Deep learning and punctuated equilibrium theory |
| |
| Affiliation: | 1. School of Psychology, University of Aberdeen, United Kingdom;2. Neurology, University of Erlangen-Nuremberg, Germany;1. College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, 22 Jinjing Road, Tianjin 300384, China;2. Department of Animal and Poultry Science, College of Agriculture and Bioresources, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada;1. Future Convergence Research Division, Korea Institute of Science and Technology, Seoul, Korea;2. Department of Biomolecular Science, University of Science and Technology, Daejeon, Korea;3. Department of Dermatology, Kyung Hee University, Seoul, Korea |
| |
| Abstract: | Deep learning is associated with the latest success stories in AI. In particular, deep neural networks are applied in increasingly different fields to model complex processes. Interestingly, the underlying algorithm of backpropagation was originally designed for political science models. The theoretical foundations of this approach are very similar to the concept of Punctuated Equilibrium Theory (PET). The article discusses the concept of deep learning and shows parallels to PET. A showcase model demonstrates how deep learning can be used to provide a missing link in the study of the policy process: the connection between attention in the political system (as inputs) and budget shifts (as outputs). |
| |
| Keywords: | Deep learning Neural networks Punctuated equilibrium Policy process Backpropagation |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
