  We discuss the feasibility of predicting, managing and subsequently
manipulating, the future evolution of a Complex Adaptive System. Our archetypal
system mimics a population of adaptive, interacting objects, such as those
arising in the domains of human health and biology (e.g. cells), financial
markets (e.g. traders), and mechanical systems (e.g. robots). We show that
short-term prediction yields corridors along which the model system will, with
high probability, evolve. We show how the widths and average direction of these
corridors varies in time as the system passes through regions, or pockets, of
enhanced predictability and/or risk. We then show how small amounts of
'population engineering' can be undertaken in order to steer the system away
from any undesired regimes which have been predicted. Despite the system's many
degrees of freedom and inherent stochasticity, this dynamical, 'soft' control
over future risk requires only minimal knowledge about the underlying
composition of the constituent multi-agent population.
