Identifying two piecewise linear additive value functions from anonymous preference information
#Preference Elicitation #Anonymous Information #Value Functions #Decision-Makers #Piecewise Linear #Additive Value Functions #arXiv Research
📌 Key Takeaways
- Researchers developed a method to identify two decision-makers' preference models from anonymous responses
- The approach works with piecewise linear additive value functions with known breaking points
- The method queries two decision-makers simultaneously without knowing which response belongs to which person
- This research addresses a significant challenge in preference elicitation and aggregation
📖 Full Retelling
Vincent Auriau and four fellow researchers from Khaled Belahcene, Emmanuel Malherbe, Vincent Mousseau, and Marc Pirlot published their research paper 'Identifying two piecewise linear additive value functions from anonymous preference information' on the arXiv preprint server on February 24, 2026, addressing the challenge of identifying two decision-makers' preference models when their responses are anonymous and cannot be attributed to specific individuals. The research focuses on preference elicitation, a process where decision-makers are asked a series of questions to understand their preferences. The authors assume these preferences can be represented by additive value functions. Their novel approach involves querying two decision-makers simultaneously and receiving two answers for each query, without knowing which answer corresponds to which decision-maker. This creates a significant challenge in preference modeling as the responses are anonymized. The researchers propose an innovative elicitation procedure that can successfully identify both preference models when the marginal value functions are piecewise linear with known breaking points, contributing to the field of artificial intelligence by providing a mathematical framework for solving this specific type of preference aggregation problem.
🏷️ Themes
Artificial Intelligence, Preference Modeling, Decision Theory
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.20638 [Submitted on 24 Feb 2026] Title: Identifying two piecewise linear additive value functions from anonymous preference information Authors: Vincent Auriau , Khaled Belahcene , Emmanuel Malherbe , Vincent Mousseau , Marc Pirlot View a PDF of the paper titled Identifying two piecewise linear additive value functions from anonymous preference information, by Vincent Auriau and 4 other authors View PDF Abstract: Eliciting a preference model involves asking a person, named decision-maker, a series of questions. We assume that these preferences can be represented by an additive value function. In this work, we query simultaneously two decision-makers in the aim to elicit their respective value functions. For each query we receive two answers, without noise, but without knowing which answer corresponds to which this http URL propose an elicitation procedure that identifies the two preference models when the marginal value functions are piecewise linear with known breaking points. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20638 [cs.AI] (or arXiv:2602.20638v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.20638 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Vincent Auriau [ view email ] [via CCSD proxy] [v1] Tue, 24 Feb 2026 07:37:02 UTC (1,192 KB) Full-text links: Access Paper: View a PDF of the paper titled Identifying two piecewise linear additive value functions from anonymous preference information, by Vincent Auriau and 4 other authors View PDF TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-02 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explore...
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