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.
翻译:我们提出了一个新型的基于变换器的多试剂系统,用于模拟2010-2016年美国最高法院的司法裁决。我们用每个活跃的最高法院2015年的各自作者意见来培训9个不同的模型,并测试由此形成的系统对96个真实世界案件进行测试。我们发现我们的系统对真实世界最高法院的裁决作出比随机更准确的预测。我们进一步发现个人司法的模型准确性与法律保守主义与自由主义之间的一致之间有着相关性。我们的方法和结果对有兴趣使用语言模型模拟多个代理之间政治压力对话的研究人员具有重要意义。