用噪声驱动适应性调节，让智能体更稳健。
[Social] Allostasis: Or, How I Learned To Stop Worrying and Love The Noise
- 模拟激素信号实现环境与社交噪声的主动利用
- 相比被动稳态，适应性存活率显著提升
- 适合研究生物启发式自适应系统的人参考
传统稳态理论认为系统通过抵抗外界扰动来维持稳定，而（社会）异稳态提出系统可主动利用这些扰动，提前调整自身参数以应对环境需求，契合冯·福斯特的‘秩序源于噪声’原则。本文构建了计算模型，采用类皮质醇、催产素等生物信号机制，编码环境与社交信息，驱动动态调节。在多动态环境中的“仿生代理”群体实验表明，异稳态及社会异稳态调节使代理能有效利用环境与社交‘噪声’进行自适应重构，相较纯反应式稳态代理，生存能力显著增强。该研究为社会异稳态原理提供了新的计算视角，有望用于设计更具鲁棒性的生物启发式自适应系统。
原文摘要 · Abstract (English)
The notion of homeostasis typically conceptualises biological and artificial systems as maintaining stability by resisting deviations caused by environmental and social perturbations. In contrast, (social) allostasis proposes that these systems can proactively leverage these very perturbations to reconfigure their regulatory parameters in anticipation of environmental demands, aligning with von Foerster's ``order through noise'' principle. This paper formulates a computational model of allostatic and social allostatic regulation that employs biophysiologically inspired signal transducers, analogous to hormones like cortisol and oxytocin, to encode information from both the environment and social interactions, which mediate this dynamic reconfiguration. The models are tested in a small society of ``animats'' across several dynamic environments, using an agent-based model. The results show that allostatic and social allostatic regulation enable agents to leverage environmental and social ``noise'' for adaptive reconfiguration, leading to improved viability compared to purely reactive homeostatic agents. This work offers a novel computational perspective on the principles of social allostasis and their potential for designing more robust, bio-inspired, adaptive systems
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