Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance

Nakharutai, Nawapon and Troffaes, Matthias C. M. and Caiado, Camila C. S. (2021) 'Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance.', International journal of approximate reasoning., 133 . pp. 95-115.

Abstract

Γ-maximin, Γ-maximax and interval dominance are familiar decision criteria for making decisions under severe uncertainty, when probability distributions can only be partially identified. One can apply these three criteria by solving sequences of linear programs. In this study, we present new algorithms for these criteria and compare their performance to existing standard algorithms. Specifically, we use efficient ways, based on previous work, to find common initial feasible points for these algorithms. Exploiting these initial feasible points, we develop early stopping criteria to determine whether gambles are either Γ-maximin, Γ-maximax and interval dominant. We observe that the primal-dual interior point method benefits considerably from these improvements. In our simulation, we find that our proposed algorithms outperform the standard algorithms when the size of the domain of lower previsions is less or equal to the sizes of decisions and outcomes. However, our proposed algorithms do not outperform the standard algorithms in the case that the size of the domain of lower previsions is much larger than the sizes of decisions and outcomes.

Item Type:Article
Full text:Publisher-imposed embargo until 24 March 2023.
(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives 4.0.
File format - PDF (In press, uncorrected proof)
(2072Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1016/j.ijar.2021.03.005
Publisher statement:© 2021 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Date accepted:19 March 2021
Date deposited:30 March 2021
Date of first online publication:24 March 2021
Date first made open access:24 March 2023

Save or Share this output

Export:
Export
Look up in GoogleScholar