AI-driven Modeling & Forecasting for Democratic Development and Decline (AIM4D)
International Conference at the University of Notre Dame
October 15-17, 2026
Conveners: Michael Coppedge and Dmitry Zaytsev
Call for Abstracts
A team of researchers at the University of Notre Dame with support from the Kellogg Institute for International Studies and the Franco Family Institute for Liberal Arts and the Public Good is organizing the International Conference on AI-Driven Modeling and Forecasting for Democratic Development and Decline (AIM4D).
The AIM4D Conference explores the fundamental dynamics of democratic development and decline, asking how artificial intelligence (AI) can be used to model, forecast, and explain these processes across political systems and historical contexts. By bringing together scholars from political science, computational social science, and data science, the conference aims to advance a rigorous interdisciplinary dialogue on the mechanisms, trajectories, and early indicators of democratic and authoritarian change. It will focus on developing and comparing interpretable AI-driven models that integrate theoretical insight with empirical data to better understand the causal patterns and temporal dynamics underlying democratic resilience, transformation, and backsliding.
Contemporary studies of democracy often rely on static models and descriptive frameworks that are hard pressed to capture the nonlinear, dynamic, and interdependent nature of democratic systems. The AIM4D Conference convenes leading scholars to explore new methods for modeling and forecasting regime change using interpretable, theory-informed machine learning (ML).
The conference invites submissions that address these themes through innovative methodological and theoretical perspectives. We welcome papers using AI, machine learning, computational, and other advanced approaches to study democratic and autocratic changes. Submissions that integrate large-scale datasets (e.g., V-Dem, Demscore, GDELT) and develop new modeling frameworks, including new measurement and forecasting tools are especially encouraged.
Please submit a 300–500 word abstract by March 1, 2026. Each submission should include: Author name(s) and affiliation(s); Paper title; 300-500 word abstract; Short bio (150–200 words)
Submit abstracts to: Michael Coppedge (mcoppedg@nd.edu) and Dmitry Zaytsev (dzaytsev@nd.edu)
Selected authors will be invited to submit full papers by July 31, 2026. The conference sponsors expect to provide support for travel, lodging, and meals for selected authors during the conference. Papers discussed at the conference will be considered for inclusion in a planned special issue on AI-driven modeling and forecasting for democracy in a highly ranked, peer-reviewed journal.We look forward to your submissions and to an engaging, interdisciplinary discussion at Notre Dame in October 2026.
