# Detection of decision-making manipulation in the pairwise comparisons method

arXiv: https://arxiv.org/abs/2405.16693 · HTML: https://mlsanity.com/paper/2405.16693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 83 references that are in the map.
- First posted on arXiv 26 May 2024.

## Builds on (most cited first, 11 of 11)

- [On the monotonicity of the eigenvector method](https://mlsanity.com/paper/1902.10790/) (2019)
- [Inconsistency thresholds for incomplete pairwise comparison matrices](https://mlsanity.com/paper/2102.10558/) (2021)
- [On the Properties of the Priority Deriving Procedure in the Pairwise Comparisons Method](https://mlsanity.com/paper/1401.8219/) (2014)
- [How to reduce the number of rating scale items without predictability loss?](https://mlsanity.com/paper/1505.01886/) (2015)
- [Axiomatizations of inconsistency indices for triads](https://mlsanity.com/paper/1801.03355/) (2018)
- [Towards secure judgments aggregation in AHP](https://mlsanity.com/paper/2303.15099/) (2023)
- [Almost optimal manipulation of a pair of alternatives](https://mlsanity.com/paper/2304.03060/) (2023)
- [Axiomatic properties of inconsistency indices for pairwise comparisons](https://mlsanity.com/paper/1306.6852/) (2013)
- [Multiple-criteria Heuristic Rating Estimation](https://mlsanity.com/paper/2205.10428/) (2022)
- [A lexicographically optimal completion for pairwise comparison matrices with missing entries](https://mlsanity.com/paper/2206.10440/) (2022)
- [Manipulation of individual judgments in the quantitative pairwise comparisons method](https://mlsanity.com/paper/2211.01809/) (2022)

## Abstract (arXiv)

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.