Identifying two piecewise linear additive value functions from anonymous preference information

TL;DR

This paper presents a method to simultaneously identify two decision-makers' additive value functions from anonymous preference responses, assuming piecewise linear marginals with known breakpoints, without noise or answer attribution.

Contribution

It introduces a novel elicitation procedure for recovering two preference models from anonymous, noise-free responses with known breakpoints in piecewise linear functions.

Findings

Successfully identifies two preference models from anonymous responses.

Works with piecewise linear marginal value functions with known breakpoints.

Applicable to scenarios with anonymous preference data.

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 decision-maker.We propose an elicitation procedure that identifies the two preference models when the marginal value functions are piecewise linear with known breaking points.