Borda Count in Collective Decision Making: A Summary of Recent Results

Rothe, Jörg

doi:10.1609/aaai.v33i01.33019830

Cited by 4 publications

(7 citation statements)

References 31 publications

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“…Rank aggregation based preferential voting for making group decisions is widely used in selecting a winner in a democratic setup (Rothe, 2019). The basic premise of preferential voting is that n voters are allowed to rank m candidates in the order of their preferences.…”

Section: Robust Predictions With Rank Aggregationmentioning

confidence: 99%

“…Borda count (Black et al, 1958) is one of the approaches for preferential voting that relies on aggregating the rankings of all the voters to make a collective decision (Rothe, 2019;Kahng et al, 2019). There exist other voting strategies to find a winner out of m different choices.…”

Section: Robust Predictions With Rank Aggregationmentioning

confidence: 99%

“…We are not aware of any smooth approximation for the ranking and vote aggregation part of REGroup. Furthermore, theoretical groundings of Borda count for collective decision making (Rothe, 2019) indicates that in order to change the final outcome, manipulators have to manipulate the voters so that they vote strategically and change their rankings accordingly. It has been shown that such manipulations of more than two voters in Borda count based preferential voting scheme is an NP-complete problem (Rothe, 2019;Conitzer et al, 2007;Brelsford et al, 2008;Davies et al, 2011;Betzler et al, 2011;Davies et al, 2014).…”

Section: Bpda Attack and Gradient Maskingmentioning

confidence: 99%

“…Furthermore, theoretical groundings of Borda count for collective decision making (Rothe, 2019) indicates that in order to change the final outcome, manipulators have to manipulate the voters so that they vote strategically and change their rankings accordingly. It has been shown that such manipulations of more than two voters in Borda count based preferential voting scheme is an NP-complete problem (Rothe, 2019;Conitzer et al, 2007;Brelsford et al, 2008;Davies et al, 2011;Betzler et al, 2011;Davies et al, 2014). Even if there exists a smooth approximation, to construct successful BPDA attacks on REGroup, the manipulations necessary would be as hard as NP-complete problems.…”

Section: Bpda Attack and Gradient Maskingmentioning

confidence: 99%

“…Instead, we use rank-order preferences of these classifiers which are then combined using a Borda count-based voting scheme. Borda counts have been used in collective decision making and are known to be robust to various manipulative attacks (Rothe, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions

Tiwari¹,

Madan²,

Anand³

et al. 2022

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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Deep Neural Networks (DNNs) are often criticized for being susceptible to adversarial attacks. Most successful defense strategies adopt adversarial training or random input transformations that typically require retraining or fine-tuning the model to achieve reasonable performance. In this work, our investigations of intermediate representations of a pre-trained DNN lead to an interesting discovery pointing to intrinsic robustness to adversarial attacks. We find that we can learn a generative classifier by statistically characterizing the neural response of an intermediate layer to clean training samples. The predictions of multiple such intermediate-layer based classifiers, when aggregated, show unexpected robustness to adversarial attacks. Specifically, we devise an ensemble of these generative classifiers that rank-aggregates their predictions via a Borda count-based consensus. Our proposed approach uses a subset of the clean training data and a pre-trained model, and yet is agnostic to network architectures or the adversarial attack generation method. We show extensive experiments to establish that our defense strategy achieves state-of-the-art performance on the ImageNet validation set.

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Section: Robust Predictions With Rank Aggregationmentioning

confidence: 99%

Section: Robust Predictions With Rank Aggregationmentioning

confidence: 99%

Section: Bpda Attack and Gradient Maskingmentioning

confidence: 99%

Section: Bpda Attack and Gradient Maskingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions

Tiwari¹,

Madan²,

Anand³

et al. 2022

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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show abstract

The Possible Winner Problem with Uncertain Weights Revisited

Neveling

Rothe

Weishaupt

2021

Fundamentals of Computation Theory

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Complexity of shift bribery for iterative voting rules

Maushagen

Neveling

Rothe

et al. 2022

Ann Math Artif Intell

Self Cite

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In iterative voting systems, candidates are eliminated in consecutive rounds until either a fixed number of rounds is reached or the set of remaining candidates does not change anymore. We focus on iterative voting systems based on the positional scoring rules plurality, veto, and Borda and study their resistance against shift bribery attacks introduced by Elkind et al. [1] and Kaczmarczyk and Faliszewski [2]. In constructive shift bribery (Elkind et al. [1]), an attacker seeks to make a designated candidate win the election by bribing voters to shift this candidate in their preferences; in destructive shift bribery (Kaczmarczyk and Faliszewski [2]), the briber’s goal is to prevent this candidate’s victory. We show that many iterative voting systems are resistant to these types of attack, i.e., the corresponding decision problems are NP-hard. These iterative voting systems include iterated plurality as well as the voting rules due to Hare, Coombs, Baldwin, and Nanson; variants of Hare voting are also known as single transferable vote, instant-runoff voting, and alternative vote.

show abstract

Borda Count in Collective Decision Making: A Summary of Recent Results

Cited by 4 publications

References 31 publications

REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions

REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions

The Possible Winner Problem with Uncertain Weights Revisited

Complexity of shift bribery for iterative voting rules

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