Abstract: The present invention relates to an artificial neural network (ANN) representation for system dynamics models (SDMs) and its applications in model construction and policy design. It first shows that, by a special design of the mapping scheme, a given flow diagram (FD) (i.e., traditional representation) can be transformed into a corresponding model in the representation of partial recurrent networks (PRNs) that will correctly behave like the one it mimics. The present invention shows the equivalence of the two types of representations, both structurally and mathematically. With the additional representation, an automatic learning method that can assist in the construction of SDMs is proposed, which starts from an initial skeleton of a PRN (mapping from an initial FD), identifies the cause-effect relationships within the SDM by neural learning, and then converts it back to the corresponding FD. The composite approach makes model construction simpler and more systematic.