Variational Principle of Least Psychomotor Action: Modelling Effects on Action from Disturbances in Psychomotor Work Involving Human, Cyborg, and Robot Workers

Self Cite

In theoretical physics and theoretical neuroscience, increased intelligence is associated with increased entropy, which entails potential access to an increased number of states that could facilitate adaptive behavior. Potential to access a larger number of states is a latent entropy as it refers to the number of states that could possibly be accessed, and it is also recognized that functioning needs to be efficient through minimization of manifest entropy. For example, in theoretical physics, the importance of efficiency is recognized through the observation that nature is thrifty in all its actions and through the principle of least action. In this paper, system intelligence is explained as capability to maintain internal stability while adapting to changing environments by minimizing manifest task entropy while maximizing latent system entropy. In addition, it is explained how automated negotiation relates to balancing adaptability and stability; and a mathematical negotiation model is presented that enables balancing of latent system entropy and manifest task entropy in intelligent systems. Furthermore, this first principles analysis of system intelligence is related to everyday challenges in production systems through multiple simulations of the negotiation model. The results indicate that manifest task entropy is minimized when maximization of latent system entropy is used as the criterion for task allocation in the simulated production scenarios.

Section: Balancing Latent Entropy and Manifest Entropymentioning

confidence: 99%

Bio-Inspired Intelligent Systems: Negotiations between Minimum Manifest Task Entropy and Maximum Latent System Entropy in Changing Environments

Fox,

Heikkilä,

Halbach

et al. 2023

Self Cite

“…For example, physical production is dependent upon embodied natural intelligence and artificial intelligence among individuals and groups [63][64][65], which carry out physical work in order to survive in markets. Apropos, active inference provides a framework for embodied perception, action and learning, which can be applied to natural life and to artificial life [66]: for example, to evolutionary robotics [67].…”

Section: Active Inference As a Unifying Frameworkmentioning

confidence: 99%

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

Fox

2021

Self Cite

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.

“…Figure 7 a summarizes actual sensory inputs being within expected difference from preferred sensory inputs: i.e., sensory inputs are within the expected range. In such scenarios, information entropy arises from specific uncertainties about particular work tasks [ 94 ]: not from uncertainties about survival. There is no need for the startup to draw upon its spare resources (i.e., slack), and stress can be positive involving brief mild stress responses.…”

Section: Future-proofing Principles For Startupsmentioning

confidence: 99%

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

Fox

2021

Self Cite

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.