Summary of Sequential Manipulation Against Rank Aggregation: Theory and Algorithm, by Ke Ma et al.
Sequential Manipulation Against Rank Aggregation: Theory and Algorithm
by Ke Ma, Qianqian Xu, Jinshan Zeng, Wei Liu, Xiaochun Cao, Yingfei Sun, Qingming Huang
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Rank aggregation with pairwise comparisons is a widely used method in various fields. However, existing methods are vulnerable to attacks due to the potential adversary’s strong motivation to manipulate the ranking list. This paper explores the potential risks by disrupting the data collection process and fabricating pairwise comparisons without knowledge of future data or true distributions. The confrontation scenario between an online manipulator and a ranker is formulated as a distributionally robust game, which deals with uncertainty of knowledge. Theoretical analysis shows that the equilibrium in this game favors the adversary, making existing sampling algorithms vulnerable to attacks. To address this issue, the paper proposes sequential manipulation policies under a Bayesian decision framework and parametric pairwise comparison models. For attackers with complete knowledge, the proposed policies are asymptotically optimal. A distributionally robust estimator is also introduced to increase the success rate of the sequential manipulation with incomplete knowledge. Empirical evidence confirms that the proposed method can manipulate rank aggregation results in a sequential manner. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how people can cheat when ranking things like soccer teams or movie ratings. Right now, there are ways for bad guys to mess up the rankings and make them unfair. The researchers wanted to know what happens if they create fake information that looks real. They found out that it’s actually pretty easy to manipulate the rankings and make them say whatever you want! To stop this from happening, they came up with new ways to make sure the rankings are fair and honest. It’s like a game where one team tries to trick the other, but in the end, they found ways to make it more fair. |
