🤖 AI Summary
This study investigates the vulnerability of maximum likelihood estimation (MLE)-based pairwise ranking systems to strategic data manipulation. Modeling the attack as a constrained combinatorial optimization problem, we propose Adaptive Subset Selection Attack (ASSA), the first efficient structured attack method tailored against MLE-based rankings. Theoretical analysis reveals a sharp phase transition phenomenon in MLE rankings: only a small number of carefully crafted perturbations can induce significant global ranking changes. Extensive experiments on both synthetic and real-world election data demonstrate that ASSA substantially outperforms random and greedy baselines under limited perturbation budgets, highlighting the high sensitivity of MLE ranking mechanisms to structured adversarial perturbations.
📝 Abstract
Pairwise ranking systems based on Maximum Likelihood Estimation (MLE), such as the Bradley-Terry model, are widely used to aggregate preferences from pairwise comparisons. However, their robustness under strategic data manipulation remains insufficiently understood.
In this paper, we study the vulnerability of MLE-based ranking systems to adversarial perturbations. We formulate the manipulation task as a constrained combinatorial optimization problem and propose an Adaptive Subset Selection Attack (ASSA) to efficiently identify high-impact perturbations.
Experimental results on both synthetic data and real-world election datasets show that MLE-based rankings exhibit a sharp phase-transition behavior: beyond a small perturbation budget, a limited number of strategic voters can significantly alter the global ranking. In particular, our method consistently outperforms random and greedy baselines under constrained budgets.
These findings reveal a fundamental sensitivity of MLE-based ranking mechanisms to structured perturbations and highlight the need for more robust aggregation methods in collective decision-making systems.