Tech
BEAST-GB model combines machine learning and behavioral science to predict people’s decisions
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 computational model 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 training data. 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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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.
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Tech
What It’s Like to Have a Brain Implant for 5 Years
Initially, Gorham used his brain-computer interface for single clicks, Oxley says. Then he moved on to multi-clicks and eventually sliding control, which is akin to turning up a volume knob. Now he can move a computer cursor, an example of 2D control—horizontal and vertical movements within a two-dimensional plane.
Over the years, Gorham has gotten to try out different devices using his implant. Zafar Faraz, a field clinical engineer for Synchron, says Gorham directly contributed to the development of Switch Control, a new accessibility feature Apple announced last year that allows brain-computer interface users the ability to control iPhones, iPads, and the Vision Pro with their thoughts.
In a video demonstration shown at an Nvidia conference last year in San Jose, California, Gorham demonstrates using his implant to play music from a smart speaker, turn on a fan, adjust his lights, activate an automatic pet feeder, and run a robotic vacuum in his home in Melbourne, Australia.
“Rodney has been pushing the boundaries of what is possible,” Faraz says.
As a field clinical engineer, Faraz visits Gorham in his home twice a week to lead sessions on his brain-computer interface. It’s Faraz’s job to monitor the performance of the device, troubleshoot problems, and also learn the range of things that Gorham can and can’t do with it. Synchron relies on this data to improve the reliability and user-friendliness of its system.
In the years he’s been working with Gorham, the two have done a lot of experimenting to see what’s possible with the implant. Once, Faraz says, he had Gorham using two iPads side by side, switching between playing a game on one and listening to music on the other. Another time, Gorham played a computer game in which he had to grab blocks on a shelf. The game was tied to an actual robotic arm at the University of Melbourne, about six miles from Gorham’s home, that remotely moved real blocks in a lab.
Gorham, who was an IBM software salesman before he was diagnosed with ALS in 2016, has relished being such a key part of the development of the technology, his wife Caroline says.
“It fits Rodney’s set of life skills,” she says. “He spent 30 years in IT, talking to customers, finding out what they needed from their software, and then going back to the techos to actually develop what the customer needed. Now it’s sort of flipped around the other way.” After a session with Faraz, Gorham will often be smiling ear to ear.
Through field visits, the Synchron team realized it needed to change the setup of its system. Currently, a wire cable with a paddle on one end needs to sit on top of the user’s chest. The paddle collects the brain signals that are beamed through the chest and transmits them via the wire to an external unit that translates those signals into commands. In its second generation system, Synchron is removing that wire.
“If you have a wearable component where there’s a delicate communication layer, we learned that that’s a problem,” Oxley says. “With a paralyzed population, you have to depend on someone to come and modify the wearable components and make sure the link is working. That was a huge learning piece for us.”
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Barkbox Offers: Themed Dog Toys, All-Natural Treats, and Subscription Deals
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Transnational AI regulation needed to protect human rights in the UK | Computer Weekly
The transnational nature of artificial intelligence (AI) means international regulation is essential to tackle the safety issues associated with advanced AI, according to tech chiefs.
In the final evidence session of the Joint Committee on Human Rights inquiry into human rights and the regulation of AI on 25 February, MPs and Lords pressed the AI minister and senior executives from Meta and Microsoft on the adequacy of current safeguards in protecting fundamental rights.
Lawmakers questioned the panel on misinformation, accountability, child safety, existential risk and Britain’s AI sovereignty, probing whether current safeguards are strong enough to protect democratic rights and freedoms as AI systems become embedded across society.
The session came just weeks after the committee warned that the UK’s existing regulatory framework is struggling to keep pace with AI harms – with several regulators telling MPs that a lack of resources, rather than statutory powers, is the greatest hurdle to effective oversight.
Ginny Badanes, general manager of tech for society at Microsoft, and Rob Sherman, deputy chief privacy officer of policy at Meta, welcomed greater harmonisation in regulatory standards at a global level.
Speaking on AI governance, Badanes told MPs the current issue is not a lack of activity, but the bigger challenge of fragmentation.
“I worry at times when we have this variety of approaches that we’re not actually addressing the broader safety or human rights risks that are at the centre of what everyone is trying collectively to solve,” she said.
Transnational by design
Badanes added that “everything about advanced AI is transnational by design – the systems are developed, tested and deployed in a variety of places across borders and within multiple supply chains, and then integrated into products that are used at a global scale”.
She argued that an alignment in international standards could lead to a base layer of agreement, “creating a strong place to get out of fragmented models”.
Sherman mirrored this, noting that Meta operates in most countries worldwide, and that its human rights policy applies globally.
He added that Meta does not build separate AI models for different countries, despite the regional variation in AI governance.
Asked whether the UK’s AI Opportunities Action Plan strikes the right balance between innovation and human rights, both companies were broadly supportive.
Badanes said the UK had made “a sensible start”, building on its “strong foundation of human rights” law and taking a risk-based approach.
Public trust, she argued, is “absolutely critical” to AI adoption. “People will not embrace and use a technology that they do not trust,” said Badanes, adding that strong but proportionate regulation would help secure that trust.
