Deep learning and punctuated equilibrium theory

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). (C) 2017 Elsevier B.V. All rights reserved.