We present a novel Transformer-based multi-agent system for simulating the judicial rulings of the 2010-2016 Supreme Court of the United States. We train nine separate models with the respective authored opinions of each supreme justice active ca. 2015 and test the resulting system on 96 real-world cases. We find our system predicts the decisions of the realworld Supreme Court with better-than-random accuracy. We further find a correlation between model accuracy with respect to individual justices and their alignment between legal conservatism & liberalism. Our methods and results hold significance for researchers interested in using language models to simulate politically-charged discourse between multiple agents.

This dataset provides detailed metadata on ca. 10.2 million works of fiction and nonfiction written after 1799 in 521 different languages available in the HathiTrust Digital Library. The dataset bolsters the May 2022 Hathifile by supplying missing predicted fiction tags with a bespoke BERT-based multilingual classifier. Our classifier completes the catalogue with an additional 400,000 non-English volumes predicted to be works of fiction, capturing 95% of all works presently provided by HathiTrust. We provide each work with metadata including the work's genre at the level of fiction or nonfiction, length in pages, original language, and the year the work was published. With a total page count of ca. 1.4 billion pages, our dataset provides researchers with a substantial source of non-English modern literature. We also present insight into how multilingual classifiers can be trained with monolingual data, itself a discovery with implications for the study of lower resource languages. We hope our provisions will accelerate empirical research into non-English prose and literature.

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