Identifying two piecewise linear additive value functions from anonymous preference information
This work addresses a specific challenge in multi-user preference modeling, but it is incremental as it builds on existing additive value function frameworks with a focus on anonymity.
The paper tackles the problem of simultaneously eliciting additive value functions from two decision-makers using anonymous preference responses, and proposes an elicitation procedure that successfully identifies both models under the assumption of piecewise linear marginal value functions with known breaking points.
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.