DYNAMIC MODELING OF POLICY NETWORKS IN AMSTERDAM

This article evaluates two competing dynamic policy-network models and one static network model by applying them to local politics in Amster dam. In the dynamic models an influence relation results from the acce ptance of an influence request. The first model, Control Maximization, represents the view that politics are primarily power driven, and the second, Policy Maximization, policy driven. Zn the static model (the Two-Stage), network relations are empirically investigated as in other policy-network models and used as a benchmark for evaluating the dyna mic models. Policy Maximization is shown to be the most accurate predi ctor of decision outcomes, better even than the static model, and to g enerate richer networks. However, both dynamic models generate network s that are too hierarchical.