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BEAST-GB model combines machine learning and behavioral science to predict people’s decisions

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BEAST-GB model combines machine learning and behavioral science to predict people’s decisions


Illustration of the model BEAST-GB. Credit: Plonsky et al.

A key objective of behavioral science research is to better understand how people make decisions in situations where outcomes are unknown or uncertain, which entail a certain degree of risk.

The ability to predict people’s choices in these situations could be highly advantageous, as it could help to draft effective initiatives aimed at prompting people to make better decisions for themselves and others in their community.

Researchers at Technion (Israel Institute of Technology) and various institutes in the United States recently developed a new computational model called BEAST-GB, which was found to predict people’s decisions in situations that entail risk and uncertainty.

Their proposed model, outlined in a paper published in Nature Human Behavior, combines advanced machine learning algorithms with behavioral science theory.

“Human-decision research is rich in competing theories, yet none reliably and accurately predicts human choices across contexts,” Ori Plonsky, first author of the paper, told Tech Xplore.

“To see which ideas really work, we organized CPC18, a ‘choice prediction competition’ in which anyone could submit a to predict people’s decisions under risk and uncertainty. We were especially interested in knowing if data-driven machine learning, theory-driven behavioral models, or, as was our guess, a hybrid that embeds behavioral theory inside ML, would excel.”

The new machine learning model developed by Plonsky and his colleagues draws from a behavioral science framework known as BEAST (Best Estimate and Sampling Tools). This is a model based on psychological theories that were previously found to predict people’s decisions with good accuracy.

“BEAST assumes that, in choice under risk and uncertainty, people mix several strategies, such as minimizing the chances of immediate regret or hedging against worst outcomes,” explained Plonsky.

“We translated each strategy into a ‘behavioral feature,’ a concise formula that captures how sensitive a decision-maker should be to that consideration in any given choice task. We then fed these theory-based features, plus purely objective task descriptors, into Extreme Gradient Boosting (a machine learning algorithm known to be highly useful in prediction tournaments)—hence the name BEAST-GB.”

With the enhancements implemented by the researchers, the BEAST-GB model could analyze behavioral data and derive the motives driving decisions, as well as the impact of these motives in different decision-making scenarios.

Notably, BEAST-GB won the CPC18 Choice Prediction Competition in 2018, capturing 93% of predictable variation in the data it was fed, and 96% in follow-up tests utilizing a dataset that was 40 times larger.

“BEAST-GB outperformed dozens of mainstream behavioral models and purely data-driven machine learning,” said Plonsky.

“With just 2% of the training data, it has already beat a deep neural network trained on all the . The model even accurately predicts choices people make in new experiments it has never seen, implying it captures general human choice patterns. Finally, we used it to improve and enhance the underlying interpretable behavioral theory, so it enhances our ability to explain, not only predict, human decision making.”

This recent work highlights the promise of machine learning models that also draw from behavioral science for predicting people’s decisions and responses in real-world scenarios. In the future, BEAST-GB and other similar models could guide the design of new large-scale interventions aimed at improving people’s decisions via nudges, incentives or other behavioral science-based strategies.

Plonsky and his colleagues eventually plan to collaborate with policymakers and other parties involved in the design or implementation of behavioral science initiatives. This would allow them to test their model “in the wild,” validating its potential in real-world settings, while also yielding insight that could inform its further advancement.

“Other recent publications have suggested that human decision-making and other behaviors can be very effectively predicted using advanced data-driven machine learning methods like large language models tuned on large behavioral data,” added Plonsky.

“We now plan to continue investigating when and how BEAST-like theory can enhance such data-driven methods in predicting behavior. Specifically, we plan to extend our domain of research by including natural-language decision problems, more aligned with the real world.”

Written for you by our author Ingrid Fadelli,
edited by Sadie Harley, and fact-checked and reviewed by Robert Egan—this article is the result of careful human work. We rely on readers like you to keep independent science journalism alive.
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please consider a donation (especially monthly).
You’ll get an ad-free account as a thank-you.

