Manipulation of individual judgments in the quantitative pairwise comparisons method
This addresses security vulnerabilities in decision-making systems for fields relying on expert judgments, but it is incremental as it builds on existing pairwise comparison methods.
The paper tackles the problem of experts being vulnerable to bribery in pairwise comparison decision-making methods, presenting three algorithms to achieve intended manipulations and analyzing them to help defend against such attacks.
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.