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Improving and benchmarking of algorithms for decision making with lower previsions.

Nakharutai, Nawapon and Troffaes, Matthias C. M. and Caiado, Camila (2019) 'Improving and benchmarking of algorithms for decision making with lower previsions.', International journal of approximate reasoning., 113 . pp. 91-105.


Maximality, interval dominance, and E-admissibility are three well-known criteria for decision making under severe uncertainty using lower previsions. We present a new fast algorithm for nding maximal gambles. We compare its performance to existing algorithms, one proposed by Troaes and Hable (2014), and one by Jansen, Augustin, and Schollmeyer (2017). To do so, we develop a new method for generating random decision problems with pre-specied ratios of maximal and interval dominant gambles. Based on earlier work, we present ecient ways to nd common feasible starting points in these algorithms. We then exploit these feasible starting points to develop early stopping criteria for the primal-dual interior point method, further improving eciency. We nd that the primal-dual interior point method works best. We also investigate the use of interval dominance to eliminate non-maximal gambles. This can make the problem smaller, and we observe that this ben- ets Jansen et al.'s algorithm, but perhaps surprisingly, not the other two algorithms. We nd that our algorithm, without using interval dominance, outperforms all other algorithms in all scenarios in our benchmarking.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Publisher statement: © 2019 This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Date accepted:28 June 2019
Date deposited:28 June 2019
Date of first online publication:04 July 2019
Date first made open access:04 July 2020

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