Learning Choice Functions with Gaussian Processes

• URL: http://arxiv.org/abs/2302.00406v1
• Date: Wed, 1 Feb 2023 12:46:43 GMT
• Title: Learning Choice Functions with Gaussian Processes
• Authors: Alessio Benavoli, Dario Azzimonti, Dario Piga
• Abstract summary: In consumer theory, ranking available objects by means of preference relations yields the most common description of individual choices. We propose a choice-model which allows an individual to express a set-valued choice.
• Score: 0.225596179391365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In consumer theory, ranking available objects by means of preference relations yields the most common description of individual choices. However, preference-based models assume that individuals: (1) give their preferences only between pairs of objects; (2) are always able to pick the best preferred object. In many situations, they may be instead choosing out of a set with more than two elements and, because of lack of information and/or incomparability (objects with contradictory characteristics), they may not able to select a single most preferred object. To address these situations, we need a choice-model which allows an individual to express a set-valued choice. Choice functions provide such a mathematical framework. We propose a Gaussian Process model to learn choice functions from choice-data. The proposed model assumes a multiple utility representation of a choice function based on the concept of Pareto rationalization, and derives a strategy to learn both the number and the values of these latent multiple utilities. Simulation experiments demonstrate that the proposed model outperforms the state-of-the-art methods.

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