Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

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.

Multiscale Free Energy Analysis of Human Ecosystem Engineering

“…These introduce several directions for further research. From the point of view of practice [48], FEP can be related to the process of human ecosystem engineering. In particular, a major design principle for human ecosystem engineering can be design for preferences that support survival.…”

Psychomotor Predictive Processing

“…Overall, humans survive by reducing prediction errors amidst the complexity of the environmental niches in which they survive [ 72 ]. Active inference is relevant to psychomotor experience in many different practical settings [ 73 ].…”

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

“…Interactions between the internal generative model and the external state takes place across the interface state (i.e., Markov blanket state [ 31 ]). For startups, transition towards scaling up takes place across the interface state through marketing that involves analyzing market characteristics, formulating market offerings, and adapting market offerings in relation to market responses.…”

“…Second, some additional tolerance for unwanted surprise, which can be described as float or slack on an action [ 56 ]: in this case, the action of trying out the startup’s market offering. If an action is carried out many times, limits of tolerable expectancy disconfirmation can be defined in statistical process control (SPC) charts [ 31 ]. However, if the action is a one-off, for example, during the trial of a startup’s good/service by a potential customer, then the limit of tolerable expectancy disconfirmation is a one-off.…”

“…Previous research has explained the relevance of AIT to human organizations that offer high volume low variety goods and/or services: e.g., mass production organizations deploying quality management systems. However, previous research has highlighted the need to for further investigation of AIT’s relevance to human organizations [ 31 ]. Overall, the paper makes a contribution to relating physics of life constructs concerned with energy, action, and ecological fitness to practice in human organizations.…”