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1. Election Control by Manipulating Issue Significance

   Estornell, Andrew ; Das, Sanmay ; Elkind, Edith ; Vorobeychik, Yevgeniy ( January 2020 , Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence)

   Integrity of elections is vital to democratic systems, but it is frequently threatened by malicious actors.The study of algorithmic complexity of the problem of manipulating election outcomes by changing its structural features is known as election control Rothe [2016].One means of election control that has been proposed, pertinent to the spatial voting model, is to select a subset of issues that determine voter preferences over candidates.We study a variation of this model in which voters have judgments about relative importance of issues, and a malicious actor can manipulate these judgments.We show that computing effective manipulations in this model is NP-hard even with two candidates or binary issues.However, we demonstrate that the problem becomes tractable with a constant number of voters or issues.Additionally, while it remains intractable when voters can vote stochastically, we exhibit an important special case in which stochastic voting behavior enables tractable manipulation. 

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2. Manipulating Elections by Selecting Issues

   Lu, Jasper ; Zhang, David ; Rabinovich, Zinovi ; Obraztsova, Svetlana ; Vorobeychik, Yevgeniy ( January 2019 , International Conference on Autonomous Agents and Multiagent Systems)

   Constructive election control considers the problem of an adversary who seeks to sway the outcome of an electoral process in order to ensure that their favored candidate wins. We consider the computational problem of constructive election control via issue selection. In this problem, a party decides which political issues to focus on to ensure victory for the favored candidate. We also consider a variation in which the goal is to maximize the number of voters supporting the favored candidate. We present strong negative results, showing, for example, that the latter problem is inapproximable for any constant factor. On the positive side, we show that when issues are binary, the problem becomes tractable in several cases, and admits a 2-approximation in the two-candidate case. Finally, we develop integer programming and heuristic methods for these problems. 

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3. Most Expected Winner: An Interpretation of Winners over Uncertain Voter Preferences

   Haoyue Ping and Julia Stoyanovich ( June 2023 , Proceedings of the '23 International Conference on the Management of Data (ACM SIGMOD 2023))

   It remains an open question how to determine the winner of an election given incomplete or uncertain voter preferences. One solution is to assume some probability space for the voting profile and declare that the candidates having the best chance of winning are the (co-)winners. We refer to this interpretation as the Most Probable Winner (MPW). In this paper, we focus on elections that use positional scoring rules, and propose an alternative winner interpretation, the Most Expected Winner (MEW), according to the expected performance of the candidates. We separate the uncertainty in voter preferences into the generation step and the observation step, which gives rise to a unified voting profile combining both incomplete and probabilistic voting profiles. We use this framework to establish the theoretical hardness of MEW over incomplete voter preferences, and then identify a collection of tractable cases for a variety of voting profiles, including those based on the popular Repeated Insertion Model (RIM) and its special case, the Mallows model. We develop solvers customized for various voter preference types to quantify the candidate performance for the individual voters, and propose a pruning strategy that optimizes computation. The performance of the proposed solvers and pruning strategy is evaluated extensively on real and synthetic benchmarks, showing that our methods are practical. 

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4. Pandering in a (flexible) representative democracy

   Sun, Xiaolin ; Masur, Jacob ; Abramowitz, Ben ; Mattei, Nicholas ; Zheng ; Zizhan ( August 2023 , Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence)

   In representative democracies, regular election cycles are supposed to prevent misbehavior by elected officials, hold them accountable, and subject them to the “will of the people." Pandering, or dishonest preference reporting by candidates campaigning for election, undermines this democratic idea. Much of the work on Computational Social Choice to date has investigated strategic actions in only a single election. We introduce a novel formal model of pandering and examine the resilience of two voting systems, Representative Democracy (RD) and Flexible Representative Democracy (FRD), to pandering within a single election and across multiple rounds of elections. For both voting systems, our analysis centers on the types of strategies candidates employ and how voters update their views of candidates based on how the candidates have pandered in the past. We provide theoretical results on the complexity of pandering in our setting for a single election, formulate our problem for multiple cycles as a Markov Decision Process, and use reinforcement learning to study the effects of pandering by single candidates and groups of candidates over many rounds. 

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5. VoteXX: Extreme Coercion Resistance

   Chaum, David ( September 2023 , ACM CCS (submitted))

   We solve a long-standing challenge to the integrity of votes cast without the supervision of a voting booth: ``improper influence,'' which we define as any combination of vote buying and voter coercion. In comparison with previous proposals, our system is the first in the literature to protect against a strong adversary who learns all of the voter's keys---we call this property ``extreme coercion resistance.'' Our approach allows each voter, or their trusted agents (which we call ``hedgehogs''), to ``nullify'' (effectively cancel) their vote in a way that is unstoppable and irrevocable, and such that the nullification action is forever unattributable to that voter or their hedgehog(s). We demonstrate the security of VoteXX in the {universal composability} model. Additionally we provide concrete implementations of sub-protocols---including inalienable authentication, decentralized bulletin boards, and anonymous communication channels---that are usually left as abstract assumptions in the literature. As in many other coercion-resistant systems, voters are authorized to vote with public-private keys. Each voter registers their public keys with the Election Authority (EA) in a way that convinces the EA that the voter has complete knowledge of their private keys. Voters concerned about losing their private keys can themselves, or by delegating to one or more hedgehog(s), monitor the bulletin board for malicious ballots cast with their keys, and can act to nullify these ballots in a privacy-preserving manner with zero-knowledge proofs. In comparison with previous proposals, our system makes fewer assumptions and protects against a stronger adversary. For example, votexx makes none of the following assumptions made by previous systems: the voter must complete registration before being coerced; the election will not close before the voter can cast a ballot after coercion; the voter needs to generate a fake password to evade coercion; and the voter knows an honest Election Authority official. 

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