# Manipulation of individual judgments in the quantitative pairwise comparisons method

arXiv: https://arxiv.org/abs/2211.01809 · HTML: https://mlsanity.com/paper/2211.01809/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 87 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [On the monotonicity of the eigenvector method](https://mlsanity.com/paper/1902.10790/) (2019)
- [On Axiomatization of Inconsistency Indicators for Pairwise Comparisons](https://mlsanity.com/paper/1307.6272/) (2013)
- [On the Properties of the Priority Deriving Procedure in the Pairwise Comparisons Method](https://mlsanity.com/paper/1401.8219/) (2014)
- [Studying a set of properties of inconsistency indices for pairwise comparisons](https://mlsanity.com/paper/1507.08826/) (2015)
- [Gerrymandering on graphs: Computational complexity and parameterized algorithms](https://mlsanity.com/paper/2102.09889/) (2021)
- [Obvious Manipulability of Voting Rules](https://mlsanity.com/paper/2111.01983/) (2021)

## Built on by (most cited first, 1 of 1)

- [Detection of decision-making manipulation in the pairwise comparisons method](https://mlsanity.com/paper/2405.16693/) (2024)

## Abstract (arXiv)

Decision-making methods very often use the technique of comparing alternatives in pairs. In this approach, experts are asked to compare different options, and then a quantitative ranking is created from the results obtained. It is commonly believed that experts (decision-makers) are honest in their judgments. In our work, we consider a scenario in which experts are vulnerable to bribery. For this purpose, we define a framework that allows us to determine the intended manipulation and present three algorithms for achieving the intended goal. Analyzing these algorithms may provide clues to help defend against such attacks.