scholarly journals Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise

2020 ◽  
Author(s):  
Ioannis Caragiannis ◽  
Christos Kaklamanis ◽  
Nikos Karanikolas ◽  
George A. Krimpas
Keyword(s):  
Social Choice ◽  
Ground Truth ◽  
Computational Social Choice ◽  
Voting Rules ◽  
Voting Rule ◽  
Robustness To Noise ◽  
Noise Models ◽  
Robust To Noise ◽  
Choice Literature

Approval-based multiwinner voting rules have recently received much attention in the Computational Social Choice literature. Such rules aggregate approval ballots and determine a winning committee of alternatives. To assess effectiveness, we propose to employ new noise models that are specifically tailored for approval votes and committees. These models take as input a ground truth committee and return random approval votes to be thought of as noisy estimates of the ground truth. A minimum robustness requirement for an approval-based multiwinner voting rule is to return the ground truth when applied to profiles with sufficiently many noisy votes. Our results indicate that approval-based multiwinner voting can indeed be robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are.

scholarly journals Computational Social Choice Meets Databases

2018 ◽  
Author(s):  
Benny Kimelfeld ◽  
Phokion G. Kolaitis ◽  
Julia Stoyanovich
Keyword(s):  
Computational Complexity ◽  
Social Choice ◽  
Relational Database ◽  
Database Management ◽  
Plurality Rule ◽  
Technical Level ◽  
Computational Social Choice ◽  
Conceptual Level ◽  
Voting Rules ◽  
Conjunctive Queries

We develop a novel framework that aims to create bridges between the computational social choice and the database management communities. This framework enriches the tasks currently supported in computational social choice with relational database context, thus making it possible to formulate sophisticated queries about voting rules, candidates, voters, issues, and positions. At the conceptual level, we give rigorous semantics to queries in this framework by introducing the notions of necessary answers and possible answers to queries. At the technical level, we embark on an investigation of the computational complexity of the necessary answers. In particular, we establish a number of results about the complexity of the necessary answers of conjunctive queries involving the plurality rule that contrast sharply with earlier results about the complexity of the necessary winners under the plurality rule.

scholarly journals Optimal Voting Rules

1995 ◽  
Vol 9 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Peyton Young
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Social Choice ◽  
Choice Theory ◽  
Social Choice Theory ◽  
Likelihood Estimation ◽  
Voting Rules ◽  
Voting Rule ◽  
Marquis De Condorcet ◽  
Kenneth Arrow

Modern social choice theory, following Kenneth Arrow, treats voting as a method for aggregating diverse preferences and values. An earlier view, initiated by Marquis de Condorcet, is that voting is a method for aggregating information. Voters’ opinions differ because they make errors of judgment; absent these errors they would all agree on the best choice. The goal is to design a voting rule that identifies the best choice with highest probability. This paper examines maximum likelihood estimation. Surprisingly, the optimal rule can also be axiomatized by variations of Arrow's axioms.

scholarly journals An Analytical and Experimental Comparison of Maximal Lottery Schemes

2018 ◽  
Author(s):  
Florian Brandl ◽  
Felix Brandt ◽  
Christian Stricker
Keyword(s):  
Social Choice ◽  
Computer Simulations ◽  
Shannon Entropy ◽  
Experimental Comparison ◽  
Computational Social Choice ◽  
Voting Rules ◽  
Weighted Majority ◽  
Support Size ◽  
Interesting Class ◽  
Pareto Efficient

Randomized voting rules are gaining increasing attention in computational and non-computational social choice. A particularly interesting class of such rules are maximal lottery (ML) schemes, which were proposed by Peter Fishburn in 1984 and have been repeatedly recommended for practical use. However, the subtle differences between different ML schemes are often ignored. Two canonical subsets of ML schemes are C1-ML schemes (which only depend on unweighted majority comparisons) and C2-ML schemes (which only depend on weighted majority comparisons). We prove that C2-ML schemes are the only Pareto efficient---but also among the most manipulable---ML schemes. Furthermore, we evaluate the frequency of manipulable preference profiles and the degree of randomization of ML schemes via extensive computer simulations. In general, ML schemes are rarely manipulable and often do not randomize at all, especially when there are only few alternatives. For up to 21 alternatives, the average support size of ML schemes lies below 4 under reasonable assumptions. The average degree of randomization (in terms of Shannon entropy) of C2-ML schemes is significantly lower than that of C1-ML schemes.

