We propose a novel approach to the statistical analysis of stochastic simulation models and, especially, agent-based models (ABMs). Our main goal is to provide fully automated, model-independent and tool-supported techniques and algorithms to inspect simulations and perform counterfactual analysis. Our approach: (i) is easy-to-use by the modeller, (ii) improves reproducibility of results, (iii) optimizes running time given the modeller's machine, (iv) automatically chooses the number of required simulations and simulation steps to reach user-specified statistical confidence, and (v) automates a variety of statistical tests. In particular, our techniques are designed to distinguish the transient dynamics of the model from its steady-state behaviour (if any), estimate properties in both 'phases', and provide indications on the (non-)ergodic nature of the simulated processes - which, in turn, allows one to gauge the reliability of a steady-state analysis. Estimates are equipped with statistical guarantees, allowing for robust comparisons across computational experiments. To demonstrate the effectiveness of our approach, we apply it to two models from the literature: a large-scale macro-financial ABM and a small scale prediction market model. Compared to prior analyses of these models, we obtain new insights and we are able to identify and fix some erroneous conclusions.

Causal representation learning algorithms discover lower-dimensional representations of data that admit a decipherable interpretation of cause and effect; as achieving such interpretable representations is challenging, many causal learning algorithms utilize elements indicating prior information, such as (linear) structural causal models, interventional data, or weak supervision. Unfortunately, in exploratory causal representation learning, such elements and prior information may not be available or warranted. Alternatively, scientific datasets often have multiple modalities or physics-based constraints, and the use of such scientific, multimodal data has been shown to improve disentanglement in fully unsupervised settings. Consequently, we introduce a causal representation learning algorithm (causalPIMA) that can use multimodal data and known physics to discover important features with causal relationships. Our innovative algorithm utilizes a new differentiable parametrization to learn a directed acyclic graph (DAG) together with a latent space of a variational autoencoder in an end-to-end differentiable framework via a single, tractable evidence lower bound loss function. We place a Gaussian mixture prior on the latent space and identify each of the mixtures with an outcome of the DAG nodes; this novel identification enables feature discovery with causal relationships. Tested against a synthetic and a scientific dataset, our results demonstrate the capability of learning an interpretable causal structure while simultaneously discovering key features in a fully unsupervised setting.

On Thursday, jurors convicted former crypto mogul Sam Bankman-Fried of defrauding his customers out of as much as $10 billion. He will likely spend the rest of his 30s--and possibly his 40s, 50s, and 60s--in prison. The judge is expected to sentence him in March. As former confidants and close friends testified against him during his monthlong trial, Bankman-Fried's parents, Joseph and Barbara, showed up day after day to support their son, whose crypto exchange FTX imploded late last year. The Stanford Law professors' hand gestures and facial expressions played prominently into journalists' recounts of the proceedings, offering the real-life version of the cutaway shot integral to any courtroom TV show.

Defense companies exploring artificial intelligence will help the U.S. military "keep up" with rivals like China, a former fighter pilot told Fox News. The Senate version of the National Defense Authorization Act could include a House-authored provision that prohibits the United States government and its contractors from buying equipment from "adversarial biotech companies" that work to "exploit" Americans' genetic information for "malign purposes," Fox News Digital has learned. Both the Senate and the House of Representatives are currently conferencing and negotiating on final NDAA text that can be passed by both chambers. The provision, which was passed in the original House bill, was introduced by House China Select Committee Chairman Mike Gallagher, R-Wis. The provision prohibits the purchase of biotechnology equipment or services from all United States adversaries, including North Korea, Russia, Iran and China.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A series of drone strikes around the town of Kidal in northern Mali have killed at least 14 people in the rebel stronghold, the town's mayor said Tuesday. Kidal Mayor Arbakane Ag Abzayack told The Associated Press that the town's deputy mayor and a local councillor were among the victims. Residents say victims of the first drone strike Tuesday included children who had gathered in front of the former U.N. peacekeeping camp that was vacated a week ago.

For Max Tegmark, last week's artificial intelligence summit at Bletchley Park was an emotional moment. The MIT professor and AI researcher was behind a letter this year calling for a pause in development of advanced systems. It didn't happen, but it was a crucial contribution to the political and academic momentum that resulted in the Bletchley gathering. "[The summit] has actually made me more optimistic. It really has superseded my expectations," he told me.

Kyiv, Ukraine – Referring to the Soviet puzzle video game, Alla says her husband kills Russian soldiers as though he is playing "human Tetris". "A drone hangs in the sky, and he watches [them] crawl across the forest," she told Al Jazeera. And then they crawl again." The war in Ukraine has turned Avdiivka, where Alla's husband is stationed, into a maze of ruins, trenches and tunnels surrounded by burned-down fields and patches of forest studded with landmines, explosion craters and remnants of Russian soldiers and armoured vehicles. Avdiivka sits 20km (12 miles) north of separatist Donetsk, wedged deep into occupied areas.

FOX News contributor Dr. Rebecca Grant tells'FOX News Live' that she believes tensions in the Middle East can be contained to just Israel. The Pentagon announced a new tactical nuclear bomb program on Oct. 27. Rep. Mike Rogers, R-Ala., and Sen. John Wicker, R-Miss., welcomed the new bomb because it "will better allow the Air Force to reach hardened and deeply-buried targets" in Europe and the Pacific. This B61-13 variant is designed for heavy blast against nasty targets such as underground enemy nuclear missile sites. And by the time the bomb is ready after the late 2020s, AI may have a hand in how and when it's detonated.

Facebook is no stranger to moderating and mitigating misinformation on its platform, having long employed machine learning and artificial intelligence systems to help supplement its human-led moderation efforts. At the start of October, the company extended its machine learning expertise to its advertising efforts with an experimental set of generative AI tools that can perform tasks like generating backgrounds, adjusting image and creating captions for an advertiser's video content. Reuters reports Monday that Meta will specifically not make those tools available to political marketers ahead of what is expected to be a brutal and divisive national election cycle. Meta's decision to bar the use of generative AI is in line with much of the social media ecosystem, though, as Reuters is quick to point out, the company, "has not yet publicly disclosed the decision in any updates to its advertising standards." TikTok and Snap both ban political ads on their networks, Google employs a "keyword blacklist" to prevent its generative AI advertising tools from straying into political speech and X (formerly Twitter) is, well, you've seen it.

In peer mechanisms, the competitors for a prize also determine who wins. Each competitor may be asked to rank, grade, or nominate peers for the prize. Since the prize can be valuable, such as financial aid, course grades, or an award at a conference, competitors may be tempted to manipulate the mechanism. We survey approaches to prevent or discourage the manipulation of peer mechanisms. We conclude our survey by identifying several important research challenges.