Strategic litigation involves bringing a case to court with the goal of having an impact beyond resolving the particular dispute at hand. In a common law system, one way a case may have far-reaching impact is by establishing new legal precedent that later courts must follow. In this paper, we explore strategic litigation from the perspective of machine learning theory. We consider an abstract model of a common law legal system where a lower court decides new cases by applying a decision rule learned from a higher court's past rulings. In this model, we explore the power of a strategic litigator, who strategically brings cases to the higher court to influence the decision rule applied by the lower court in future cases. We explore questions including: What impact can a strategic litigator have? Which cases should a strategic litigator bring to court? Does it ever make sense for a strategic litigator to bring a case when they are sure the court will rule against them? We show that this strategic case selection problem has interesting structure, with even simple settings exhibiting counterintuitive phenomena. When cases are represented by points in one dimension and the lower court's learning algorithm is nearest neighbor, or as points in d dimensions and the lower court's learning algorithm is a support vector machine, we characterize the set of inducible decision rules and develop algorithms for selecting an optimal set of cases to bring to the higher court given the strategic litigator's objectives.

翻译：战略诉讼是指通过提起诉讼实现超越具体个案争议更广泛影响力的目标。在普通法体系中，案件产生深远影响的方式之一，是确立后续法院必须遵循的新司法先例。本文从机器学习理论视角探讨战略诉讼问题。我们构建了一个普通法法律体系的抽象模型，其中下级法院通过应用从上级法院过往裁决中习得的决策规则来审理新案件。在该模型中，我们探究战略诉讼人的影响力——即其有策略地向上级法院提交案件，以影响下级法院在未来案件中适用的决策规则。我们研究的核心问题包括：战略诉讼人能产生何种影响？应选择哪些案件提交至上级法院？在确信法院会作出不利裁决时，战略诉讼人是否仍有必要提起诉讼？研究表明，该战略性案件选择问题具有独特结构，即便在简单场景下也会呈现反直觉现象。当案件由一维空间中的点表示且下级法院的学习算法为最近邻法时，或由d维空间中的点表示且下级法院采用支持向量机作为学习算法时，我们刻画了可诱导决策规则集合的特征，并基于战略诉讼人的目标函数，开发出用于选择最优案件组合提交至上级法院的算法。