voter
Trump Says Voters Are Angry With Republicans--But Not With Him. Here's What Polls Show
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Tucker Carlson Lays Out Manifesto After Vow to Help Form a Third Party. Here's What's In It
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The Winners and Losers From Michigan's High-Stakes Senate Primary
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A new piece of Democrats' midterm strategy: Being 'practical'
Things to Do in L.A. Tap to enable a layout that focuses on the article. The Democrats have made affordability and the economy their defining midterm messaging, playing off elevated inflation and prices for groceries. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.
New Polling After McMorrow's Exit Shakes Up Michigan Senate Race
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Californians back Becerra and reject AI data centers by big margins, poll finds
A new poll shows Xavier Becerra with a hefty lead in the California governor's race. Voters also expressed strong opposition to AI data centers and nuanced views on environmental policies and affordability.
Political Candidates Have An Opening on Clean Energy
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The Kids Aren't Alright With MAGA
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Tight Bounds On The Distortion of Randomized and Deterministic Distributed Voting
We study metric distortion in distributed voting, where nvoters are partitioned into k groups, each selecting a local representative, and a final winner is chosen from these representatives (or from the entire set of candidates). This setting models systems like U.S. presidential elections, where state-level decisions determine the national outcome. We focus on four cost objectives from Anshelevich et al. [1]: avg-avg, avg-max, max-avg, and max-max. We present improved distortion bounds for both deterministic and randomized mechanisms, offering a near-complete characterization of distortion in this model. For deterministic mechanisms, we reduce the upper bound for avg-max from 11 to 7, establish a tight lower bound of 5 for max-avg (improving on 2+ 5), and tighten the upper bound for max-max from 5 to 3. For randomized mechanisms, we consider two settings: (i) only the second stage is randomized, and (ii) both stages may be randomized. In case (i), we prove tight bounds: 5 2/k for avg-avg, 3for avg-max and max-max, and 5for max-avg. In case (ii), we show tight bounds of 3 for max-avg and max-max, and nearly tight bounds for avg-avg and avg-max within [3 2/n, 3 2/(kn)]and [3 2/n, 3], respectively, where n denotes the largest group size.
Anonymous and Copy-Robust Delegations for Liquid Democracy
Liquid democracy with ranked delegations is a novel voting scheme that unites the practicability of representative democracy with the idealistic appeal of direct democracy: Every voter decides between casting their vote on a question at hand or delegating their voting weight to some other, trusted agent. Delegations are transitive, and since voters may end up in a delegation cycle, they are encouraged to indicate not only a single delegate, but a set of potential delegates and a ranking among them. Based on the delegation preferences of all voters, a delegation rule selects one representative per voter. Previous work has revealed a trade-off between two properties of delegation rules called anonymity and copy-robustness. To overcome this issue we study two fractional delegation rules: MIXEDBORDA BRANCHING, which generalizes a rule satisfying copy-robustness, and the RANDOMWALKRULE, which satisfies anonymity. Using the Markov chain tree theorem, we show that the two rules are in fact equivalent, and simultaneously satisfy generalized versions of the two properties. Combining the same theorem with Fulkerson's algorithm, we develop a polynomial-time algorithm for computing the outcome of the studied delegation rule. This algorithm is of independent interest, having applications in semi-supervised learning and graph theory.