Modelling intentions in large groups is cognitively costly. Not alone must first order beliefs be tracked (’what does A think about X?’), but also beliefs about beliefs (’what does A think about B’s belief concerning X?’). Thus linear increases in group size impose non-linear increases in cognitive processing resources. At the same time, however, large groups offer coordination advantages relative to smaller groups due to specialisation and increased productive capacity. How might these competing demands be reconciled? We propose that fictional narrative can be understood as a cultural tool for dealing with large groups. Specifically, we argue that prototypical action roles that are removed from real-world interactions function as interpretive priors in a form of variational Bayesian inference, such that they allow estimations can be made of unknown social motives. We offer support for this claim in two ways. Firstly, by evaluating the existing literature on narrative cognition and showing where it anticipates a variational model; and secondly, by simulation, where we show that an agent-based model naturally converges on a set of social categories that resemble narrative across a wide range of starting points.

中文翻译：

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虚构叙事作为估计大群体社会倾向的变分贝叶斯方法


在大群体中建模意图的认知成本很高。不仅必须跟踪一阶信念（“A 对 X 有何看法？”），还必须跟踪关于信念的信念（“A 对 B 对 X 的信念有何看法？”）。因此，群体规模的线性增加会导致认知处理资源的非线性增加。然而，与此同时，由于专业化和生产能力的提高，大集团相对于小集团具有协调优势。如何协调这些相互竞争的需求？我们建议，虚构叙事可以被理解为一种与大群体打交道的文化工具。具体来说，我们认为从现实世界的交互中移除的原型行动角色以变分贝叶斯推理的形式充当解释先验，这样它们就可以对未知的社会动机进行估计。我们通过两种方式支持这一主张。首先，通过评估现有的叙事认知文献并展示其对变分模型的预测；其次，通过模拟，我们表明基于代理的模型自然地收敛于一组类似于跨越广泛起点的叙事的社会类别。