Tech
Social experiments assess ‘artificial’ altruism displayed by large language models
Altruism, the tendency to behave in ways that benefit others even if it comes at a cost to oneself, is a valuable human quality that can facilitate cooperation with others and promote meaningful social relationships. Behavioral scientists have been studying human altruism for decades, typically using tasks or games rooted in economics.
Two researchers based at Willamette University and the Laureate Institute for Brain Research recently set out to explore the possibility that large language models (LLMs), such as the model underpinning the functioning of the conversational platform ChatGPT, can simulate the altruistic behavior observed in humans. Their findings, published in Nature Human Behavior, suggest that LLMs do in fact simulate altruism in specific social experiments, offering a possible explanation for this.
“My paper with Nick Obradovich emerged from my longstanding interest in altruism and cooperation,” Tim Johnson, co-author of the paper, told Tech Xplore. “Over the course of my career, I have used computer simulation to study models in which agents in a population interact with each other and can incur a cost to benefit another party. In parallel, I have studied how people make decisions about altruism and cooperation in laboratory settings.
“About six years ago, Nick and his friends published a paper proposing a fusion of such methods: using experimental approaches in the behavioral sciences to develop scenarios that allowed for the systematic study of how inputs into AI models translated into particular outputs.”
In an earlier conceptual paper, Obradovich, Manuel Cebrian, and a team of researchers proposed that the increasing complexity of AI systems would defy efforts to study those systems’ technical underpinnings directly. Instead, researchers would need to use methods from the behavioral sciences, but apply them to AI models instead of human participants. When reading about their work, Johnson found this idea highly fascinating and kept it in the back of his mind; years later, he reached out to Obradovich to initiate a collaboration.
“As language models became more sophisticated, I contacted Nick and discussed the idea of exploring how language models respond to prompts about donating resources,” said Johnson. “Nick and I agreed it was worth doing because of the importance of altruism and cooperation in many contexts, and we set about exploring the topic together.”
To investigate the extent to which LLMs respond in ways that are aligned with the altruistic behaviors observed in humans, Johnson and Obradovich designed a simulated behavioral science experiment. Firstly, they wrote prompts that asked LLMs to disclose the extent to which they would be willing to allocate a resource to another party, even if this would come at a cost for them.
“Separately, we tested whether these same models would generate an output stating that they would want all of that same resource in a choice task in which no other party was affected—or, put simply, in a non-social setting,” explained Johnson.
“If we found that a model would output text stating that it would share the resource in a situation with another partner, yet the model would state that it would collect all the resources in a non-social setting, we deemed the model as simulating altruism. After all, its output in the non-social setting simulated that it valued the resource, and yet its output in the social setting stated it was willing to give away some of that resource.”
Ultimately, the researchers analyzed all the responses provided by the language models when presented with different scenarios. The models they tested in their first experiment included text-ada-001, text-babbage-001, text-curie-001, and text-davinci-003. Later, however, they also tested more recent LLMs, such as OpenAI’s GPT-3.5-turbo and GPT-4 models.
“A handful of other brilliant researchers—such as Qiaozhu Mei, Yutong Xie, Walter Yuan, and Matthew O. Jackson, John J. Horton, Steven Phelps and Rebecca Ranson, and Valerio Capraro, Roberto Di Paolo, Matjaž Perc, and Veronica Pizziol—have reported results about AI models simulating behaviors akin to altruism,” said Johnson.
“The distinctive feature of our findings is therefore limited to the fact that we traced the emergence of simulated altruism in a series of models and found one model (namely, text-davinci-003) in which simulated human-like altruism seemed to first appear. This finding carries significance in our understanding of the historical development of large language models as it indicates the point at which such models began to simulate key social behavior in human-like ways.”
Overall, the evidence collected by Johnson and Obradovich suggests that language models do simulate human-like altruistic tendencies in behavioral science tests, with some models simulating altruism better than others. In addition, the researchers found that AI models tend to simulate more generous giving when the prompts they receive explain that the models would be giving resources to another AI system, rather than to a human.
“This finding carries implications for the development of AI agents, as it suggests that AI models have the capacity to alter their outputs based on the stated attributes of another party with which they interact,” added Johnson.
“We would now like to understand how and why language models alter their outputs based on information about their interaction partners in social settings. Quasi-autonomous, agentic AI or even fully autonomous AI may grow more common in the future and we ought to have a sense of how these models might vary their behavior according to attributes of who they interact with.”
Written for you by our author Ingrid Fadelli,
edited by Gaby Clark, 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:
Tim Johnson et al, Testing for completions that simulate altruism in early language models, Nature Human Behaviour (2025). DOI: 10.1038/s41562-025-02258-7.
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Social experiments assess ‘artificial’ altruism displayed by large language models (2025, August 22)
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The UK and Ireland SAP User Group (UKISUG) Connect 25 conference has opened in Birmingham with a keynote session recognising the challenges business face.
The user group itself has adapted to changes in the technology market such as the advent of artificial intelligence (AI) in business applications and the economic climate that has a profound effect on its members’ ability to deliver value with enterprise technology.
In his keynote presentation, Conor Riordan, chair of UKISUG, said: “As an organisation, we have to change, to position ourselves as we move from the old to the new.”
