Quantum-Informed Cybernetics for Collective Intelligence in IoT Systems

Collective intelligence within a quantum-informed cybernetic paradigm presents a transformative perspective to examine adaptability and resilience in Internet of Things (IoT) systems. This paper introduces Cogitor5, a fifth-order cybernetic system that builds upon the foundational principles of the fourth-order COgITOR framework, a liquid computational system designed for complex adaptive processes. The term COgITOR is etymologically linked to the Latin passive verb cogĭtur, translating to “He is gathered,” in contrast to the more commonly recognized active form cogito, meaning “I gather” or “I think,” as famously articulated by Descartes. In contrast to conventional binary systems, Cogitor5 functions as a simulation-based complex adaptive system, inspired by a population of nano agents represented by nanoparticles suspended in a colloidal medium. These agents exhibit autonomous interactions within the solvent, featuring quantum-enabled properties that facilitate advanced self-organization and coevolutionary dynamics. This intricate model captures the complexities of agent interaction, offering a refined representation of their evolving collective intelligence. The study redefines collective intelligence as emergent process intelligence, relevant to the adaptive capacities of both biological and cybernetic systems. By utilizing metacybernetic principles in conjunction with theories of complex adaptive systems, this paper investigates how IoT networks can evolve to enhance agency trajectory formation and increase adaptability. Cogitor5 serves as an innovative computational framework for addressing the inherent complexities of IoT, providing clarity in examining self-organization, self-regulation, self-maintenance, and sustainability, thus elevating system viability. The methodology encompasses the modeling of collective and process intelligence within the scope of Mindset Agency Theory (MAT), an advanced metacybernetic model that allows for evaluable characteristics. Furthermore, this approach integrates theoretical modelling and a practical case study implemented in Matlab® to illustrate agency functionality within a dynamic system simulating failures in the nodes of an electric grid.