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Novel Applications of Machine Learning
Sat, September 7, 10:00 to 11:30am, Pennsylvania Convention Center (PCC), 106BSession Submission Type: Created Panel
Session Description
This panel offers a collection of innovative applications of machine-learning techniques (such as LLMs, explainable AI, and tree-based models) to practical challeges in our discipline --- including missingness with complex data objects, record linkage, simulation of experimental outcomes, and measurement in data-scarce authoritarian regimes. Each approach demonstrates a push towards more sophisticated, data-driven analysis techniques that are adaptable, faster, and provide deeper insights into complex datasets.
Sub Unit
Individual Presentations
Probabilistic Record Linkage Using Pre-trained Text Embeddings - Joseph T. Ornstein, University of Georgia
Beyond Human Subjects: LLMs as Participants in Political Experimentation - Selim Yaman, American University
A Measure of Nationalization: Subpresidential Elections Are Not Created Equal - Francesca Tang, Princeton University
Explainable Non-linear Models for Predicting Political Instability - Budrul Ahsan, Philips Japan; Sota Kato, The Tokyo Foundation for Policy Research; Iku Yoshimoto, Univresity of Tokyo; HIROSHI OKAMOTO, The University of Tokyo; Takafumi Nakanishi, Musashino University
Measuring Political Uncertainty in Autocracies Using Evidence from China - Tamar Zeilberger, London School of Economics