Resilience and Active Inference
11 Nov 2022
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TL;DR: In this article , the authors propose an orthogonal, threefold distinction between meanings of the word resilience: inertia, elasticity and plasticity, and map resilience as inertia onto high precision beliefs, resilience as elasticity onto relaxation back to characteristic states, and resilience as plasticity onto functional redundancy and structural degeneracy.
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Abstract: In this paper, we aim to conceptualize and formalize the construct of resilience using the tools of active inference, a new physics-based modeling approach apt for the description and analysis of complex adaptive systems. We intend this as a first step towards a computational model of resilient systems. We begin by offering a conceptual analysis of resilience, to clarify its meaning, as established in the literature. We examine an orthogonal, threefold distinction between meanings of the word “resilience”: (i) inertia, or the ability to resist change (ii) elasticity, or the ability to bounce back from a perturbation, and (iii) plasticity, or the ability to flexibly expand the repertoire of adaptive states. We then situate all three senses of resilience within active inference. We map resilience as inertia onto high precision beliefs, resilience as elasticity onto relaxation back to characteristic (i.e., attracting) states, and resilience as plasticity onto functional redundancy and structural degeneracy.
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Citations
Sustainability under Active Inference
Mahault Albarracin,Maxwell J. D. Ramstead,Riddhi J. Pitliya,Inês Hipólito,Lancelot Da Costa,Maria Raffa,Axel Constant,Sarah Grace Manski +7 more
- 02 May 2024
TL;DR: Sustainability under active inference explores the connection between sustainability, resilience, and well-being within the framework of active inference. It emphasizes the need for holistic sustainability encompassing material wealth, community networks, labor, and knowledge. To achieve sustainability, resource renewal, harmonious system-entity exchanges, self-organization, and resilience are key pathways.
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Addiction as a Disruption of the Markov Blanket: A Hyper-Precision of Momentary Rewards Over Long-Term Adaptation
Mahault Albarracin
TL;DR: Addiction disrupts the Markov blanket, leading to excessive precision on a narrow set of sensory inputs and active states, resulting in compulsive behaviors and compromised long-term adaptation.
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