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This paper uses multi-agent variational inference, a core machine learning method, to test Feyerabend's claim that holding rival theories helps science progress. It compares communities of "pluralist" agents with "monist" ones and reports how beliefs shift, how communication affects them, and how sharing theory-level support differs from sharing beliefs. For AI readers, the value is a formal framework for studying collective belief dynamics in agent systems, though the results are simulation-based and a long way from validated claims about real scientific communities.
Source coveringarXiv

Can Network Models of Science Do Without Pluralist Agents?

arXiv3 min read

arXiv:2611.04590v1 Announce Type: cross Abstract: Science is a process of useful models being proven wrong time and again, but despite its many flaws, it somehow works. Why is this? Paul Feyerabend argued that it works because people dare to hold and defend incompatible rival theories against the accepted one, since a theory's failures often lead to its abandonment in the presence of a more accurate or simpler alternative. He called this view theoretical pluralism. We introduce a theoretical and experimental model in which this can be examined. It compares communities of pluralist agents, which can hold rival theories side by side and maintain beliefs within and between those theories, with communities of monist agents, which can only incorporate what they hear or observe into a single model of their own. In a multi-agent variational inference setting, our experimental demonstrations illustrate how communities with rival theories can go through periods of uncertainty followed by a shift in the theories they hold true, how the information that is communicated between communities affects beliefs when those communities make different observations about the world, and how sharing support for theories and sharing beliefs within them contribute differently to collective belief dynamics.

Title:Can Network Models of Science Do Without Pluralist Agents?

Abstract:Science is a process of useful models being proven wrong time and again, but despite its many flaws, it somehow works. Why is this? Paul Feyerabend argued that it works because people dare to hold and defend incompatible rival theories against the accepted one, since a theory's failures often lead to its abandonment in the presence of a more accurate or simpler alternative. He called this view theoretical pluralism. We introduce a theoretical and experimental model in which this can be examined. It compares communities of pluralist agents, which can hold rival theories side by side and maintain beliefs within and between those theories, with communities of monist agents, which can only incorporate what they hear or observe into a single model of their own. In a multi-agent variational inference setting, our experimental demonstrations illustrate how communities with rival theories can go through periods of uncertainty followed by a shift in the theories they hold true, how the information that is communicated between communities affects beliefs when those communities make different observations about the world, and how sharing support for theories and sharing beliefs within them contribute differently to collective belief dynamics.

21 pages, 6 figures, 1 table. Accepted at IWAI 2026 (International Workshop on Active Inference)

Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)

arXiv:2611.04590 [cs.MA]

(or arXiv:2611.04590v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2611.04590 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 313 words

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