A Bayesian Model of the Litigation Game

TL;DR

This paper introduces a formal Bayesian model to analyze the probability of a defendant being found liable in civil and criminal trials, offering a mathematical perspective on legal decision-making.

Contribution

It presents the first non-normative, probabilistic framework for understanding litigation outcomes based on Bayesian inference.

Findings

Provides a formal Bayesian model of the litigation process

Analyzes how evidence influences the probability of liability

Offers insights into legal decision-making under uncertainty

Abstract

Over a century ago, Oliver Wendell Holmes invited scholars to look at the law through the lens of probability theory: "The prophecies of what the courts will do in fact, and nothing more pretentious, are what I mean by the law." Yet few legal scholars have taken up this intriguing invitation. As such, in place of previous approaches to the study of law, this paper presents a non-normative, mathematical approach to law and the legal process. Specifically, we present a formal Bayesian model of civil and criminal litigation, or what we refer to as the litigation game; that is, instead of focusing on the rules of civil or criminal procedure or substantive legal doctrine, we ask and attempt to answer a mathematical question: what is the posterior probability that a defendant in a civil or criminal trial will be found liable, given that the defendant has, in fact, committed a wrongful act?