US最高法院的统计力学
Statistical mechanics of the US Supreme Court
摘要：
我们建立一个简单的模型，在一个组中的投票模式的分布，使用美国最高法院作为一个例子。结构最少或熵最大的模型与观察到的法官投票之间的成对相关性是一致的，相当于伊辛自旋玻璃。虽然所有的相关性（也许令人惊讶）都是正的，但自旋玻璃模型中有效的成对相互作用具有两种符号，恢复了我们的一些直觉，即意识形态光谱中对立双方的法官应该对彼此产生负面影响。尽管存在相互竞争的相互作用，但模型中出现了强烈的平行性趋势，这与数据在数量上一致。该模型表明，投票模式是在一个相对简单的“能量景观”中组织起来的，正确地预测了每个大法官与多数人的相关程度，并为我们提供了一个衡量大法官相互影响的标准。这些结果表明，基于统计物理学的简单模型可以定量地捕捉集体决策的基本特征，即使在复杂的政治背景下。
英文摘要：
We build simple models for the distribution of voting patterns in a group, using the Supreme Court of the United States as an example. The least structured, or maximum entropy, model that is consistent with the observed pairwise correlations among justices' votes is equivalent to an Ising spin glass. While all correlations (perhaps surprisingly) are positive, the effective pairwise interactions in the spin glass model have both signs, recovering some of our intuition that justices on opposite sides of the ideological spectrum should have a negative influence on one another. Despite the competing interactions, a strong tendency toward unanimity emerges from the model, and this agrees quantitatively with the data. The model shows that voting patterns are organized in a relatively simple "energy landscape," correctly predicts the extent to which each justice is correlated with the majority, and gives us a measure of the influence that justices exert on one another. These results suggest that simple models, grounded in statistical physics, can capture essential features of collective decision making quantitatively, even in a complex political context.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
