The Flawed Probabilistic Foundation of Law & Economics

Abstract:     
This Article challenges the mathematical probability system that underlies law and economics and behavioral analysis and argues that many of the core insights of both approaches are irremediably flawed. The Article demonstrates that mathematical probability is only suitable for pure gambles and hence does not provide a useful epistemic tool for analyzing individual decisionmaking. As a result, mathematical probability cannot serve as a useful tool for lawmakers. Mathematical probability, the Article proposes, ought to be replaced with causative probability - a system of reasoning compatible with the causal structure of people’s physical, social and legal environments. Originating from the writings of John Stuart Mill and Francis Bacon, causative probability differs from its mathematical cousin both conceptually and substantively. By contrast to the mathematical system that bases probability estimates on abstract averages, the causative system bases probability estimates upon case-specific evidential variety. Under the causative system, the probability that a person’s action will bring about a particular consequence - gain or loss - is determined by the number and scope of the consequence’s evidential confirmations in the individual case, and not by general averages that are usually irrelevant to the individual determination at hand. Causative probability allows a person to develop a better epistemic grasp of her individual case relative to what she could achieve under the mathematical system. This epistemological advantage turns causative probability into a superior tool for understanding how legal mechanisms work, for improving those mechanisms, and for defining the rationality of individuals’ decisions.

Number of Pages in PDF File: 61

Keywords: probability, economic analysis of law, behavioral economics, mathematical probability, causative probability, Bacon, Mill, incentives, law-enforcement

JEL Classification: A12, K00, K12, K13, K40, K42

Accepted Paper Series