@article{01K6Z8MFA3V2K6W53ZDV85DZ35,
  abstract     = {{We study a generalized version of maximizing expected utility, called E-admissibility, to make decisions when the decision-maker's uncertainty is described by a set of probability mass functions. In particular, instead of specifying this set directly, we assume that we only have partial information about the decision-maker's preferences or choices, in the form of which options she rejects from some finite sets of options. We describe both the decision-making process and the available information using choice functions, and we provide an algorithm, based on linear programming, to compute the most conservative extension of a given choice assessment to a choice function that makes decisions based on E-admissibility. Next, we relate this E-admissible extension to the so-called coherent extension and show how the same techniques that are used to simplify the computation of this coherent extension can also be used to simplify that of the E-admissible one. In our experiments, we demonstrate that decision-making with the E-admissible extension is faster and more informative than with the coherent one, but also observe that the required computations are challenging once the parameters of the problem scale.}},
  articleno    = {{109612}},
  author       = {{Decadt, Arne and Erreygers, Alexander and De Bock, Jasper}},
  issn         = {{0165-0114}},
  journal      = {{FUZZY SETS AND SYSTEMS}},
  keywords     = {{E-admissible extension,Sets of probabilities,E-admissibility,Choice function,Decision-making,Linear programming}},
  language     = {{eng}},
  pages        = {{26}},
  title        = {{Conservative decision-making with sets of probabilities : how to infer new choices from previous ones}},
  url          = {{http://doi.org/10.1016/j.fss.2025.109612}},
  volume       = {{523}},
  year         = {{2026}},
}

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