Maynard Smith revisited: A multi-agent reinforcement learning approach to the coevolution of signalling behaviour

Olivia Macmillan-Scott; Mirco Musolesi

doi:10.1371/journal.pcbi.1013302

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Open Access

Peer-reviewed

Research Article

Maynard Smith revisited: A multi-agent reinforcement learning approach to the coevolution of signalling behaviour

Olivia Macmillan-Scott ,

Roles Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing – original draft

* E-mail: olivia.macmillan-scott.16@ucl.ac.uk

Affiliation AI Centre, Department of Computer Science, University College London, London, United Kingdom

https://orcid.org/0000-0001-6330-4704

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Mirco Musolesi

Roles Conceptualization, Supervision, Writing – review & editing

Affiliations AI Centre, Department of Computer Science, University College London, London, United Kingdom, Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
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Maynard Smith revisited: A multi-agent reinforcement learning approach to the coevolution of signalling behaviour

Olivia Macmillan-Scott,
Mirco Musolesi

Published: August 26, 2025
https://doi.org/10.1371/journal.pcbi.1013302

About the Authors

Olivia Macmillan-Scott

Roles Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing – original draft

* E-mail: olivia.macmillan-scott.16@ucl.ac.uk

Affiliation AI Centre, Department of Computer Science, University College London, London, United Kingdom

https://orcid.org/0000-0001-6330-4704

Mirco Musolesi

Roles Conceptualization, Supervision, Writing – review & editing

Affiliations AI Centre, Department of Computer Science, University College London, London, United Kingdom, Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

Competing Interests

The authors have declared that no competing interests exist.

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Prosocial behavior
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