Bargaining in the Shadow of Big Data

Abstract

Attorney bargaining has traditionally taken place in the shadow of trial, as litigants alter their pretrial behavior—including their willingness to negotiate a settlement — based on their forecast of the outcome at trial and associated costs. Lawyers bargaining in the shadow of trial have traditionally relied on their knowledge of precedent, intuition, and previous interactions with the presiding judge and opposing counsel to forecast trial outcomes and litigation costs. Today, however, technology for leveraging legal data is moving the practice of law into the shadow of the trends and patterns observable in aggregated litigation data. In this Article, we describe the tools that are facilitating this paradigm shift, and examine how lawyers are using them to forecast litigation outcomes and reduce Coasean bargaining costs, in both litigation and transactional scenarios. We also explore some of the risks associated with bargaining in the shadow of big data and offer guidance to lawyers for leveraging these tools to improve their practice.

Our discussion pushes beyond the cartoonish image of big data as a mechanical fortuneteller that predicts who will win or lose a case, supposedly eliminating research or deliberation. We also debunk the alarmist clichés about newfangled technologies eliminating jobs. Demand for lawyers capable of effectively bargaining in the shadow of big data will continue to increase, as the legal profession catches up to the data-centric approach found in other industries. Ultimately, this Article paints a portrait of what big data really means for practicing attorneys, and provides a framework for exploring the theoretical implications of lawyering in the era of information analytics.

Keywords: law, data, analytics, technology, prediction, firm, counsel, courts, judges, machine learning, legal, practice, lawyer, attorney, software, bargaining, litigation, trial, automation, PACER, Coase theorem, black swan, Justice Oliver Wendell Holmes, future, innovation, Moneyball, legal research

JEL Classification: K00, K40, K41, K49, O30, O31, O33, C70, C80, C82, C87, C88, J44