Optimizing Integrated Information with a Prior Guided Random Search Algorithm

TL;DR

This paper introduces a random search algorithm to optimize the integrated information measure, Φ, in network models of consciousness, highlighting challenges and potential improvements for complex black-box optimization methods.

Contribution

The paper presents a novel random search approach for maximizing Φ in IIT networks and discusses the limitations of advanced black-box optimization techniques in this context.

Findings

The random search effectively finds high-Φ graph structures as nodes increase.

Complex black-box algorithms face difficulties due to the problem's nature.

Suggestions are provided for enhancing optimization techniques for IIT models.

Abstract

Integrated information theory (IIT) is a theoretical framework that provides a quantitative measure to estimate when a physical system is conscious, its degree of consciousness, and the complexity of the qualia space that the system is experiencing. Formally, IIT rests on the assumption that if a surrogate physical system can fully embed the phenomenological properties of consciousness, then the system properties must be constrained by the properties of the qualia being experienced. Following this assumption, IIT represents the physical system as a network of interconnected elements that can be thought of as a probabilistic causal graph, $\mathcal{G}$, where each node has an input-output function and all the graph is encoded in a transition probability matrix. Consequently, IIT's quantitative measure of consciousness, $\Phi$, is computed with respect to the transition probability matrix and the present state of the graph. In this paper, we provide a random search algorithm that is able to optimize $\Phi$ in order to investigate, as the number of nodes increases, the structure of the graphs that have higher $\Phi$. We also provide arguments that show the difficulties of applying more complex black-box search algorithms, such as Bayesian optimization or metaheuristics, in this particular problem. Additionally, we suggest specific research lines for these techniques to enhance the search algorithm that guarantees maximal $\Phi$.