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[2602.20638] Identifying two piecewise linear additive value functions from anonymous preference information
Summary
The paper discusses a method for identifying two piecewise linear additive value functions from anonymous preference information, enhancing preference modeling in decision-making.
Why It Matters
This research is significant as it addresses the complexities of preference elicitation in artificial intelligence, particularly in scenarios where decision-makers' identities are unknown. Understanding how to accurately model preferences can improve decision-making processes in various AI applications, such as recommendation systems and automated decision support.
Key Takeaways
- Introduces a novel elicitation procedure for preference modeling.
- Focuses on two decision-makers with anonymous responses.
- Explores piecewise linear functions with known breaking points.
- Aims to enhance the accuracy of preference identification.
- Relevant for applications in AI and decision-making frameworks.
Computer Science > Artificial Intelligence arXiv:2602.20638 (cs) [Submitted on 24 Feb 2026] Title:Identifying two piecewise linear additive value functions from anonymous preference information Authors:Vincent Auriau, Khaled Belahcene (Heudiasyc), Emmanuel Malherbe, Vincent Mousseau (MICS), 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...
