Evaluating system dynamics models of risky projects using decision trees: alternative energy projects as an illustrative example

Many important risky projects are characterized by stochastic processes embedded in nonlinear, feedback structures with delays. System dynamics models may be used to estimate the cash flow resulting from these projects for any given predetermined sequence of decisions. However, using system dynamics models to evaluate these cash flows when management has the flexibility to change its decisions in the face of future information often remains difficult in practice. To remedy this, we leverage the decision analysis literature to propose a methodology that transforms the system dynamics model into an approximate decision tree. The methodology then evaluates that tree to account for the value of managerial flexibility in executing a project. This approach has the virtue of combining system dynamics' capability to cope with dynamic complexity with that of decision analysis to model managerial flexibility. We illustrate this method with a model drawn from the wind power industry, which is characterized by numerous uncertainties and high managerial flexibility. We conclude with a discussion describing the limitations of this approach and the conditions under which its use would be most appropriate.