发表机构
School of Engineering Mathematics and Technology, University of Bristol; Graduate School of Arts and Sciences, The University of Tokyo（布里斯托大学工程数学与技术学院; 东京大学文学部・理学部研究科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出带不精确融合算子的多智能体社会学习模型，发现对错误信念有强初始偏差的群体，一定程度的信念融合不精确性可提升学习准确性。
AI 中文摘要
在社会学习中，智能体不仅从直接证据中学习，还通过与同伴的互动学习。本研究探讨此类互动中不精确性的作用，探究其能否提升集体学习过程的有效性。为此，我们提出一种社会学习模型，其中信念等价于命题语言中的公式，智能体通过融合算子结合彼此的信念来相互学习。该融合算子可参数化以允许不同程度的不精确性，当两个待融合信念存在差异时，更不精确的融合算子倾向于生成更不精确的融合信念。在该背景下，我们描述了社会学习的差分方程模型和基于智能体的模拟，模拟涵盖多种条件及不同初始偏差。结果表明，对于对错误信念具有强烈初始偏差的群体，在一系列学习条件下，融合过程中的一定程度不精确性可提升学习准确性；此外，这种不精确性的益处与所提出差分方程模型不动点的稳定性分析一致。
英文摘要
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 parametrised so as to allow for different levels of imprecision, where a more imprecise fusion operator tends to generates 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.
Journal refZixuan Liu, Jonathan Lawry, Michael Crosscombe, Imprecise belief fusion improves multi-agent social learning, Physica A: Statistical Mechanics and its Applications, Volume 664, 2025, 130424, ISSN 0378-4371,
DOI：10.1016/j.physa.2025.130424
