Most decision-making models, including the pairwise comparison method, assume
the decision-makers honesty. However, it is easy to imagine a situation where a
decision-maker tries to manipulate the ranking results. This paper presents
three simple manipulation methods in the pairwise comparison method. We then
try to detect these methods using appropriately constructed neural networks.
Experimental results accompany the proposed solutions on the generated data,
showing a considerable manipulation detection level.

该论文介绍了决策制定模型中的三种简单操纵方法，并使用适当构造的神经网络来检测这些方法。实验结果表明，在生成的数据上，这些解决方案展现了相当的操纵检测水平。

对偶比较方法中的决策操控检测

Detection of decision-making manipulation in the pairwise comparisons  method

Abstract Like electoral systems, decision-making methods are also vulnerable
to manipulation by decision-makers. The ability to effectively defend against
such threats can only come from thoroughly understanding the manipulation
mechanisms. In the presented article, we show two algorithms that can be used
to launch a manipulation attack. They allow for equating the weights of two
selected alternatives in the pairwise comparison method and, consequently,
choosing a leader. The theoretical considerations are accompanied by a Monte
Carlo simulation showing the relationship between the size of the PC matrix,
the degree of inconsistency, and the ease of manipulation. This work is a
continuation of our previous research published in the paper (Szybowski et al.,
2023)

两个算法可以用于发起决策方法中两个被选备选方案的权重相等化的操纵攻击，在使用蒙特卡洛模拟的理论研究中展示了偏好矩阵的大小、不一致度和操纵的易程度之间的关系。