More information:
Ori Plonsky et al, Predicting human decisions with behavioural theories and machine learning, Nature Human Behaviour (2025). DOI: 10.1038/s41562-025-02267-6.

© 2025 Science X Network

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BEAST-GB model combines machine learning and behavioral science to predict people’s decisions (2025, August 14)
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Why Everyone Is Suddenly in a ‘Very Chinese Time’ in Their Lives

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Why Everyone Is Suddenly in a ‘Very Chinese Time’ in Their Lives


In case you didn’t get the memo, everyone is feeling very Chinese these days. Across social media, people are proclaiming that “You met me at a very Chinese time of my life,” while performing stereotypically Chinese-coded activities like eating dim sum or wearing the viral Adidas Chinese jacket. The trend blew up so much in recent weeks that celebrities like comedian Jimmy O Yang and influencer Hasan Piker even got in on it. It has now evolved into variations like “Chinamaxxing” (acting increasingly more Chinese) and “u will turn Chinese tomorrow” (a kind of affirmation or blessing).

It’s hard to quantify a zeitgeist, but here at WIRED, chronically online people like us have been noticing a distinct vibe shift when it comes to China over the past year. Despite all of the tariffs, export controls, and anti-China rhetoric, many people in the United States, especially younger generations, have fallen in love with Chinese technology, Chinese brands, Chinese cities, and are overall consuming more Chinese-made products than ever before. In a sense the only logical thing left to do was to literally become Chinese.

“It has occurred to me that a lot of you guys have not come to terms with your newfound Chinese identity,” the influencer Chao Ban joked in a TikTok video that has racked up over 340,000 likes. “Let me just ask you this: Aren’t you scrolling on this Chinese app, probably on a Chinese made phone, wearing clothes that are made in China, collecting dolls that are from China?”

Everything Is China

As is often the case with Western narratives about China, these memes are not really meant to paint an accurate picture of life in the country. Instead, they function as a projection of “all of the undesirable aspects of American life—or the decay of the American dream,” says Tianyu Fang, a PhD researcher at Harvard who studies science and technology in China.

At a moment when America’s infrastructure is crumbling and once-unthinkable forms of state violence are being normalized, China is starting to look pretty good in contrast. “When people say it’s the Chinese century, part of that is this ironic defeat,” says Fang.

As the Trump administration remade the US government in its own image and smashed long-standing democratic norms, people started yearning for an alternative role model, and they found a pretty good one in China. With its awe-inspiring skylines and abundant high-speed trains, the country serves as a symbol of the earnest and urgent desire among many Americans for something completely different from their own realities.

Critics frequently point to China’s massive clean energy investments to highlight America’s climate policy failures, or they point to its urban infrastructure development to shame the US housing shortage. These narratives tend to emphasize China’s strengths while sidelining the uglier facets of its development—but that selectivity is the point. China is being used less as a real place than as an abstraction, a way of exposing America’s own shortcomings. As writer Minh Tran observed in a recent Substack post, “In the twilight of the American empire, our Orientalism is not a patronizing one, but an aspirational one.”

Part of why China is on everyone’s mind is that it’s become totally unavoidable. No matter where you live in the world, you are likely going to be surrounded by things made in China. Here at WIRED, we’ve been documenting that exhaustively: Your phone or laptop or robot vacuum is made in China; your favorite AI slop joke is made in China; Labubu, the world’s most coveted toy, is made in China; the solar panels powering the Global South are made in China; the world’s best-selling EV brand, which officially overtook Tesla last year, is made in China. Even the most-talked about open-source AI model is from China. All of these examples are why this newsletter is called Made in China.





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VTL Group boosts output by 10% with Coats Digital’s GSDCost solution

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VTL Group boosts output by 10% with Coats Digital’s GSDCost solution



Coats Digital is delighted to announce that VTL Group, one of the largest vertically integrated textile manufacturers in the Mediterranean region, has adopted Coats Digital’s GSDCost solution to standardise production methods, increase productivity, and improve pricing accuracy across its Tunisian operations. The initiative is already showing a significant impact, with VTL reducing standard minute values (SMVs) by 15–20% and increasing line output by 10% across its three, key sewing facilities.

