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: Mixed Borda branching, which generalizes a rule satisfying copy-robustness, and the random walk rule, 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.

Ana Corina Sosa (second from left), receives the Nobel Peace Prize for her mother, Venezuelan opposition leader Maria Corina Machado, from the Chair of the Norwegian Nobel Committee Jorgen Watne Frydnes next to a photo of Machado, in Oslo on Wednesday. OSLO - Democracies must be prepared to fight for freedom in order to survive, Nobel Peace Prize laureate Maria Corina Machado said on Wednesday, in a speech delivered by her daughter during a ceremony Machado could not attend. The Venezuelan opposition leader said that the prize held profound significance, not only for her country but for the world. "It reminds the world that democracy is essential to peace," she said, via her daughter Ana Corina Sosa Machado. "And the most important, the lesson Venezuelans can share with the world, is a lesson forged on a long and difficult path: If we want democracy, we must be prepared to fight for freedom."

AI can influence voters' minds. What does that mean for democracy? AI chatbots may have the power to influence voters' opinions Does the persuasive power of AI chatbots spell the beginning of the end for democracy? In one of the largest surveys to date exploring how these tools can influence voter attitudes, AI chatbots were more persuasive than traditional political campaign tools including advertisements and pamphlets, and as persuasive as seasoned political campaigners. But at least some researchers identify reasons for optimism in the way in which the AI tools shifted opinions.

This happens in noisy settings when voters have limited information.

Fulkerson's algorithm, we develop a polynomial-time algorithm for computing the

This happens in noisy settings when voters have limited information.

Palantir's CEO is good with ICE and says he defends human rights. But will Israel and Trump ever go too far for him? Alex Karp and I would not seem to have much in common. I work for WIRED, which does tough reporting on Trumpworld; Karp is the CEO of Palantir, a $450 billion firm that has contracts with agencies like the CIA and ICE and worked for the Israeli military during its campaign in Gaza. I live in the East Village of New York City, and the home Karp spends the most time in is a 500-acre compound in rural New Hampshire. I was a plain old English major, and he's got a law degree and a PhD in philosophy, studying under the legendary Jürgen Habermas. I consider myself a progressive; Karp regards that stuff as "pagan religion." But we can bond over one shared status: Both of us are alumni of Central High School, a Philadelphia magnet school. I have some years on the 58-year-old executive.)

Social media has reshaped political discourse, offering politicians a platform for direct engagement while reinforcing polarization and ideological divides. This study introduces a novel topic evolution framework that integrates BERTopic-based topic modeling with Moral Foundations Theory (MFT) to analyze the longevity and moral dimensions of political topics in Twitter activity during the 117th U.S. Congress. We propose a methodology for tracking dynamic topic shifts over time and measuring their association with moral values and quantifying topic persistence. Our findings reveal that while overarching themes remain stable, granular topics tend to dissolve rapidly, limiting their long-term influence. Moreover, moral foundations play a critical role in topic longevity, with Care and Loyalty dominating durable topics, while partisan differences manifest in distinct moral framing strategies. This work contributes to the field of social network analysis and computational political discourse by offering a scalable, interpretable approach to understanding moral-driven topic evolution on social media.

Current frameworks for consensus statement generation with large language models lack the inherent structure needed to provide provable fairness guarantees when aggregating diverse free-form opinions. We model the task as a multi-objective, token-level Markov Decision Process (MDP), where each objective corresponds to an agent's preference. Token-level rewards for each agent are derived from their policy (e.g., a personalized language model). This approach utilizes the finding that such policies implicitly define optimal Q-functions, providing a principled way to quantify rewards at each generation step without a value function (Rafailov et al., 2024). This MDP formulation creates a formal structure amenable to analysis using principles from social choice theory. We propose two approaches grounded in social choice theory. First, we propose a stochastic generation policy guaranteed to be in the ex-ante core, extending core stability concepts from voting theory to text generation. This policy is derived from an underlying distribution over complete statements that maximizes proportional fairness (Nash Welfare). Second, for generating a single statement, we target the maximization of egalitarian welfare using search algorithms within the MDP framework. Empirically, experiments using language models to instantiate agent policies show that search guided by the egalitarian objective generates consensus statements with improved worst-case agent alignment compared to baseline methods, including the Habermas Machine (Tessler et al., 2024).

Abstract: This paper aims to explore two compet ing data science meth odologies to attempt answer ing th e question, " Which issues contributed most to voters' choice in the 2024 presidential election? " The methodologies involve novel empirical evidence driven by artificial intelligence (AI) techniques . By using two distinct methods based on natural language processing and clustering analysis to mine over eight thousand user comments on election - related YouTube videos from one right leaning journal, Wall Street Journal, and one left leaning journal, New York Times, during pre - election week, we quantify the frequency of selected issue areas among user comments to infer which issues were most salient to potential voters in the seven days preceding the November 5th election. Empirically, we primarily demonstrate that immigration and democracy were the most frequently and consistently invoked issues in user comments on the analyzed YouTube videos, followed by the issue of identity politics, while inflation was significantly less frequently referenced. These results corroborate certain findings of post - election surveys but also refute the supposed importance of inflation as an election issue. This indicate s that variations on opinion mining, with their analysis of raw user data online, ca n be more revealing than polling and surveys for analyzing election outcomes. Keywords: artificial intelligence; opinion mining; clustering; vot e choice; cleavages 1. Introduction The Democrats lost both houses of Congress and the Presidency to Republicans in the 2024 election, with former president Donald Trump winning all seven swing states and the national popular vote, despite most pre - election polls giving Vice President Kamala Harris and President Trump a roughly equal chance of winning . Most post - election punditry and analysis in the legacy press and alternative media has attributed the Democrats' large loss to two main issues - inflation [59] and immigration [30] However, a growing contingent of analysts has also attributed the election outcome to the Democratic party's association with cultural issues purportedly distant from the median voter's preferences, such as th ose alternatively aggregated under the concept of "identity" or " woke " politics [54, 56] . To this point, three post - election studies illustrate how voters associated Democrats with left - of - center ideas that were ostensibly distant from most voters' priorities. S urvey research from the think tank Third Way demonstrates that Democrats, and thus Kamala Harris, were largely perceived as "too liberal" [15], while a study from More In Common polling over 5, 000 Americans concluded that while inflation was the top concern for every major demographic group across both parties, Americans misperceived LGBT/transgender policies as the top policy priority for Democrats [37] .