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Decision-Making over Compact Preference Structures
Conference proceeding

Decision-Making over Compact Preference Structures

Andrea Martin and Kristen Brent Venable
Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part II, Vol.14506, pp.373-387
Lecture Notes in Computer Science
Machine Learning, Optimization, and Data Science: 9th International Conference (Grasmere, United Kingdom, 09/22/2023–09/26/2023)
09/2023
DOI: https://doi.org/10.1007/978-3-031-53966-4_28
Web of Science ID: WOS:001217090300028

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Abstract

We consider a scenario where a user must make a set of correlated decisions and we propose a computational cognitive model of the deliberation process. We assume the user compactly expresses her preferences via soft constraints and we study how a psychology-based model of human decision-making, namely Multi-Alternative Decision Field Theory (MDFT), can be applied in this context. We design and study sequential and synchronous procedures which combine local decision-making on each variable, with constraint propagation, as well as a one-shot approach. Our experimental results, which focus on tree-shaped Fuzzy Constraint Satisfaction Problems, suggest that decomposing the decision process along the preference structure allows to find solutions of high quality in terms of preferences, maintains MDFT's ability to replicate behavioral effects and is more efficient in terms of computational cost.

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