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. 2022 Dec 22:13:1059117.
doi: 10.3389/fpsyg.2022.1059117. eCollection 2022.

Resilience and active inference

Mark Miller  1 , Mahault Albarracin  2   3 , Riddhi J Pitliya  2   4 , Alex Kiefer  2   5 , Jonas Mago  6   7   8 , Claire Gorman  9 , Karl J Friston  2   8 , Maxwell J D Ramstead  2   8
Affiliations

Resilience and active inference

Mark Miller et al. Front Psychol. .

Abstract

In this article, 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 toward 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.

Keywords: active inference; complex adaptive system; elasticity; inertia; plasticity; resilience; robustness.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
An example of generative model taken from Albarracin et al. (2021).
See this image and copyright information in PMC

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