Sherman described the UK’s strategy as “a really thoughtful and sensible approach”, and, in some respects, “a global model”. He also praised the UK’s AI Security Institute as “a global thought leader” in technical AI governance.
Misinformation and democracy
The committee asked if Meta was doing enough to challenge the use of AI by foreign actors on social media, raising concerns about how AI and social media are being used to undermine democratic rights and freedoms.
The committee noted that anonymous posting is increasingly the main way people post on Facebook groups.
Sherman stressed that Facebook is a “real identity platform”, meaning identity is verified using government-issued photo IDs, and that these groups were intended to allow people to share sensitive information without attaching their identity to it. Without accounting for the platform’s own role in spreading misinformation, he said, “I would encourage people to be thoughtful about the sources of the information that they consume”.
However, Sherman said the company would “certainly never suggest that the work to do that is done”, noting that adversaries “continue to evolve their tactics” and “behave adversarially”.
On the reliability of large language models, executives admitted AI systems can generate false information – so-called “hallucinations”. While models are “designed to tell you the truth”, Sherman conceded they are not 100% accurate.
Badanes added: “I think it’s incredibly difficult to ask a large language model to consistently provide you with the truth, in part because of the inherent flaws of the way the systems are designed. I do expect they will continue to get better, but also because truth is at times subjective, and it is a challenging environment to guarantee or ensure anything.”
The committee asked about situations when chatbots provide incorrect or manipulative outputs. Badanes noted the importance of public trust in AI, saying it is lost when the system does not answer a question.
The witnesses said Facebook and Microsoft are working to improve factual alignment, provide citations and, in some cases, indicate levels of confidence in responses. They also emphasised the importance of AI literacy and managing expectations of what services chatbots should provide.
The most difficult questions centred on accountability. When asked who should be responsible if someone suffers harm after relying on incorrect or manipulative AI outputs, such as bad legal advice or encouragement of self-harm, executives stopped short of proposing a specific legal framework.
Microsoft’s Badanes said accountability should attach “where there’s meaningful control”, suggesting responsibility may vary depending on whether harm stems from the model itself, its deployment, or a malicious user. Meta’s Sherman agreed courts would likely need to examine “multiple players” in any given case.
Parental controls
Sherman highlighted that age verification often varies app to app, and highlighted that standardised, platform-level verification is not in the current ecosystem, but would be valuable.
Badanes emphasised the variation in experiences of AI across platforms. “A chatbox where a child can form relationships is going to be a higher-risk scenario than potentially a tutoring app,” she said, encouraging a risk-based approach to AI governance rather than attempting to apply a single age-based threshold across AI tools.
“It’s not just about restricting access, we also need to build these age-appropriate designs and safety guardrails – it’s about adding clear boundaries into the system from the very beginning,” said Badanes.
Existential risks from AI
Asked if individuals should be able to opt out of AI entirely, Sherman said AI has been embedded in services such as Facebook and Instagram “since the beginning”, from news feed ranking to spam filtering. “I don’t think that opting out of AI as a technology is probably realistic,” he said, warning against the idea that it would be possible to “wall off AI from the rest of technology”.
Sherman and Badanes pushed back against binary artificial general intelligence narratives, such as the 2023 extinction-risk statement from the Centre for AI Safety, signed by many leaders in the tech industry, that warned of possible risks of extinction from AI.
Sherman said: “I think the reality is maybe a little bit less exciting and a little bit more mundane, which is that the technology will continue to improve iteratively. I don’t think we’re in a situation where we’re going to wake up one day, and the world is vastly different.”
Badanes described existential harm as “low-probability, high-impact”, stressing that companies are focused on managing both long-term and immediate dangers. “We have to address the risks in the here and now,” said Sherman, while continuing to plan for more extreme scenarios.
Both firms pointed to internal governance structures, including red-teaming exercises, external expert consultation and frontier risk frameworks. Sherman told MPs that through the Frontier programme, Meta evaluates models for “chemical, biological, cyber security and autonomy risks” before and after deployment.
They also emphasised the importance of collaboration with governments, noting that states hold intelligence and national security information unavailable to the private sector.
Speaking to the committee in a separate session, AI minister Kanishka Narayan praised the UK’s AI Security Institute, saying it provides “unparalleled pre-deployment access” to advanced models and plays a key role in developing international evaluation standards.
Badanes likened AI to nuclear regulation. “There are a lot of really complicated challenges that we as a big, large society, have been able to resolve that have had similar roots,” she said.
However, MPs raised concerns about AI researchers who have left major companies over safety disagreements. Asked whether voluntary corporate safeguards were sufficient, Sherman responded that firms have “clear internal reporting mechanisms” and “encourage dissent”, but stopped short of calling for binding global treaties.
Industry leaders urged policymakers to prioritise “interoperable, risk-based global standards” for the most capable systems and invest in content provenance tools, including watermarking, to counter misinformation.
Narayan noted that compared with the first AI summit in Bletchley Park, the India AI Impact Summit was much more focused on the day-to-day experience of people rather than the more abstract, long-term questions of how AI might fundamentally transform the economy, or the more long-term risks it may pose.
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