  Over the last decade new technologies for making large numbers of
fine-grained measurements have led to the surprising discovery that many
biological systems sit near a critical point. These systems are potentially
more adaptive in that small changes to component behavior can induce
large-scale changes in aggregate structure and function. Accounting for
criticality remains a challenge as sensitivity to perturbation suggests a lack
of robustness. Furthermore, change induced by perturbation may not be adaptive.
Complicating matters further critical phenomena can result from
history-dependent stochastic processes. A question central to distinguishing
among these conflicting views of criticality is to what degree criticality can
be controlled by the components of the system. We address the control of
criticality using data on conflict dynamics and fight sizes from an animal
society model system (Macaca nemestrina, n=48). The system is fundamentally
finite so we operationalize criticality in information theoretic terms using
Fisher information and a measure of instability. We analyze criticality using
empirically-grounded equilibrium (maximum entropy) and dynamic (branching
process) models of the monkeys' fight-joining behavior. We find that (1) this
heterogeneous, socially organized system, like homogeneous, spatial systems
(flocks and schools), sits near a critical point, (2) the contributions
individuals make to how critical the system is can be quantified and vary, and
(3) the distance from the critical point (DFC) can be controlled through
biologically plausible mechanisms operating on this heterogeneity. These
mechanisms include third-party policing, which dampens fight participation of
the individuals with the largest effect on DFC. Control of DFC allows
biological systems to balance the tradeoff between robustness and need for
rapid change.
