• Computer Science > Artificial Intelligence [Submitted on 24 Feb 2026] Title:Identifying two piecewise linear additive value functions from anonymous preference information View PDFAbstract: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. • Submission history From: Vincent Auriau [view email] [via CCSD proxy][v1] Tue, 24 Feb 2026 07:37:02 UTC (1,192 KB) References & Citations export BibTeX citation Loading… • Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) Papers with Code (What is Papers with Code?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (W

Article Summaries:

  • Researchers Vincent Auriau and colleagues present a method for simultaneously eliciting additive value functions from two anonymous decision‑makers. By asking a series of preference queries and receiving two answers per query-without knowing which answer belongs to which individual-the procedure identifies both participants’ piecewise‑linear marginal value functions, provided the breakpoints are known. The approach assumes noiseless responses and additive structure, and it offers a systematic way to recover two distinct preference models from anonymous data. This contribution advances preference‑learning techniques in multi‑criteria decision analysis and could inform applications where individual identities are withheld.

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