Abstract
Game theoretic models of policy making have received much attention in political research and have strongly influenced our understanding of how political decisions and institutions work. These models often include complex arguments on the interaction of political actors and institutional rules that are difficult to test directly. Insights into the explanatory power of these arguments are therefore often limited. In this chapter I discuss whether the turn to stochastic game–theoretic modelling that has been advanced in the past decade can provide more comprehensive information on the explanatory power of policy making theories in empirical applications and key characteristics of the policy making process such as the impact of veto, amendment and agenda-setting rights. I develop a stochastic version of the Baron and Ferejohn/Banks and Duggan bargaining model that provides an extraordinarily parsimonious account of how these different rights interact and affect outcomes in legislative negotiations that has been very influential in legislative research. I then evaluate in a series of Monte-Carlo simulations to what extent this model can correctly identify whether these rights and interests work in the direction theoretically assumed by the model, respectively, in what respect they differ from the theoretical assumptions. The results show that the stochastical version of bargaining model gives us quite precise information to what extent the policy making theory fits the data in general and even what theoretical arguments are violated. However, if serious errors occur in the final stages of decision making, arguments on earlier stages are sometimes difficult to evaluate due to their dependence on later events in the strategic model which may sometimes limit the inferences that can be drawn.
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Notes
- 1.
A workaround proposed in some studies is to transform the distance measure and calculate more sophisticated criteria to evaluate model predictions based on the distance between observed and predicted outcomes (for an overview, see Achen 2006a). These include quasi hit rates that report how often a prediction come “close” to the observation measured by its distance, the calculation of pseudo R 2 that is also based on the aggregation of distances between observed and predicted outcomes, and calculations of correlations between predictions and outcomes. But it is not clear how these transformations can improve the empirical assessment of the models substantially if the transformed measure is already flawed. Other known limitations of the distance indicator of empirical model performance include a bias in favor of models that predict outcomes close to the mean of the particular scale employed to measure actor positions de Mesquita (2004); Junge and König (2007, 134)
- 2.
Because there are comparatively few formal restrictions on amendments the set of stable outcomes is comparatively small in this model. I employ a discount factor of 0.2 for all players for the purpose of demonstration to compensate for this effect and increase stability in the predictions.
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Junge, D. (2011). Game Theoretic Models and the Empirical Analysis of EU Policy Making: Strategic Interaction, Collective Decisions, and Statistical Inference. In: König, T., Debus, M., Tsebelis, G. (eds) Reform Processes and Policy Change. Studies in Public Choice, vol 16. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5809-9_12
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