Designing a rational process for risk-taking

In this paper, we attempt to invent a new way to understand risk, measure it, and weigh its consequences. We attempt to design a rational process for risk-taking; a process that gives the system dynamicist the ability to define what may happen in the future and then to choose among alternatives. The two conventional ways to use optimization in system dynamics models are historical data fitting and optimal policy design. In this paper, we propose another use of system dynamics optimization, which is finding the most robust policy under variation (uncertainty) in the underlying model risk-factors. To our knowledge, this usage can be considered a new contribution in the field of system dynamics. Policy robustness is a vital issue that has gained a lot of attention and concern in the system dynamics field. Yet, there is no automatic way to find the most (optimal or near optimal) robust policy. The Complexity of the Manager’s Task As Jay Forrester (Forrester, 1961) puts it "The manger’s task is far more difficult and challenging than the normal tasks of the mathematicians, the physicist or the engineer. In management, many more significant factors must be taken into account. The interrelationships of the factors are more complex. The systems are of greater scope. The nonlinear relationships that control the course of events are more significant. Change is more the essence of the manger’s environment". Everyday, the manager must take multiple decisions that can be very crucial to his business or organization. Profiting or losing can rely on making the right decision or