What does this research mean for the field?

The Ontology Field model provides a formalized multidimensional representation of consciousness that allows for the simulation of cognitive change and dynamics without relying on conventional time. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

The research aims to create a multidimensional mathematical model to represent and formalize consciousness dynamics.

February 19, 20260 citationsOpen Access

Ontology Field: A Multidimensional Mathematical Model of Consciousness

OGOleg Glushkov

Key Points

The research aims to create a multidimensional mathematical model to represent and formalize consciousness dynamics.
Developed the Ontology Field as a formalized multidimensional space representing consciousness.
Encoded cognitive and behavioral dimensions within nodes using a unique mathematical structure.
Allowed nodes to evolve without conventional time, utilizing higher-dimensional isotopy.
Visualized abstract processes through projections into 3D.
Implemented the model in Python for simulations related to individual and collective consciousness.
Successful mathematical formalization of cognitive change was achieved.
Projections provided interpretable visualizations of complex processes.
Demonstrated potential for nodes to forecast and mitigate damage through protective mechanisms.
Supported simulations revealed insights into cultural dynamics and systemic risk propagation.

Abstract

This work introduces the Ontology Field, a formalized multidimensional space representing consciousness and its dynamics. The field comprises nodes Uα= (Oα, Sα, Iα, Fα, Lα, Hα) U_ = (O_, S_, I_, F_, L_, H_) Uα= (Oα, Sα, Iα, Fα, Lα, Hα), each encoding multiple cognitive and behavioral dimensions. Nodes evolve across higher-dimensional isotopy, allowing formalization of cognitive change without reliance on conventional time. Projections into 3D provide interpretable visualizations of abstract processes. Influence among nodes is formalized mathematically, accounting for initiative, harmony, and external disruptors. Protective mechanisms (reserves) enable nodes to forecast and mitigate potential damage. The model is implemented in Python, supporting simulation of individual and collective consciousness, cultural and social dynamics, and systemic risk propagation. This framework provides a novel computational tool for researchers in mathematical cognition, complex systems, and consciousness studies.

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