Optimizing Integrated Information with a Prior Guided Random Search Algorithm

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.

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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 $\Phi$.