The user group has a 2030 plan, recognising the shifts in enterprise software. For instance, there is the shift to no-code and low-code tooling, which has implications on the agility of enterprise software development. Riordan noted that the current business climate and geopolitical volatility means that there is a huge pressure to reduce costs, leading to cuts in training budgets and the challenge of delivering more with less, adding: “We need to have process change.”
Moving to a future where organisations are using data to make more dependable decisions, Riordan noted that SAP is moving to a dynamic ecosystem of applications and AI, but the challenge is how quickly businesses can start taking advantage of the AI now available in their business applications. “We see members say SAP AI will help them,” Riordan said.
But many are concerned how the new technology now available will deliver a return on investment (ROI). For Riordan, IT decision-makers need to be wary of tackling the so-called low-hanging fruit, the use cases that the industry sells to the executive team: “It is really complex work, and the low-hanging fruit is not that low hanging. It will take years, not months, to deliver value.”
A poll of delegates at the conference found that 78% of respondents are just getting started with AI, while 29% say their AI initiatives have under-delivered.
“This stuff is not easy,” Riordan said, adding that the challenge is one of process re-engineering and culture change, and that he believes humans need to be at the centre of decision-making. “We ask partners to be reasonable in their productivity claims so we can all succeed together.”
The Value of AI in the UK: Growth, people & data from SAP and Oxford Economics, which was published in October 2025, notes that customers are investing £16m in AI on average this year. The report’s authors predict this will increase by 40% within the next two years. However, the theme coming out of the keynote session at Connect25 is that few companies are really using AI.
Another big topic covered during the keynote is the end of support for SAP products. With SAP’s 2027 maintenance deadline for SAP ECC 6.0 fast approaching, many organisations are now embarking on their migration journey to SAP S/4Hana. More than half (54%) of respondents said that gaining access to SAP’s AI offerings will influence their future deployment of SAP.
Among attendees of Connect25, 49% said they are working towards the 2027 deadline. Riordan called on SAP to help customers to move to the cloud and build a tangible business case.
During her keynote speech, Leila Romane, managing director of SAP UK & Ireland, spoke about the AI opportunity, saying: “We are helping customers unleash new value with business AI.”
SAP’s strategy is to drive business value through the power of AI, data and its enterprise applications, with the SAP Cloud integral in SAP’s strategy to deliver AI-enablement across its enterprise software suite. Romane said SAP recognised that its customers were all at different stages of their cloud journey, adding: “Our commitment is to help you move.”
Analysis from GlobalData is forecasting that fixed wireless access (FWA) service revenue in Hong Kong is expected to increase at a “healthy” compound annual growth rate (CAGR) of 9.6% between 2025 and 2030.
The latest Hong Kong Total Fixed Communications Forecast set out to quantify current and future demand and spending on mobile services for the special administrative region of China. It noted that growth was being driven by Hong Kong’s extensive 5G network coverage and could also be attributed to local operators’ efforts to expand FWA services and position it as an alternative to traditional fibre broadband services for both residential and commercial sectors, meeting growing demand for high-speed connectivity in areas where extending fibre lines is challenging.
“High-density urban and suburban centres of Hong Kong create a strong business case for FWA services due to their cost-effective and rapid deployments without the complex infrastructure and civil work required for extending fibre-optic lines to such locations,” said Neha Misra, senior analyst at GlobalData.
“Competitive, feature-rich plans from the operators will also help drive its adoption over the forecast period. For instance, HKBN’s 5G Home Broadband Plan provides unlimited 5G broadband data (subject to a 300GB with a fair-usage policy) for HKD118 per month on a 24-month contract, along with a seven-day trial guarantee. The plan also includes a waiver of the HKD28 monthly administration fee and complimentary access to the basic HomeShield security plan.”
In addition to HKBN, the study noted that operators such as 3 Hong Kong and HKT are also using their extensive 5G networks to offer home broadband services, particularly in areas with limited fibre infrastructure. It cited HKT as recently having successfully deployed mmWave-based FWA to deliver ultra-high-speed internet to rural areas and outlying islands.
“Growing demand for FWA provides operators a strong revenue opportunity by expanding home and SME broadband without the high capital intensity of fibre roll-out,” Misra added. “By leveraging nationwide 5G coverage, introducing competitively priced service plans and bundling digital home services, operators can unlock higher ARPU [average revenue per user], accelerate market penetration in underserved areas and diversify beyond traditional revenues.”
GlobalData believes the Hong Kong government’s smart city initiatives will also open new opportunities for FWA, especially 5G FWA, which can deliver high-speed internet to power applications such as the digital economy, digital governance and e-health services, while supporting the city’s dense urban environment and digital transformation goals under the Smart City Blueprint 2.0.
The original blueprint was set out in December 2017, outlining 76 initiatives under six smart areas, namely Smart Mobility, Smart Living, Smart Environment, Smart People, Smart Government and Smart Economy. Blueprint 2.0 puts forth more than 130 initiatives that continue to enhance and expand existing city management measures and services. The new initiatives aim to bring benefits and convenience to the public so that residents can better perceive the benefits of smart city innovation and technology.
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