Formal Model of Consciousness Based on Integrated Information Theory and Graph Theory
Zenodo (CERN European Organization for Nuclear Research) August 31, 2026 DOI: 10.5281/zenodo.22187462 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key points | Proposes that consciousness can be quantified through graph-theoretic measures of information network complexity, translating IIT into a formal model and defining metrics like node degree, clustering coefficient, and path length to support future algorithmic estimation. |
Abstract
This paper proposes a formal mathematical model of consciousness based on Giulio Tononi's Integrated Information Theory (IIT) and graph theory. The core idea is that consciousness can be understood through a quantifiable measure derived from the structure and complexity of an information network. We represent consciousness as a graph where nodes represent individual cognitive elements and edges represent the flow of information between them. The complexity of this graph, specifically its interconnectedness and the number of unique pathways, is used as a proxy for the level of consciousness. This model offers a novel approach to studying consciousness by providing a framework for formal analysis and potentially allowing for the prediction of consciousness levels in different systems. The key contribution lies in translating IIT's abstract concepts into a concrete, mathematically tractable representation, paving the way for computational investigation and further theoretical development. We define several key metrics related to graph properties to capture aspects of integrated information, including node degree, clustering coefficient, and path length. The ultimate goal is to develop an algorithm that, given a cognitive system's structure, can estimate its level of consciousness.