When cognitive actors connect through the network interface, they form systems that exhibit emergent properties beyond any individual’s contribution. These are complex adaptive systems—large numbers of interacting agents that adapt and learn, generating coherent behavior through distributed control rather than central direction. Such systems exhibit sensitive dependence on initial conditions: deterministic dynamics can produce fundamentally unpredictable outcomes. Yet they are not beyond understanding. Control theory extended to complex networks identifies which nodes must be influenced to steer collective behavior—applying concepts of controllability and observability to systems once thought ungovernable. The network’s value lies not in simple aggregation but in the dynamic interactions that generate insights no single node could produce.