Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4942386 | Cognitive Systems Research | 2017 | 11 Pages |
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).
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Simon Hegelich,