Coordination and balanced communication support collective problem-solving in organic teams

Collective intelligence–the ability of groups to solve diverse problems–has been explored using laboratory experiments, computer simulations, and questionnaires. These instruments, however, suffer from limitations, such as external validity in the case of laboratory experiments and self-reporting bias in the case of questionnaires. Here we investigate the exploration-exploitation dynamics of small teams using high-frequency, observational data from escape rooms: a non-interventional yet controlled environment where naturally occurring teams solve exploration and exploitation tasks.

We find that more effective teams tend to coordinate throughout problem-solving, exhibit balanced communication patterns, and are more responsive, addressing tasks promptly as they become solvable rather than accumulating them. In contrast, members of less effective teams often work in isolation, participate in problem-solving unequally, and tend to accumulate tasks rather than addressing them as they become solvable. Importantly, we show that no single collaborative structure works for every task: exploitation is faster under team-wide communication and may even benefit from the dominance of key members, while efficient exploration aligns with balanced participation.

Additionally, positive exchanges are associated with faster exploitation but slower exploration. These findings expand the external validity of experimental work on collective intelligence to an organic non-interventional setting and highlight the importance of understanding team performance through behavior instead of team demographics. This project is funded by the European Union under Horizon EU project LearnData, 101086712.

F.B. also acknowledges support from the Air Force Office of Scientific Research under award number FA8655-22-1-7025. R.O. expresses gratitude to Cesar A. Hidalgo for his invaluable guidance and support, and all authors thank him and John Meluso for useful discussions.

Open access funding provided by Corvinus University of Budapest. Center for Collective Learning, CIAS, Corvinus University, Budapest, Hungary Department of Network Science, Institute of Data Analytics and Information Systems, Corvinus University, Budapest, Hungary Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Center for Networks and Complexity, Luiss University of Rome, Rome, Italy Department of AI, Data and Decision Sciences, Luiss University of Rome, Rome, Italy Department of Network and Data Science, Central European University, Vienna, Austria The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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