Equilibrium-Driven Antifragility in Computing Continuum Systems

Abstract

In this article, we propose a novel framework inspired by neuroscience to define and maintain equilibrium in computing continuum systems (CCSs). To do so, we conceptualize equilibrium not as a static state but as a dynamic condition of predictive regulation that supports resilience and adaptation. Our methodology quantifies distance from equilibrium by modeling system behavior using Bayesian networks and computing Kullback–Leibler divergence between expected and observed system states under perturbation. Conceptually, this approach is grounded in the Fluctuation–Dissipation Theorem, which describes how a system’s internal variability predicts its response to external changes. While this is a conceptual article without empirical implementation, it provides a principled foundation based on the Free Energy Principle (FEP) and active inference for future research aimed at managing and potentially designing CCSs that are not only resilient but also antifragile, capable of learning from disruptions to improve their performance over time.