Empirical evidence has proliferated that living systems might operate in
the vicinity of critical points, at the borderline between order and disorder, with
examples ranging from spontaneous brain activity to flock dynamics. A central
unresolved issue is to understand how and why interacting living systems dynamically
tune themselves to be poised in the vicinity of a critical point. In this talk we
will present an approach based on statistical mechanics and information theory to
show that complex
adaptive or evolutionary systems can be much more efficient in coping with diverse
heterogeneous environmental conditions when operating at criticality. Analytical as
well as computational evolutionary and adaptive models vividly illustrate that a
community of such systems dynamically self-tunes close to a critical state as the
complexity of the environment increases while they remain non-critical for simple
and predictable environments. A more robust convergence to criticality emerges in
co-evolutionary and co-adaptive set-ups in which individuals aim to represent other
agents in the community with fidelity, thereby creating a collective critical
ensemble and providing the best possible trade-off between accuracy and flexibility.
Our approach provides a parsimonious and general mechanism for the emergence of
critical-like behavior in living systems needing to cope with complex environments
or trying to efficiently coordinate themselves as an ensemble.