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.

成对比较是比较替代方案/标准的长期技术，其作用在现代决策方法的发展中发挥着关键作用，例如层次分析/网络过程 (AHP/ANP)、最佳-最差方法 (BWM) ）、PROMETHEE 等。成对比较可以在多个框架内执行，例如偏好的乘法、加法和模糊表示，它们是基于阿贝尔线性有序组的更通用框架的特定实例。尽管偏好的乘法、加法和模糊表示在实践中被广泛使用，但尚不清楚决策者在测量客观数据时在上述三种表示中是否同样精确。因此，本文的目的是设计、实施和分析来自捷克和意大利两个国家的 200 多名受访者（本科生）的实验，以比较受访者在所有三种表示中的精确度。在实验中，受访者两两比较（通过近似）四个几何图形的面积，然后通过精确的两两比较计算距离来衡量他们评估的不精确性。我们对受访者进行分组，允许每个参与者处理一种独特的代表类型。实验结果表明乘法方法是最精确的。