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Imprecise belief fusion improves multi-agent social learning

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  • Liu, Zixuan
  • Lawry, Jonathan
  • Crosscombe, Michael

Abstract

In social learning, agents learn not only from direct evidence but also through interactions with their peers. We investigate the role of imprecision in such interactions and ask whether it can improve the effectiveness of the collective learning process. To that end we propose a model of social learning where beliefs are equivalent to formulas in a propositional language, and where agents learn from each other by combining their beliefs according to a fusion operator. The latter is parameterised so as to allow for different levels of imprecision, where a more imprecise fusion operator tends to generate a more imprecise fused belief when the two combined beliefs differ. In this context we describe both difference equation models and agent-based simulations of social learning under a variety of conditions and with different initial biases. The results presented suggest that for populations with a strong initial bias towards incorrect beliefs some level of imprecision in fusion can improve learning accuracy across a range of learning conditions. Furthermore, such benefits of imprecision are consistent with a stability analysis of the fixed points of the proposed difference equation models.

Suggested Citation

  • Liu, Zixuan & Lawry, Jonathan & Crosscombe, Michael, 2025. "Imprecise belief fusion improves multi-agent social learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 664(C).
  • Handle: RePEc:eee:phsmap:v:664:y:2025:i:c:s0378437125000767
    DOI: 10.1016/j.physa.2025.130424
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    References listed on IDEAS

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    1. Dietrich, Franz & List, Christian, 2014. "Probabilistic Opinion Pooling," MPRA Paper 54806, University Library of Munich, Germany.
    2. Rainer Hegselmann & Ulrich Krause, 2006. "Truth and Cognitive Division of Labour: First Steps Towards a Computer Aided Social Epistemology," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(3), pages 1-10.
    3. Bernd Meyer & Cedrick Ansorge & Toshiyuki Nakagaki, 2017. "The role of noise in self-organized decision making by the true slime mold Physarum polycephalum," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-19, March.
    4. Maxime Derex & Alex Mesoudi, 2020. "Cumulative cultural evolution within evolving population structures," Post-Print hal-02923980, HAL.
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