Optimizing Integrated Information with a Prior Guided Random Search Algorithm
arXiv Preprint Archive December 8, 2022 Eduardo C. Garrido-Merchán, Javier Sánchez-Cañizares
Integrated information theory (IIT) proposes a quantitative measure, Φ, to estimate whether a physical system is conscious, its degree of consciousness, and the complexity of its experienced qualia. The theory models a physical system as a probabilistic causal graph of interconnected elements with input-output functions. This paper presents a random search algorithm that optimizes Φ to investigate how graph structure changes with increasing numbers of nodes to achieve higher Φ. The authors also discuss why more complex black-box search methods like Bayesian optimization or metaheuristics face difficulties for this problem and suggest future research directions to improve the search for maximal Φ.