Emergent of Criticality in Living Systems

The hypothesis that complex interacting living systems can benefit from operating at the vicinity of critical points has gained momentum in recent years. Criticality may confer an optimal balance between too ordered and exceedingly noisy states. In this thesis we will study a model, based on information theory and statistical mechanics, illustrating how and why a community of agents aimed at understanding and communicating with each other converges to a globally coherent state in which all individuals are close to an internal critical state, i.e. at the borderline between order and disorder. We will study both analytically and computationally the circumstances under which criticality is the best possible outcome of the dynamical process, and eventually confirming the convergence to critical points under very generic conditions. Furthermore the effect of evolutionary strategy will be investigated together with the role of different time scales in evolution and adaptation.