Abstract
Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.
Highlights
Just as with individuals, social organization needs to remain in nonequilibrium steady-states (NESS) by restriction to a limited number of states. This is because social organization involves living systems that need to resist the second law of thermodynamics in order to persist as bounded self-organizing systems over time
Active inference is a corollary of the free-energy principle (FEP), which formalizes cognition in the autopoietic organization of living systems that resist the second law of thermodynamics by occupying a limited repertoire of states, persisting as bounded self-organizing systems over time
It is explained throughout this paper how industrial engineering and quality management better enable social organization to occupy a limited number of states in order to persist as bounded self-organizing systems over time rather than dissipating into their surrounding environment
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The purpose of this paper is to relate active inference to social organization in order to explain opportunities for, and challenges in, active inference theory providing a unifying framework for social organization employing human and artificial intelligence This involves relating important constructs in active inference to practice concerned with minimizing unwanted surprises. Throughout the paper, theory-based practical examples are discussed that can provide common descriptions for scientists researching active inference and engineers who are interested in implementing active inference research in multi-intelligence social organization They can provide widely applicable practical examples, because industrial engineering and quality management are applied in social organization in many different sectors around the world [14,15].
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