标题： 从机器学习理论视角看战略性诉讼 显示英文标题

标题： A Machine Learning Theory Perspective on Strategic Litigation

摘要： 战略诉讼是指提起一项法律诉讼以期产生超出解决案件本身之外的更广泛影响：例如，创造一种先例，从而影响未来的裁决。在本文中，我们从机器学习理论的角度探讨了战略诉讼。我们考虑了一个普通法司法系统的抽象模型，在这个模型中，下级法院通过应用从上级法院过去裁决中学到的决策规则来决定新案件。在这个模型中，我们探索了战略诉讼者的影响力，即他们战略性地将案件提交给上级法院以影响所学的决策规则，从而影响未来的案件。我们探讨的问题包括： 战略诉讼者可能产生什么影响？ 战略诉讼者应该将哪些案件提交法庭？ 当战略诉讼者确信法庭会对其不利时，提交案件是否合理？ 显示英文摘要

摘要： Strategic litigation involves bringing a legal case to court with the goal of having a broader impact beyond resolving the case itself: for example, creating precedent which will influence future rulings. 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 learned decision rule, thereby affecting 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?