Past legal language exerts an almost magnetic force on negotiators. From boilerplate treaties or copy-and-paste adaptations to the codification of prior jurisprudence – practitioners constantly recycle already existent terms, phrases, and concepts into new legal outputs. This chapter links the reproduction of legal language to the concept of path-dependency and applies it to international investment agreements. The chapter shows that historical sociology rather than rational design helps to explain the path-dependent style and content of today’s investment regime. Using the fair and equitable treatment clause as a case study, the chapter traces how these clauses first emerged haphazardly in investment law, yet then became entrenched through efficiency considerations, sociological forces, and cognitive biases. The ensuing path-dependency has prevented adaptations of superior treaty design alternatives, and instead geared negotiators into reproducing or refining the fair and equitable treatment standard. Differently put, negotiators have become locked in language. The chapter concludes by outlining ways in which current reform efforts can overcome the system’s path-dependency to allow for innovation inspired not by past practices but by current needs.
When traditional international law research techniques reach their conceptual and methodological limits, international lawyers need to look for help in other disciplines. International law scholars have in the past drawn inspirations from economics, political science or sociology to enrich the study and understanding of international law. Now the time has come to add a new discipline to this list: computer science. The computational analysis of international law renders legal analysis scalable and empowers international lawyers to study international law in unprecedented depth and breadth. In this contribution, I provide an overview of computational techniques for the doctrinal and legal-institutional study of international law highlighting this neglected, but increasingly important field of interdisciplinary study.
Lawyers routinely compare legal texts. Advances in technology now help automate such comparison by treating legal texts as data and quantifying their similarity. This renders legal text comparisons scalable and enables lawyers to investigate similarity patterns in large legal text corpora. In this chapter, I outline the technological underpinnings of automated legal text comparison and discuss the practical limitations, implications and applications arising from its increasing use. Automated text comparisons, amongst others, improve legal search and recommender systems, allow to better track legal processes and can reveal patterns in legal corpora impossible to detect through traditional means. Automated text comparison, however, also suffers from limitations. It focuses on similarity of text and not similarity of meaning and cannot (yet) differentiate between legally significant and legally insignificant text differences. Therefore, automated text comparison is best seen as a complement to, rather than a substitute for, a manual or concept-based comparison of legal texts.