Pairwise comparisons have been a long-standing technique for comparing alternatives/criteria and their role has been pivotal in the development of modern decision-making methods such as the Analytic Hierarchy/Network Process (AHP/ANP), the Best-Worst method (BWM), PROMETHEE and many others. Pairwise comparisons can be performed within several frameworks such as multiplicative, additive and fuzzy representations of preferences, which are particular instances of a more general framework based on Abelian linearly ordered groups. Though multiplicative, additive and fuzzy representations of preferences are widely used in practice, it is unknown whether decision makers are equally precise in the three aforementioned representations when they measure objective data. Therefore, the aim of this paper is to design, carry out and analyse an experiment with over 200 respondents (undergraduate university students) from two countries, Czechia and Italy, to compare precision of the respondents in all three representations. In the experiment, respondents pairwise compared (by approximation) the areas of four geometric figures and then, the imprecision of their assessments was measured by computing the distance with the exact pairwise comparisons. We grouped the respondents in such a way that each participant was allowed to deal with a unique type of representation. The outcomes of the experiment indicate that the multiplicative approach is the most precise.

As an important major public health emergency, COVID-19 broke out more than two years. At present, China has entered the post-epidemic era. However, it is still necessary to study the medical health resource allocation in public health emergencies. Therefore, the evaluation of medical health resources allocation is important. Firstly, we use two kinds of linguistic preference orderings (LPOs) to represent experts’ opinions when evaluating the medical health resources allocation in public health emergencies. Then, a novel ORESTE method with LPOs is developed to solve multiple criteria decision-making (MCDM) problems. Additionally, we apply the proposed ORESTE method to solve a practical MCDM problem involving the medical health resources allocation in public health emergencies. Finally, some comparative analyses among the proposed ORESTE method and some existing methods under a double hierarchy linguistic environment are set up, and some discussions are summarized to show the validity and applicability of the proposed novel ORESTE method.