scholarly journals Loss Functions, Axioms, and Peer Review

2021 ◽  
Vol 70 ◽  
pp. 1481-1515
Author(s):  
Ritesh Noothigattu ◽  
Nihar Shah ◽  
Ariel Procaccia
Keyword(s):  
Social Choice ◽  
Peer Review ◽  
Loss Function ◽  
Ground Truth ◽  
Loss Functions ◽  
Empirical Risk Minimization ◽  
Computational Social Choice ◽  
Risk Minimization ◽  
Empirical Risk ◽  
Matrix Extension

It is common to see a handful of reviewers reject a highly novel paper, because they view, say, extensive experiments as far more important than novelty, whereas the community as a whole would have embraced the paper. More generally, the disparate mapping of criteria scores to final recommendations by different reviewers is a major source of inconsistency in peer review. In this paper we present a framework inspired by empirical risk minimization (ERM) for learning the community's aggregate mapping. The key challenge that arises is the specification of a loss function for ERM. We consider the class of L(p,q) loss functions, which is a matrix-extension of the standard class of Lp losses on vectors; here the choice of the loss function amounts to choosing the hyperparameters p and q. To deal with the absence of ground truth in our problem, we instead draw on computational social choice to identify desirable values of the hyperparameters p and q. Specifically, we characterize p=q=1 as the only choice of these hyperparameters that satisfies three natural axiomatic properties. Finally, we implement and apply our approach to reviews from IJCAI 2017.

scholarly journals Decision Making in Committees: Transparency, Reputation, and Voting Rules

2007 ◽  
Vol 97 (1) ◽  
pp. 150-168 ◽  
Author(s):  
Gilat Levy
Keyword(s):  
Decision Making ◽  
Career Concerns ◽  
Decision Making Process ◽  
Voting Rules ◽  
The Public ◽  
Voting Rule ◽  
The Right

In this paper I analyze the effect of transparency on decision making in committees. I focus on committees whose members are motivated by career concerns. The main result is that when the decision-making process is secretive (when individual votes are not revealed to the public), committee members comply with preexisting biases. For example, if the voting rule demands a supermajority to accept a reform, individuals vote more often against reforms. Transparent committees are therefore more likely to accept reforms. I also find that coupled with the right voting rule, a secretive procedure may induce better decisions than a transparent one. (JEL D71, D72)

scholarly journals Quantum learning with noise and decoherence: a robust quantum neural network

2019 ◽  
Author(s):  
Elizabeth Behrman ◽  
Nam Nguyen ◽  
James Steck
Keyword(s):  
Neural Network ◽  
Quantum Computing ◽  
Large Scale ◽  
Learning Approaches ◽  
Quantum Neural Network ◽  
Quantum Learning ◽  
Learning With Noise ◽  
Robustness To Noise ◽  
Robust To Noise ◽  
Arbitrary Quantum

<p>Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing. </p> <p> </p>

Parameterization in Computational Social Choice

2015 ◽  
pp. 1-5
Author(s):  
Piotr Faliszewski ◽  
Rolf Niedermeier
Keyword(s):  
Social Choice ◽  
Computational Social Choice

scholarly journals Image Saliency Prediction in Transformed Domain: A Deep Complex Neural Network Method

2019 ◽  
Vol 33 ◽  
pp. 8521-8528 ◽  
Author(s):  
Lai Jiang ◽  
Zhe Wang ◽  
Mai Xu ◽  
Zulin Wang
Keyword(s):  
Neural Network ◽  
Amplitude Spectrum ◽  
Ground Truth ◽  
Phase Spectrum ◽  
Saliency Maps ◽  
Image Saliency ◽  
Saliency Prediction ◽  
Network Method ◽  
Salient Regions ◽  
Robust To Noise

The transformed domain fearures of images show effectiveness in distinguishing salient and non-salient regions. In this paper, we propose a novel deep complex neural network, named SalDCNN, to predict image saliency by learning features in both pixel and transformed domains. Before proposing Sal-DCNN, we analyze the saliency cues encoded in discrete Fourier transform (DFT) domain. Consequently, we have the following findings: 1) the phase spectrum encodes most saliency cues; 2) a certain pattern of the amplitude spectrum is important for saliency prediction; 3) the transformed domain spectrum is robust to noise and down-sampling for saliency prediction. According to these findings, we develop the structure of SalDCNN, including two main stages: the complex dense encoder and three-stream multi-domain decoder. Given the new SalDCNN structure, the saliency maps can be predicted under the supervision of ground-truth fixation maps in both pixel and transformed domains. Finally, the experimental results show that our Sal-DCNN method outperforms other 8 state-of-theart methods for image saliency prediction on 3 databases.

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