Future-Proofing Startups: Stress Management Principles Based on Adaptive Calibration Model and Active Inference Theory

• Main Author: Stephen Fox
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Entropy
• Subjects: active inference theory (AIT); adaptive calibration model (ACM); double-loop learning; conservation of resources; free energy principle; physics of life
• Online Access: https://www.mdpi.com/1099-4300/23/9/1155

In this paper, the Adaptive Calibration Model (ACM) and Active Inference Theory (AIT) are related to future-proofing startups. ACM encompasses the allocation of energy by the stress response system to alternative options for action, depending upon individuals’ life histories and changing external contexts. More broadly, within AIT, it is posited that humans survive by taking action to align their internal generative models with sensory inputs from external states. The first contribution of the paper is to address the need for future-proofing methods for startups by providing eight stress management principles based on ACM and AIT. Future-proofing methods are needed because, typically, nine out of ten startups do not survive. A second contribution is to relate ACM and AIT to startup life cycle stages. The third contribution is to provide practical examples that show the broader relevance ACM and AIT to organizational practice. These contributions go beyond previous literature concerned with entrepreneurial stress and organizational stress. In particular, rather than focusing on particular stressors, this paper is focused on the recalibrating/updating of startups’ stress responsivity patterns in relation to changes in the internal state of the startup and/or changes in the external state. Overall, the paper makes a contribution to relating physics of life constructs concerned with energy, action and ecological fitness to human organizations.