Elicitation for decision problems under severe uncertainties

Nakharutai, Nawapon; Troffaes, Matthias; Destercke, Sébastien

Abstract

In this paper, we investigate the problem of eliciting information from an expert, where the assumed uncertainty model is a coherent upper prevision (or equivalently a closed convex set of probabilities). The goal is to solve a decision problem under the maximality decision rule, with as few queries to the expert as possible. To address this, we study the range of coherent upper bounds an expert may give on a given query. In doing so, we provide new results and characterisations for this range. We then use these results to provide an algorithm of elicitation. We illustrate the algorithm on an example.

Citation

Nakharutai, N., Troffaes, M., & Destercke, S. (2024, November). Elicitation for decision problems under severe uncertainties. Presented at The 16th International Conference on Scalable Uncertainty Management (SUM 2024), Palermo, Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: The 16th International Conference on Scalable Uncertainty Management (SUM 2024)
• Start Date: Nov 27, 2024
• End Date: Nov 29, 2024
• Acceptance Date: Aug 31, 2024
• Online Publication Date: Nov 12, 2024
• Publication Date: Nov 12, 2024
• Deposit Date: Oct 21, 2024
• Publicly Available Date: Nov 12, 2024
• Print ISSN: 0302-9743
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 15350
• Pages: 312-324
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 0302-9743
• Book Title: Scalable Uncertainty Management: 6th International Conference, SUM 2024 Palermo, Italy, November 27–29, 2024 Proceedings
• ISBN: 9783031762345
• DOI: https://doi.org/10.1007/978-3-031-76235-2_23
• Public URL: https://durham-repository.worktribe.com/output/2978406