Evolution Management in a Complex Adaptive System: Engineering the   Future
  We examine the feasibility of predicting and subsequently managing the future evolution of a Complex Adaptive System. Our archetypal system mimics a competitive population of mechanical, biological, informational or human objects. We show that short-term prediction yields corridors along which the system will, with very high probability, evolve. 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 population's composition. 
David M.D. Smith and Neil F. Johnson (Oxford University, UK)