Scott de Marchi

Chair and Professor, Dept. of Political Science / Director of Decision Science
Duke University

Professor at Duke, splitting his time between computational modeling, game theory, and statistical methods. His research group predicts things — no toy models.

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Scott de Marchi is Professor of Political Science and Director of the Decision Science program at Duke University. His work has been funded by the Department of Defense, the National Science Foundation, and USAID. His research focuses on mathematical methods, especially bargaining theory, computational social science, machine learning, and mixed methods. Substantively, he examines decision-making in contexts that include the American Congress, coalition and crisis bargaining, interstate conflict, and voting behavior.

At Duke, he is the founder (and sometimes director) of the Modeling Economic and Political Systems Focus program (MESS) and the Decision Science program. He has been an external fellow at the Santa Fe Institute and the National Defense University, a principal investigator for NSF’s EITM program, an associate editor of the American Political Science Review, and a consultant on quantitative modeling for various offices at the Dept of Defense, Dept of State, and USAID.

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Selected Works

Policy and the Structure of Roll Call Voting in the US House — Journal of Public Policy (with Spencer Dorsey and Michael Ensley)

Congressional Bargaining in Presidential Time: Give and Take, Anticipation, and the Constitutional Rationalization of Dead Ducks — Journal of Politics (with Terry Sullivan)

District Complexity as an Advantage in Congressional Elections — American Journal of Political Science (with Michael Ensley and Michael Tofias)

Costly Search and the Advantages of Incumbency: Some Experimental Results — Public Choice (with Michael Ensley and Michael Munger)

Trust, but Verify: Assessing the Accuracy of Self-reported Pollution Data — Journal of Risk and Uncertainty (with James Hamilton)

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Government Formation in the Shadow of an Uncertain Future Election — Edward Elgar Handbook of Coalition Politics (with Michael Laver and Georg Vanberg)

The Governance Cycle in Parliamentary Democracies: A Computational Social Science Approach — Cambridge University Press (Studies in Comparative Politics) (with Michael Laver)

Government Formation as Logrolling in High-Dimensional Issue Spaces — Journal of Politics (with Michael Laver)

Cabinet Formation and Portfolio Distribution in European Multi-party Systems — British Journal of Political Science (with Josh Cutler, Max Gallop, Florian Hollenbach, Michael Laver, and Matthias Orlowski)

Congressional Bargaining in Presidential Time: Give and Take, Anticipation, and the Constitutional Rationalization of Dead Ducks — Journal of Politics (with Terry Sullivan)

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Linking Theory and Empirics: The Importance of Research Communities — The Oxford Handbook of Engaged Methodological Pluralism in Political Science

Prediction and Cumulative Knowledge in the Social Sciences — Cambridge University Press (Strategies for Social Inquiry series) (with Max Gallop, Libby Jenke, and Scott Page)

Rashomon Histories: What the Data Support Is Bigger Than What We Report — Under review (with Alan van Beek and Max Gallop)

Computational and Machine Learning Models: The Necessity of Connecting Theory and Empirics — Sage Handbook of Research Methods in Political Science (with Brandon Stewart)

Computational and Mathematical Modeling in the Social Sciences — Cambridge University Press

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Prediction and Cumulative Knowledge in the Social Sciences — Cambridge University Press (Strategies for Social Inquiry series) (with Max Gallop, Libby Jenke, and Scott Page)

The Governance Cycle in Parliamentary Democracies: A Computational Social Science Approach — Cambridge University Press (Studies in Comparative Politics) (with Michael Laver)

The Complexity of Polarization — Proceedings of the National Academy of Sciences

Computational and Machine Learning Models: The Necessity of Connecting Theory and Empirics — Sage Handbook of Research Methods in Political Science (with Brandon Stewart)

Agent Based Models — Annual Review of Political Science (with Scott Page)

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Rashomon Histories: What the Data Support Is Bigger Than What We Report — Under review (with Alan van Beek and Max Gallop)

Decisive or Distracted: The Effects of United States Constraint on Security Networks — British Journal of Political Science (with Ha Eun Choi, Max Gallop, and Shahryar Minhas)

Nuclear and Conventional Deterrence in Theory and Practice — OSF Preprints (SocArXiv) (with Kathryn Cochran, Max Gallop, and John Nagl)

Untangling Neural Networks — American Political Science Review (with Chris Gelpi and Jeff Grynaviski)

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Rashomon Histories: What the Data Support Is Bigger Than What We Report — Under review (with Alan van Beek and Max Gallop)

Using Large Language Models to Analyze Political Texts Through Natural Language Understanding — American Journal of Political Science (with Kenneth Benoit, Conor Laver, Michael Laver, and Jinshuai Ma)

Computational and Machine Learning Models: The Necessity of Connecting Theory and Empirics — Sage Handbook of Research Methods in Political Science (with Brandon Stewart)

Untangling Neural Networks — American Political Science Review (with Chris Gelpi and Jeff Grynaviski)

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The Complexity of Polarization — Proceedings of the National Academy of Sciences

District Complexity as an Advantage in Congressional Elections — American Journal of Political Science (with Michael Ensley and Michael Tofias)

Costly Search and the Advantages of Incumbency: Some Experimental Results — Public Choice (with Michael Ensley and Michael Munger)

Adaptive Models and Electoral Instability — Journal of Theoretical Politics

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