Bribery's Influence on Ranked Aggregation
Abstract
Kemeny Consensus is a well-known rank aggregation method in social choice theory. In this method, given a set of rankings, the goal is to find a ranking that minimizes the total Kendall tau distance to the input rankings. Computing a Kemeny consensus is NP-hard, and even verifying whether a given ranking is a Kemeny consensus is coNP-complete. Fitzsimmons and Hemaspaandra [IJCAI 2021] established the computational intractability of achieving a desired consensus through manipulative actions. Kemeny Consensus is an optimisation problem related to Kemeny's rule. In this paper, we consider a decision problem related to Kemeny's rule, known as Kemeny Score, in which the goal is to decide whether there exists a ranking whose total Kendall tau distance from the given rankings is at most . Computation of Kemeny score is known to be NP-complete. In this paper, we investigate the impact of several manipulation actions on the Kemeny Score problem, in which given a set of rankings, an integer , and a ranking , the question is to decide whether it is possible to manipulate the given rankings so that the total Kendall tau distance of from the manipulated rankings is at most . We show that this problem can be solved in polynomial time for various manipulation actions. Interestingly, these same manipulation actions are known to be computationally hard for Kemeny consensus.
Cite
@article{arxiv.2603.28574,
title = {Bribery's Influence on Ranked Aggregation},
author = {Pallavi Jain and Anshul Thakur},
journal= {arXiv preprint arXiv:2603.28574},
year = {2026}
}