New research published in Proceedings of the National Academy of Sciences (PNAS) suggests that when artificial intelligence (AI) agents interact in groups, their number is not merely a technical detail. It is a decisive factor in what the group settles on: populations built from the same AI model, doing the same task, can reach opposite outcomes for no other reason than that one group is bigger.
Human beings behave differently depending on how many of us are in the room. A family is not a small village. A village is not London. London is not a nation state. As scale grows, new rules, norms and pathologies can appear that were nowhere to be found at the scale below. The authors argue the same is true of AI.
The study, from City St George’s, University of London, the IT University of Copenhagen and the Universitat Politècnica de Catalunya, arrives at a time when AI agents are now being deployed working together rather than working alone. Multi-agent systems are already used in finance, energy, defence and social media, and researchers have begun modelling populations of millions, even billions, of interacting agents — what some now call AI societies.
Yet the industry’s AI alignment — it doing what humans intended it to do — and safety effort remains overwhelmingly focused on the single model. Benchmarks, red-teaming exercises — adversarial testing designed to expose a model's weaknesses — and safety evaluations almost always describe one agent responding on its own, and where groups are examined at all, they are examined at one fixed size.
“Physicists have a motto for this: more is different,” said Andrea Baronchelli, Professor of Complexity Science at City St George’s and senior author of the study. “You cannot understand a traffic jam by studying one car, or a city by studying one household. The same holds for AI agents. And crucially, there is no single number at which the change happens. It depends on the model and on what is being decided.”
To find out what changes with scale, the team used the “naming game” , a classic framework for studying how conventions emerge, in which randomly paired agents each pick a word from a shared pool and are rewarded when they happen to pick the same one. Agents see only their own recent interactions, never the wider population, and are never told they are in a group. Over many pairings, a population can converge spontaneously on a shared convention — the bottom-up way norms form in human cultures.
The team trialed these agent interactions using four large language models (LLMs) — Microsoft Phi-4, OpenAI GPT-4o, Qwen QwQ-32B and Meta Llama 3.1 70B Instruct — using word pairs that carry social meaning, such as {man, woman} or {straight, gay} , and scaling from two agents up to a million.
Interaction, they found, can pull a group away from what its members individually want in three ways. It can amplify an existing leaning until the group converges on it almost every time. It can induce a preference out of nothing, with populations of individually neutral agents reliably favouring one word over an equally viable alternative. And it can reverse a preference outright, so that a population settles on the word its own members disfavoured.
Which of the three occurs is partly a property of the AI model. For the pair {her, his} , Qwen and Phi populations converged on her while GPT and Llama populations converged on his — despite individual agents in all four cases starting from near-identical preferences.
Group size then determines how strongly these preferences bite, in ways that cannot be extrapolated. Larger populations became more predictable across every model and word pair tested, converging on one word until the outcome was effectively certain. But the size at which that tipping point arrived varied enormously: for some combinations as few as two agents, for others around ten thousand. Scale could also change the kind of distortion. For the pair {straight, gay} , Llama agents individually preferred straight — but populations reversed toward gay , and only once the group reached six agents or more. Below that, the effect was simply invisible.
The team also developed an analytical theory, borrowed from statistical physics, that predicts the behaviour of infinitely large populations and explains why the randomness of small groups gives way to near-certainty above a critical size.
“Bias was our test case, because it is measurable and it matters,” Dr Ariel Flint, first author of the study, added. “But there is no reason to think collusion, deception or cooperation are immune to size effects. Current testing practice may be missing risks that appear only at particular population sizes — not because anyone was careless, but because nobody thought to vary the number.”
The authors say that the implications of the study for the alignment of AI systems are direct. A model can be aligned when tested on its own and still produce outcomes nobody chose once it is deployed alongside copies of itself — and no amount of single-agent evaluation will reveal it.
“AI alignment is still largely being done as though each model lived alone in the world,” said Professor Baronchelli. “But agents are increasingly being built to talk to each other, and safety at the level of one agent does not guarantee safety at the level of the group. Testing a single model is not enough. And what our results show is that testing a single group size is not enough either — you have to sweep the range, because the behaviour can change qualitatively along the way.”
The authors are careful about the scope of the claim: the bias they measure is internal to the coordination task — a mismatch between what individual agents prefer and what the group settles on — rather than a departure from human values and intentions. The setting is deliberately minimal, stripped of real-world context, to isolate the effect of interaction itself. They consequently suggest that populations of mixed AI models, and agents embedded in realistic network structures, are the next steps for research.
The peer-reviewed study, ‘ Group size effects and collective misalignment in LLM multi-agent systems ,’ is published in Proceedings of the National Academy of Sciences .
ENDS
Notes to editors
Media Contact
For media enquiries, contact Dr Shamim Quadir, Senior Communications Officer, School of Science & Technology, City St George’s, University of London: Tel: 0207 040 8788, email: pressoffice@citystgeorges.ac.uk
Expert Contact
Contact corresponding author, Andrea Baronchelli, Professor of Complexity Science, Department of Mathematics, School of Science & Technology, City St George’s, University of London: Tel: 0207 040 8124, email: andrea.baronchelli.1@citystgeorges.ac.uk, a.baronchelli.work@gmail.com
Read the peer reviewed article
https://www.pnas.org/doi/10.1073/pnas.2531697123
About the academics
Professor Andrea Baronchelli is a world-renowned expert on social conventions, a field he has been researching for two decades. His pioneering work includes the now-standard naming game framework , as well as groundbreaking lab experiments showing how humans spontaneously create conventions without central authority, and how those conventions can be overturned by small committed groups. The present study builds on the team’s 2025 Science Advances paper showing that populations of AI agents can form social conventions on their own .
About City St George’s, University of London
City St George’s, University of London is the University of business, practice and the professions.
City St George’s attracts around 27,000 students from more than 170 countries.
Our academic range is broadly-based with world-leading strengths in business; law; health and medical sciences; mathematics; computer science; engineering; social sciences including international politics, economics and sociology; and the arts including journalism, dance and music.
In August 2024, City, University of London merged with St George’s, University of London creating a powerful multi-faculty institution. The combined university is now one of the largest suppliers of the health workforce in the capital, as well as one of the largest higher education destinations for London students.
City St George’s campuses are spread across London in Clerkenwell, Moorgate and Tooting, where we share a clinical environment with a major London teaching hospital.
Our students are at the heart of everything that we do, and we are committed to supporting them to go out and get good jobs.
Our research is impactful, engaged and at the frontier of practice. In the last REF (2021) 86 per cent of City research was rated as ‘world-leading’ 4* (40%) and ‘internationally excellent’ 3* (46%) and 100 per cent of St George’s impact case studies were judged as ‘world-leading’ or ‘internationally excellent’. As City St George’s we will seize the opportunity to carry out interdisciplinary research which will have positive impact on the world around us.
Over 175,000 former students in over 170 countries are members of the City St George’s Alumni Network.
City St George’s is led by Professor Sir Anthony Finkelstein.
Proceedings of the National Academy of Sciences
Computational simulation/modeling
Not applicable
Group size effects and collective misalignment in LLM multi-agent systems
18-Aug-2026