Point Predictions and the Punctuated Equilibrium Theory: A Data Mining Approach-U.S. Nuclear Policy as Proof of Concept

In Punctuated Equilibrium Theory (PET), information processing under the constraints of limited attention and bounded rationality leads to stick-slip dynamics in policy outcomes. Empirical work in this field often focuses on the macro level. Using the case of nuclear energy policy in the United States as proof of concept, we demonstrate how decisive budget changes in a specific policy subsystem can be linked to attention of Congress and the president. We utilize a mixed-methods data-mining approach: Maximum likelihood estimation is used to analyze the distribution of the nuclear energy RD&D budget. Then attention data of both Congress and the president are structured by means of cluster analysis and principal component analysis. Finally, these data are used in a generalized linear model to predict specific budget shifts. The article is designed as a proof of concept: In the case of nuclear energy policy, we are able to predict budget shifts without violating the assumptions of PET. More importantly: we can demonstrate that attention is not only affecting the final policy outcome but also the corridor of the possible.