让智能体用模糊信念融合，反而能提升群体学习准确性。
Imprecise Belief Fusion Improves Multi-agent Social Learning
- 用可调节模糊度的融合机制整合智能体信念
- 在初始错误信念较强时，适度模糊能提高整体学习精度
- 适合研究群体智能与社会学习的学者
在社会学习中，智能体不仅依据直接证据，还通过与同伴互动来更新信念。本文研究了信念模糊性在交互中的作用，提出一种基于命题逻辑公式的社会学习模型，其中智能体通过参数化融合算子结合信念。该算子允许不同层次的模糊性：当两个信念差异较大时，更模糊的融合会产生更不确定的合并结果。通过差分方程模型和基于代理的仿真，在多种条件与初始偏见下进行实验，结果表明：当群体初始偏向错误信念时，一定程度的模糊融合可显著提升学习准确率。这一现象在差分方程固定点的稳定性分析中得到一致支持。
原文摘要 · Abstract (English)
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.
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