Using white-box nonlinear optimization methods in system dynamics policy improvement
We present a new strategy for the direct optimization of the values of policy functions. This approach is particularly well suited to model actors with a global perspective on the system and relies heavily on modern mathematical white-box optimization methods. We demonstrate our strategy on two classical models: market growth and World. Each model is ﬁrst transformed into an optimization problem by deﬁning how the actor can inﬂuence the models’ dynamics and by choosing objective functions to measure improvements. To improve comparability between different runs, we also introduce a comparison measure for possible interventions. We solve the optimization problems, discuss the resulting policies and compare them to the existing results from the literature. In particular, we present a run of the World2 model which signiﬁcantly improves the published “towards a global equilibrium” run with equal cost of intervention.