With over 5,000 employees and 3,000 sewing machines across 90 sewing lines, VTL Group specialises in jersey knits and denim, producing up to 20 million garments per year for world-renowned brands such as Lacoste, Adidas, G-Star, Hugo Boss, Replay and Paul & Shark. The company operates six garment production units, along with dedicated facilities for screen printing, knitting, dyeing and textile finishing. This extensive vertical integration gives VTL complete control over quality, lead-times and cost-efficiency, which is vital for meeting the stringent demands of its global customer base.

VTL Group has adopted Coats Digital’s GSDCost to standardise production, boost productivity, and improve pricing accuracy across its Tunisian operations.
The solution cut SMVs by 15–20 per cent, raised line output by 10 per cent, and enhanced planning, cost accuracy, and customer confidence, enabling competitive pricing, lean operations, and stronger relationships with global fashion brands.

Prior to implementing GSDCost, VTL calculated capacity and product pricing using data from internal time catalogues stored in Excel. This approach led to inconsistent and inaccurate cost estimations, causing both lost contracts due to inflated production times and reduced margins from underestimations. In some cases, delays caused by misaligned time predictions resulted in increased transportation costs and operational inefficiencies that impacted customer satisfaction.

Hichem Kordoghli, Plant Manager, VTL Group, said: “Before GSDCost, we struggled with inconsistent operating times that directly impacted our competitiveness. We lost orders when our timings were too high and missed profits when they were too low. GSDCost has transformed the way we approach planning, enabling us to quote confidently with accurate, reliable data. We’ve already seen up to 20% reductions in SMVs, a 10% rise in output, and improved customer confidence. It’s a game-changer for our sales and production teams.”

Since adopting GSDCost across 50 sewing lines, VTL Group has been able to establish a reliable baseline for production planning and line efficiency monitoring. This has led to a more streamlined approach to managing load plans and forecasting. Importantly, GSDCost has given the business the flexibility to align pricing more effectively with actual production realities, contributing to greater customer satisfaction and improved profit margins.

Although it’s too early to determine the exact financial impact, VTL Group has already realised improvements in pricing flexibility and competitiveness thanks to shorter product times and better planning. These gains are seen as instrumental in enabling the company to pursue more strategic orders, reduce wasted effort and overtime, and maintain the high expectations of leading global fashion brands.

Hichem Kordoghli, Plant Manager, VTL Group, added: “GSDCost has empowered our teams with reliable data that has translated directly into real operational benefits. We are seeing more consistent line performance, enhanced planning precision, and greater confidence across departments. These improvements are helping us build stronger relationships with our brand partners, while setting the foundation for sustainable productivity gains in the future.”

The company now plans to expand usage across an additional 30 lines in 2025, supported by a second phase of GSD Practitioner Bootcamp training to strengthen in-house expertise and embed best practices throughout the production environment. A further 10 lines are expected to follow in 2026 as part of VTL’s phased rollout strategy.

Liz Bamford, Customer Success Manager, Coats Digital, commented: “We are proud to support VTL Group in their digital transformation journey. The impressive improvements in planning accuracy, quoting precision, and cross-functional alignment are a testament to their commitment to innovation and excellence. GSDCost is helping VTL set a new benchmark for operational transparency and performance in the region, empowering their teams with the tools needed for long-term success.”

GSDCost, Coats Digital’s method analysis and pre-determined times solution, is widely acknowledged as the de-facto international standard across the sewn products industry. It supports a more collaborative, transparent, and sustainable supply chain in which brands and manufacturers establish and optimise ‘International Standard Time Benchmarks’ using standard motion codes and predetermined times. This shared framework supports accurate cost prediction, fact-based negotiation, and a more efficient garment manufacturing process, while concurrently delivering on CSR commitments.

