Optimizing Integrated Information with a Prior Guided Random Search Algorithm

.

ntegrated 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, 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, Φ, 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 Φ in
order to investigate, as the number of nodes increases, the structure of the graphs that have higher
Φ. 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