Computational Social Choice Meets Databases

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.

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Protecting Elections by Recounting Ballots

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/37 ◽

2019 ◽

Cited By ~ 4

Author(s):

Edith Elkind ◽

Jiarui Gan ◽

Svetlana Obraztsova ◽

Zinovi Rabinovich ◽

Alexandros A. Voudouris

Keyword(s):

Social Choice ◽

Stackelberg Game ◽

Election Outcome ◽

Complete Picture ◽

Plurality Rule ◽

Computational Social Choice ◽

Two Stage

Complexity of voting manipulation is a prominent topic in computational social choice. In this work, we consider a two-stage voting manipulation scenario. First, a malicious party (an attacker) attempts to manipulate the election outcome in favor of a preferred candidate by changing the vote counts in some of the voting districts. Afterwards, another party (a defender), which cares about the voters' wishes, demands a recount in a subset of the manipulated districts, restoring their vote counts to their original values. We investigate the resulting Stackelberg game for the case where votes are aggregated using two variants of the Plurality rule, and obtain an almost complete picture of the complexity landscape, both from the attacker's and from the defender's perspective.

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Election with Bribe-Effect Uncertainty: A Dichotomy Result

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/23 ◽

2019 ◽

Cited By ~ 1

Author(s):

Lin Chen ◽

Lei Xu ◽

Shouhuai Xu ◽

Zhimin Gao ◽

Weidong Shi

Keyword(s):

Computational Complexity ◽

Social Choice ◽

Deterministic Model ◽

Threshold Value ◽

Realistic Model ◽

Computational Social Choice ◽

Mathematical Property ◽

New Model ◽

Fixed Threshold

We consider the electoral bribery problem in computational social choice. In this context, extensive studies have been carried out to analyze the computational vulnerability of various voting (or election) rules. However, essentially all prior studies assume a deterministic model where each voter has an associated threshold value, which is used as follows. A voter will take a bribe and vote according to the attacker's (i.e., briber's) preference when the amount of the bribe is above the threshold, and a voter will not take a bribe when the amount of the bribe is not above the threshold (in this case, the voter will vote according to its own preference, rather than the attacker's). In this paper, we initiate the study of a more realistic model where each voter is associated with a willingness function, rather than a fixed threshold value. The willingness function characterizes the likelihood a bribed voter would vote according to the attacker's preference; we call this bribe-effect uncertainty. We characterize the computational complexity of the electoral bribery problem in this new model. In particular, we discover a dichotomy result: a certain mathematical property of the willingness function dictates whether or not the computational hardness can serve as a deterrence to bribery attackers.

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

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/11 ◽

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.

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An Analytical and Experimental Comparison of Maximal Lottery Schemes

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/16 ◽

2018 ◽

Cited By ~ 1

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.

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