Key Benefits and ROI for VTL Group

  • 15–20% reduction in SMVs across 50 production lines
  • 10% productivity increase across key sewing facilities
  • More competitive pricing for strategic sales opportunities
  • Improved cost accuracy and quotation flexibility
  • Standardised time benchmarks for future factory expansion
  • Enhanced planning accuracy and load plan management
  • Greater alignment with lean and sustainable manufacturing goals
  • Increased brand confidence and satisfaction among premium customers
Note: The headline, insights, and image of this press release may have been refined by the Fibre2Fashion staff; the rest of the content remains unchanged.

Fibre2Fashion News Desk (HU)



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NSA urges continuous checks to achieve zero trust | Computer Weekly

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NSA urges continuous checks to achieve zero trust | Computer Weekly


The US National Security Agency (NSA) has published its latest guidance on zero trust to secure US federal government IT networks and systems. This is the first of two guidance documents coming out of the NSA, providing “practical and actionable” recommendations that can be applied as best practice to secure corporate IT environments both in the public and private sectors.

In the Zero trust primer document, the NSA defines a “zero-trust mindset”, which means assuming IT environment traffic, users, devices and infrastructure may be compromised. To achieve this, the guidance urges IT security teams to establish a rigorous authentication and authorisation process for all access requests.

In the context of securing the integrity of government IT systems, it said that such a strategy enhances the security posture of networks by rigorously validating every access request, which prevents unauthorised changes, reduces risk of malicious code insertion, and ensures the integrity of software and supply chains

The main takeaway from the NSA regarding zero trust is to never trust users or devices that request network connectivity or access to internal resources. The NSA guidance calls for verification without exception, where dynamic authentication and explicit approval is used across all activities on the network, adhering to the principle of least privilege.

Specifically, the NSA’s latest guidance suggests that IT security teams should assume they are working in an IT environment where there is a breach, which means operating and defending resources under the assumption that an adversary already has a presence in the environment.

The NSA said IT security teams should plan for deny-by-default and heavily scrutinise all users, devices, data flows and requests. This means that IT security teams need to log, inspect and monitor all configuration changes, resource accesses and environment traffic for suspicious activity continuously.

The guidance also recommends explicit verification. This implies that access to all resources is consistently verified, using both dynamic and static mechanisms, which is used to derive what the NSA calls “confidence levels for contextual access decisions”.

Commenting on the guidelines, zero-trust expert Brian Soby, CTO and co-founder of AppOmni, said: “Across the guidance, the emphasis is on continuous logging, inspection and monitoring of resource access and configuration change, plus comprehensive visibility across layers.

“Read plainly, the NSA is suggesting that many programs are built around coarse checkpoints and limited signals, while the real risk lives inside enterprise applications, especially SaaS, where sensitive data and business workflows reside.”

Soby’s understanding of the new guidelines is that effective zero trust requires a thorough understanding of what users can and cannot do, instead of simply relying on their ability to authenticate through network directory services and the authorisation that successful authentication gives them.

“Many security programs still substitute directory groups and simplistic roles for true entitlement materiality, even though effective access in modern SaaS is shaped by application-native permissions, sharing rules, delegated administration, conditional controls and third-party OAuth grants.”

He noted that the NSA’s emphasis on monitoring resource access and configuration change implies that relying on coarse identity abstractions leaves IT security teams blind to the actions and permission shifts that create exposure and enable misuse.

“This gap also lines up uncomfortably well with the breaches and campaigns we are seeing now,” he added.

As an example, Soby said that recent intrusions tied to groups tracked as UNC6040 and UNC6395 have highlighted how attackers can bypass traditional, frontdoor-centred controls by abusing SaaS identities and integrations, including compromised OAuth tokens and third-party application access, to reach and extract data from SaaS environments.

“In that light, the NSA’s guidance supports a sharper conclusion: identity security programs that cannot truly understand user activities, behaviours and the materiality of entitlements inside applications do not match the principles of zero trust,” said Soby. “These often become more performative than effective, leaving security operations centre teams stuck with generic signals like logins when the meaningful attacker activity is happening inside the app.”



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