A Machine Learning Theory Perspective on Strategic Litigation

TL;DR

This paper models strategic litigation using machine learning theory, analyzing how litigators can influence legal decision rules and future rulings through case selection and strategic behavior.

Contribution

It introduces an abstract model connecting strategic litigation with machine learning decision rules, providing insights into litigators' influence on legal systems.

Findings

Strategic litigators can significantly influence decision rules.

Case selection critically impacts future rulings.

Sometimes bringing a case with an unfavorable outcome can be strategically beneficial.

Abstract

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?