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 Stat...

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Publicado en:Policy Studies Journal Vol. 43; no. 2; pp. 228 - 257
Autores principales: Hegelich, Simon, Fraune, Cornelia, Knollmann, David
Formato: Case Study
Publicado: Wiley-Blackwell May2015
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Acceso en línea:Ver este registro en EBSCOhost
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        atl: Point Predictions and the Punctuated Equilibrium Theory: A Data Mining Approach- U. S. Nuclear Policy as Proof of Concept.
      aug:
        au:
          Hegelich, Simon
          Fraune, Cornelia
          Knollmann, David
      su:
        United States
        United States. Congress
        Punctuated equilibrium (Social science)
        Energy Policy Act of 2005
        Nuclear energy policy
        Executive power
      sug:
        subj:
          Punctuated equilibrium (Social science)
          United States
          United States. Congress
          Executive Offices
          Nuclear Electric Power Generation
          Other federal government public administration
          Legislative Bodies
          Energy Policy Act of 2005
          Nuclear energy policy
          Executive power
      keyword:
        data mining
        nuclear energy
        Punctuated Equilibrium Theory
        data mining
        nuclear energy
        Punctuated Equilibrium Theory
      ab: 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.
      pubtype: Academic Journal
      doctype: Case Study
      src: R
    language: English
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