I do interdisciplinary research—combining methodologies from law, economics, and statistics to find new ways to approach traditional legal issues. I am particularly interested in using game theory, probability, and controlled experiments to revitalize procedural and doctrinal dialog in areas where standard legal research methods have stalled. My scholarship generally sorts into four broad subject-areas: (1) evidence law and procedure, (2) antitrust and business law, (3) legal decision-making, and (4) other law-and-economics topics. Details on my specific research interests in each area follow.
Evidence Law & Procedure
My primary interest in evidence law is using probability theory and theoretical statistics to unpack and reinterpret difficult evidence problems. For example, my current working paper, A Likelihood Story, tackles a century-old evidence-law puzzle: how to conceptualize the fact-finding process and associated burdens of persuasion. This paper shows that mounting frustration with nearly 50 years of attempts to devise a probability-based theory of fact-finding owes to a mistaken focus on probabilities: we should really be looking at likelihoods. These are fundamentally different concepts. Probabilities measure subjective belief; likelihoods measure evidence. Put another way, a probability approach to fact-finding asks about the probability of random facts given fixed evidence; a likelihood approach asks about the probability of random evidence given different assumptions about the fixed facts. I show that a likelihood approach to fact-finding escapes the paradoxes and unacceptable implications of probability theories. It also better fits the procedure of adversarial litigation. Using the statistical properties of likelihoods, I show that the same simple and intuitive rule of likelihood reasoning describes every burden of persuasion in use today.
Antitrust & Business Law
My interest in antitrust issues stems from the growing role that economics plays in this area of law. But its role could be bigger still. For example, What Structural Presumption?, I show how a seemingly innocuous bit of legal formalism has subtly displaced necessary economic analysis in an important area of merger law. In a similar vein, I am interested in re-examining the old and potentially outdated legal factors thought to represent necessary conditions for competing firms to engage in anticompetitive coordination. I am also interested in finding cross-subject applications for antitrust and business-law principles. In a recent working paper, Powers without Power, I use concepts from both antitrust (collusion) and agency law (principal-agent interaction) to reinterpret and partially defend Supreme Court doctrine on the constitutionality of delegations of legislative powers.
I am interested in the many individual decision-making problems that result from (and comprise) the civil justice system. Much of this work involves negotiation. For example, in Why Wait to Settle?, I conduct a rare empirical test of the popular theory that delays in the settlement of lawsuits could be the result of informational asymmetries between litigants. Using data from an economic experiment in which subjects engage in mock lawsuits for real money, I find that the introduction of informational asymmetries increases settlement delay by as much as 90%. In addition to other work on legal negotiation, I am interested in decision-making problems involving bounded rationality and the weight courts afford to boilerplate contract terms in commercial transactions.
Law & Economics
Finally, I am interested in a variety subjects that might be classified as traditional law-and-economics topics. Like a handbook chapter that I coauthored on criminal justice data, much of this research centers around understanding the limitations of available data, and using appropriate statistical methods to work around these limitations. This empirical focus also informs my approach to law-and-economics theory. Because a model is only as sound as its assumptions—and only as useful as its predictive power—I devote much of my research effort to examining how well models fit and predict observable reality.