Suchergebnisse

This article evaluates two competing dynamic policy-network models and one static network model by applying them to local politics in Amsterdam. In the dynamic models an influence relation results from the acceptance 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. In 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 dynamic models. Policy Maximization is shown to be the most accurate predictor of decision outcomes, better even than the static model, and to generate richer networks. However, both dynamic models generate networks that are too hierarchical.

This article evaluates two competing dynamic policy-network models and one static network model by applying them to local politics in Amsterdam. In the dynamic models an influence relation results from the acceptance 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 dynamic models. Policy Maximization is shown to be the most accurate predictor of decision outcomes, better even than the static model, and to generate richer networks. However, both dynamic models generate networks that are too hierarchical.