Political consultant Bradley Tusk has spent a fortune on mobile voting efforts. Now, he's launching a protocol to try to mainstream the technology. Joe Kiniry, a security expert specializing in elections, was attending an annual conference on voting technology in Washington, DC, when a woman approached him with an unusual offer. She said she represented a wealthy client interested in funding voting systems that would encourage bigger turnouts. Did he have any ideas?

Online social networks have transformed the ways in which political mobilization messages are disseminated, raising new questions about how peer influence operates at scale. Building on the landmark 61-million-person Facebook experiment \citep{bond201261}, we develop an agent-based simulation framework that integrates real U.S. Census demographic distributions, authentic Twitter network topology, and heterogeneous large language model (LLM) agents to examine the effect of mobilization messages on voter turnout. Each simulated agent is assigned demographic attributes, a personal political stance, and an LLM variant (\texttt{GPT-4.1}, \texttt{GPT-4.1-Mini}, or \texttt{GPT-4.1-Nano}) reflecting its political sophistication. Agents interact over realistic social network structures, receiving personalized feeds and dynamically updating their engagement behaviors and voting intentions. Experimental conditions replicate the informational and social mobilization treatments of the original Facebook study. Across scenarios, the simulator reproduces qualitative patterns observed in field experiments, including stronger mobilization effects under social message treatments and measurable peer spillovers. Our framework provides a controlled, reproducible environment for testing counterfactual designs and sensitivity analyses in political mobilization research, offering a bridge between high-validity field experiments and flexible computational modeling.\footnote{Code and data available at https://github.com/CausalMP/LLM-SocioPol}

Perpetual voting considers sequences of decis ions made by the same electorate, where fairness must be evaluated over time rather than perdecision [16]. A centralchallenge in this setting is ensuring adequaterepresentation for voters who are repeatedly in the minority. Traditional a ggregation rules, such as majority voting or Borda count, fail in this regard: they offer no guarantees of long-term fai rness or cumulative influence. In response, methods such as Perpetual Phragmén [17] and Perpetual Consensus [16] hav e been proposed to distribute influence more equitably over time. However, they rely on full knowledge of all voters ' approval sets, implicitly requiring consistent voter participation, a condition which can be hard to satisfy in real-world contexts. Real-world elections face various practical constraints-- including scheduling conflicts, limited resources, and restricted information access--that inevitably prevent vote rs from participating consistently.

Large language models (LLMs) are perceived by some as having the potential to revolutionize social science research, considering their training data includes information on human attitudes and behavior. If these attitudes are reflected in LLM output, LLM-generated "synthetic samples" could be used as a viable and efficient alternative to surveys of real humans. However, LLM-synthetic samples might exhibit coverage bias due to training data and fine-tuning processes being unrepresentative of diverse linguistic, social, political, and digital contexts. In this study, we examine to what extent LLM-based predictions of public opinion exhibit context-dependent biases by predicting voting behavior in the 2024 European Parliament elections using a state-of-the-art LLM. We prompt GPT-4-Turbo with anonymized individual-level background information, varying prompt content and language, ask the LLM to predict each person's voting behavior, and compare the weighted aggregates to the real election results. Our findings emphasize the limited applicability of LLM-synthetic samples to public opinion prediction. We show that (1) the LLM-based prediction of future voting behavior largely fails, (2) prediction accuracy is unequally distributed across national and linguistic contexts, and (3) improving LLM predictions requires detailed attitudinal information about individuals for prompting. In investigating the contextual differences of LLM-based predictions of public opinion, our research contributes to the understanding and mitigation of biases and inequalities in the development of LLMs and their applications in computational social science.

This information was projected on the life cycle of the Their distributed communicating nature makes them vulnerable turnout according to time aging and operation aging to cyberattacks [2]. The security of CPS has criteria in order to compute a cyberthreat likelihood for emerged as a complex problem, after discovering the each current curve observed. Maintenance operators use Stuxnet malware [3] that targeted the Iranian industrial the estimated likelihood to assess the authenticity of each control system.

Former Wisconsin Gov. Scott Walker, R., joined Americas Newsroom to discuss what is at stake with the swing states pivotal election. A Wisconsin woman is using online dating applications to reach young people nationwide and help raise voter turnout during elections, according to a local report. Kristi Johnston is part of Next Gen. America, an organization that works toward increasing voter turnout among young Americans, WKOW-TV reported. Johnston and the group do not push for any specific political party or candidate and instead raise awareness and remind people to get out and vote.

In 2008, when Hillary Clinton first ran for the Democratic presidential nomination against Barack Obama, I asked one of her oldest allies how she could be losing a race that appeared to be hers to win. "I've known Hillary for many years, ever since she came to Arkansas," former Sen. Dale Bumpers told me. "She'll find a way to screw it up. Eight years later, the conventional wisdom is that Clinton is a much better candidate. She learned hard lessons from her failure in 2008; her campaign this year is smarter and less chaotic.