  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 real-world 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.
