Multi-target Decision Making Under Conditions of Severe Uncertainty

Jansen, Christoph and Schollmeyer, Georg and Augustin, Thomas (2023) Multi-target Decision Making Under Conditions of Severe Uncertainty. In: Modeling Decisions for Artificial Intelligence : 20th International Conference, MDAI 2023, Umeå, Sweden, June 19–22, 2023, Proceedings. Lecture Notes in Artificial Intelligence . Springer, Cham, pp. 45-57. ISBN 9783031334979

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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’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.

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Contribution in Book/Report/Proceedings
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ID Code:
221171
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Deposited On:
07 Jun 2024 10:40
Refereed?:
Yes
Published?:
Published
Last Modified:
07 Jun 2024 10:40