Correlation as a measure for fitness in multi-agent learning systems

In this work we explore the behaviour of steady state trajectories through covariance matrix (Grammian or inverse Dirichlet matrix) obtained in a set of networks to analyze how those more correlated agents could show some cues about the environment and how this information could improve the fitness in an evolving environment.