Extending choice assessments to choice functions: An algorithm for computing the natural extension

Fuente: arXiv
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Auteurs principaux: Decadt, Arne, Erreygers, Alexander, De Bock, Jasper
Format: Preprint
Publié: 2024
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author Decadt, Arne
Erreygers, Alexander
De Bock, Jasper
author_facet Decadt, Arne
Erreygers, Alexander
De Bock, Jasper
contents We study how to infer new choices from prior choices using the framework of choice functions, a unifying mathematical framework for decision-making based on sets of preference orders. In particular, we define the natural (most conservative) extension of a given choice assessment to a coherent choice function -- whenever possible -- and use this natural extension to make new choices. We provide a practical algorithm for computing this natural extension and various ways to improve scalability. Finally, we test these algorithms for different types of choice assessments.
format Preprint
id arxiv_https___arxiv_org_abs_2407_21164
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Extending choice assessments to choice functions: An algorithm for computing the natural extension
Decadt, Arne
Erreygers, Alexander
De Bock, Jasper
Artificial Intelligence
Probability
68T37, 60A99
We study how to infer new choices from prior choices using the framework of choice functions, a unifying mathematical framework for decision-making based on sets of preference orders. In particular, we define the natural (most conservative) extension of a given choice assessment to a coherent choice function -- whenever possible -- and use this natural extension to make new choices. We provide a practical algorithm for computing this natural extension and various ways to improve scalability. Finally, we test these algorithms for different types of choice assessments.
title Extending choice assessments to choice functions: An algorithm for computing the natural extension
topic Artificial Intelligence
Probability
68T37, 60A99
url https://arxiv.org/abs/2407.21164