Blind Judgement: Agent-Based Supreme Court Modelling With GPT
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
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.
Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 15 of its 28 references that are in the map. First posted on arXiv 12 Jan 2023.
Builds on
15 of 28 references.
References · 15
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- 2021A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Levelcited by 96
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Citing papers per year: 2023: 14; 2024: 14; 2025: 5; 2026: 4.
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