The collective statistics of voting on judicial courts present hints about their inner workings. Many approaches for studying these statistics, however, assume that judges’ decisions are conditionally independent: a judge reaches a decision based on the case at hand and his or her personal views. In reality, judges interact. We develop a minimal model that accounts for judge bias, depending on the context of the case, and peer interaction. We apply the model to voting data from the US Supreme Court. We find strong evidence that interaction is an important factor across natural courts from 1946 to 2021. We also find that, after accounting for interaction, the recovered biases differ from highly cited ideological scores. Our method exemplifies how physics and complexity-inspired modelling can drive the development of theoretical models and improved measures for political voting.

This article is part of the theme issue ‘A complexity science approach to law and governance’.

司法法院投票的集体统计数据揭示了其内部运作的线索。然而，许多研究这些统计数据的方法都假设法官的决定是有条件独立的：法官根据当前案件及其个人观点做出决定。事实上，法官是互动的。我们开发了一个最小模型，根据案件的背景和同行互动来解释法官的偏见。我们将该模型应用于美国最高法院的投票数据。我们发现强有力的证据表明，从 1946 年到 2021 年，互动是自然法庭的一个重要因素。我们还发现，在考虑互动后，恢复的偏见与被高度引用的意识形态分数不同。我们的方法举例说明了物理和复杂性启发的建模如何推动理论模型的发展和政治投票改进措施的发展。

本文是主题“法律和治理的复杂性科学方法”的一部分。