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 $\Phi$.

本文介绍了一个随机搜索算法来优化意识的一个定量测量标准 phi，并给出了一些增强搜索算法的特定研究线路。