–22, 2023, Proceedings. editor / Vicenç Torra ; Yasuo Narukawa. Cham : Springer, 2023. pp. 45-57 (Lecture Notes in Artificial Intelligence).
Bibtex
@inproceedings{16a47c4cd7974c8eaf561fd927e5963f,
title = "Multi-target Decision Making Under Conditions of Severe Uncertainty",
abstract = "The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence{\textquoteright}s performance under the various targets often differ in their scale of measurement, classically being either purely ordinal or perfectly cardinal. In this paper, we transfer recent developments from abstract decision theory with incomplete preferential and probabilistic information to this multi-target setting and show how – by exploiting the (potentially) partial cardinal and partial probabilistic information – more informative orders for comparing decisions can be given than the Pareto order. We discuss some interesting properties of the proposed orders between decision options and show how they can be concretely computed by linear optimization. We conclude the paper by demonstrating our framework in an artificial (but quite real-world) example in the context of comparing algorithms under different performance measures.",
author = "Christoph Jansen and Georg Schollmeyer and Thomas Augustin",
year = "2023",
month = may,
day = "19",
doi = "10.1007/978-3-031-33498-6_2",
language = "English",
isbn = "9783031334979",
series = "Lecture Notes in Artificial Intelligence",
publisher = "Springer",
pages = "45--57",
editor = "Torra, {Vicen{\c c} } and Yasuo Narukawa",
booktitle = "Modeling Decisions for Artificial Intelligence",
}
RIS
TY - GEN
T1 - Multi-target Decision Making Under Conditions of Severe Uncertainty
AU - Jansen, Christoph
AU - Schollmeyer, Georg
AU - Augustin, Thomas
PY - 2023/5/19
Y1 - 2023/5/19
N2 - The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence’s performance under the various targets often differ in their scale of measurement, classically being either purely ordinal or perfectly cardinal. In this paper, we transfer recent developments from abstract decision theory with incomplete preferential and probabilistic information to this multi-target setting and show how – by exploiting the (potentially) partial cardinal and partial probabilistic information – more informative orders for comparing decisions can be given than the Pareto order. We discuss some interesting properties of the proposed orders between decision options and show how they can be concretely computed by linear optimization. We conclude the paper by demonstrating our framework in an artificial (but quite real-world) example in the context of comparing algorithms under different performance measures.
AB - The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence’s performance under the various targets often differ in their scale of measurement, classically being either purely ordinal or perfectly cardinal. In this paper, we transfer recent developments from abstract decision theory with incomplete preferential and probabilistic information to this multi-target setting and show how – by exploiting the (potentially) partial cardinal and partial probabilistic information – more informative orders for comparing decisions can be given than the Pareto order. We discuss some interesting properties of the proposed orders between decision options and show how they can be concretely computed by linear optimization. We conclude the paper by demonstrating our framework in an artificial (but quite real-world) example in the context of comparing algorithms under different performance measures.
U2 - 10.1007/978-3-031-33498-6_2
DO - 10.1007/978-3-031-33498-6_2
M3 - Conference contribution/Paper
SN - 9783031334979
T3 - Lecture Notes in Artificial Intelligence
SP - 45
EP - 57
BT - Modeling Decisions for Artificial Intelligence
A2 - Torra, Vicenç
A2 - Narukawa, Yasuo
PB - Springer
CY - Cham
ER -
