CITP Luncheon Speaker Series:
Jason Anastasopoulos – Understanding Delegation in the European Union Through Machine Learning

CITP Luncheon Series

Date: Tuesday, October 3, 2017
Time: 12:30 p.m.
Location: 306 Sherrerd Hall
Streaming Live: https://www.youtube.com/user/citpprinceton
Hashtag: #citptalk

No RSVP required for current Princeton faculty, staff, and students. Open to members of the public by invitation only. Please contact Jean Butcher at if you are interested in attending a particular lunch.

The delegation of powers by legislators is essential to the functioning of modern government, and presents an interesting tradeoff in multi-level states such as the European Union (EU). More authority for member states mitigates ideological drift by the European Commission, but less authority reduces the credibility of commitments to centralized policies. Extant empirical studies of this problem have relied on labor-intensive content analysis that ultimately restricts our knowledge of how delegation responded to legislative and executive power changes in recent years. We present a machine-learning approach to replicating the content analysis of 158 laws between 1958-2000 by Franchino (2001,2007) that trains classifiers to examine EU laws enacted since 2000 in a similar way. Using the trained classifier with the highest overall performance, we introduce probabilistic delegation ratios (PDR) as an alternative to the delegation ratio first introduced by Epstein and O’Halloran (1999) and also demonstrate that our trained classifier is able to automatically estimate delegation ratios in legislation as well. While our principal interest is in the European Union, the method we employ can be used to understand delegation in a variety of contexts.


Jason Anastasopoulos is CITP’s Microsoft Visiting Professor of Information Technology Policy. Jason’s research focuses on political economy, political institutions, and political methodology. His current research focuses on understanding when, why and how centralized governing bodies delegate policy-making authority. Other research focuses on the interplay between legislative accomplishment and conflicts between interest groups and constituency pressures in the United States.

While at Princeton, Jason plans to study the political economy of cryptocurrencies such as Bitcoin and their potential impacts on political institutions and monetary policy.

Jason’s research interests in political methodology include machine learning methods for text and image analysis with a special interest Bayesian inference for causal inference, deep learning, network analysis and the emerging field of algorithmic game theory. Projects in political methodology include the development of causal inference methodologies for time series data using deep learning methods such as Long Short Term Memory (LSTMs) neural networks and General Adversarial Networks (GANs).