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Durham University

Department of Mathematical Sciences

Academic Staff

Publication details for Matthias Troffaes

Nakharutai, Nawapon, Troffaes, Matthias C. M. & Caiado, Camila (2019). Improving and benchmarking of algorithms for decision making with lower previsions. International Journal of Approximate Reasoning 113: 91-105.

Author(s) from Durham

Abstract

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 Tro aes 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-speci ed 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-
e ts 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.