作为一个和许多:在主动推理中关联个人和新兴的组级生成模型
Peter Thestrup Waade1, Christoffer Lundbak Olesen1, Jonathan Ehrenreich Laursen2
1Interacting Minds Centre, Aarhus University, 8000 Aarhus, Denmark.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
积极推断模型是如何组形成更大的代理人. 本研究介绍了一种方法,将个体代理模型与群体行为联系起来,揭示了自我组织系统中的非微不足道关系.
科学领域:
- 计算神经科学是一种神经科学.
- 理论生物学 理论生物学
- 人工智能的人工智能
背景情况:
- 基于自由能量原理的积极推理,为跨度尺度的行为和自我维护提供了一个统一的框架.
- 新兴的小组级代理人可以从个人代理人的集体中形成,如果他们保持一个小组级的马尔科夫毯.
- 了解这些新兴群体代理的生成模型是具有挑战性的,限制了多尺度主动推理研究.
研究的目的:
- 提出一个数据驱动的方法来描述一个集团级代理的生成模型与其组成的个体代理的动态之间的关系.
- 通过计算认知建模方法来演示这种方法.
- 探索对理解自我组织系统和嵌套活动推理代理的含义.
主要方法:
- 使用计算认知建模和计算精神病学技术.
- 在使用ActiveInference.jl库的多人武装强盗任务中模拟一个带有马科夫毯子的特工集体.
- 采用基于抽样的参数估计来推断集团级代理人的生成模型.
主要成果:
- 在个体代理的生成模型和新出现的集团级代理之间确定了一个非微不足道的关系.
- 提出的方法论成功地描述了个人和集体代理模型之间的联系.
- 这些发现甚至在简化的多武装强盗任务设置中也是如此.
结论:
- 开发的方法提供了一种新的方法来研究多规模的积极推断和新兴的群体行为.
- 这种方法可以扩展到分析跨越各种时空尺度的嵌套活动推理代理.
- 进一步的研究可以将这种方法应用于各种自我组织系统,从细胞集体到人类社会.
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