A drama manager (DM) monitors an interactive experience, such as a computer game, and intervenes to shape the global experience so that it satisfies the author's expressive goals without decreasing a player's interactive agency. Most research work on drama management has proposed AI architectures and provided abstract evaluations of their effectiveness. A smaller body of work has evaluated the effect of drama management on player experience, but little attention has been paid to evaluating the authorial leverage provided by a drama management architecture: determining, for a given DM architecture, the additional non-linear story complexity a DM affords over traditional scripting methods. In this paper we propose three criteria for evaluating the authorial leverage of a DM: 1) the script-and-trigger complexity of the DM story policy, 2) the degree of policy change given changes to story elements, and 3) the average story branching factor for DM policies vs. script-and-trigger policies for stories of equivalent quality. We present preliminary work towards applying these metrics to declarative optimization-based drama management, using decision-tree learning to capture the equivalent trigger logic for a DM policy.