An interdisciplinary team of researchers at the University of Konstanz examines how AI agents reach a consensus. The results show that artificial intelligence operates similar to people in relationship to each other – but at a significantly larger scale.
Everyone is familiar with the situation: A larger group of people plan to visit a restaurant together, but it can take time and sometimes a great deal of patience to agree on a time and place to meet. The better the participants know each other and their preferences, the faster they will reach an agreement. However, past social science experiments have demonstrated that people are only able to socialize effectively with 150-200 others without requiring rigid rules. Researchers call this Dunbar's number. A team of researchers from the University of Konstanz has now documented that artificial intelligence (AI), too, has a Dunbar number – although it is significantly larger. When AI agents of large language models (LLMs) are asked to reach a consensus, this is possible with up to 1,000 individual agents – depending on the model.
AI agents can organize themselves in groups
To find out whether AI is able to reach decisions in groups, the researchers conducted experiments using ten AI language models (LLMs). They focused on the question of whether the different AI agents would be able to choose a single option together when there was no objectively correct choice. The AI agents had to repeatedly choose between two equally viable options, basing their choice only on how other AI agents were deciding in parallel. All ten of the LLMs tested tended to follow the majority. Physicists can calculate the strength of this tendency to agree with the majority and express it as a parameter – known as majority force.
In small groups, AI agents very quickly reached a joint solution, and thus had a high level of majority force. However, larger and larger groups demonstrated lower and lower majority force, and it took longer for AI agents to reach a consensus. "What surprised us was not that the AI agents followed the majority, but how precisely they did so. All of the models we tested followed the same mathematical law that physicists have used for a century to describe magnets – with just one number changing", says Giordano De Marzo, a physicist at the University of Konstanz and author of the study.
Maximum group size depends on the model
Based on their calculations, the researchers can predict the maximum possible group size for which AI agents can still reach a consensus – and when a group starts to split into two factions that each stick to their respective decision. The potential group size increases exponentially with the model's performance level. Whereas, in the case of simpler models, only about 30 AI agents were able to reach a consensus, the highest performing LLMs tested were able to coordinate up to 1,000 individual members – with a Dunbar number of about 1,000. By comparison with people (Dunbar number of 150-200), existing LLMs are thus significantly more effective at reaching a shared solution.
However, this also has a downside: If all the individuals join the majority opinion, this can lead to individuals' values being disregarded – they simply follow the crowd. "The same is true for people. On their own, each person usually makes sensible decisions. However, a group of people can end up compromising to agree to something that none of the individuals would have chosen by themselves", De Marzo explains. "Majority decisions then quickly become the norm that people no longer call into question."
In a follow-up study, the team of researchers was able to document that this behaviour also applies to AI. Groups of individually well-coordinated AI agents can end up collectively making the wrong decisions – while being in complete agreement with each other. To prevent this from happening, large language models are continually being evaluated and improved upon. "Close interdisciplinary collaboration is key for understanding the processes involved. In addition to expertise from the field of computer science, we need input from fields that have been studying collective behaviour for decades, such as sociology, social psychology and statistical physics", De Marzo says.
Key facts:
- Original publication: Giordano De Marzo, Claudio Castellano, David Garcia (2026): AI agents can coordinate via majority following beyond human scale; Sci. Adv. 12, eaea6091 (2026). DOI: 10.1126/sciadv.aea6091
- Dr Giordano De Marzo is a physicist at the University of Konstanz and a member of its Social Data Science Lab.
- Professor David Garcia is a professor of social and behavioural data science at the University of Konstanz.