[Social] Allostasis: Or, How I Learned To Stop Worrying and Love The Noise

• URL: http://arxiv.org/abs/2508.12791v1
• Date: Mon, 18 Aug 2025 10:06:33 GMT
• Title: [Social] Allostasis: Or, How I Learned To Stop Worrying and Love The Noise
• Authors: Imran Khan,
• Abstract summary: This paper formulates a computational model of allostatic and social allostatic regulation.<n>It employs biophysiologically inspired signal transducers, analogous to hormones like cortisol and oxytocin, to encode information from both the environment and social interactions.<n>Results show that allostatic and social allostatic regulation enable agents to leverage environmental and social noise'' for adaptive reconfiguration.
• Score: 0.6599344783327052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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