Mass Torts and Rational (Bayesian) Optimism
Vanderbilt University, Nashville TN
Investigators
Abstract
Population-based evidence is used when knowledge of how exposure to a product or behavior causes harm is unavailable (e.g., the mechanism of action of many therapeutic drugs is not precisely-understood), resulting in reliance on epidemiological studies. This type of evidence is particularly relevant to mass torts, wherein the use of a mass-marketed product (such as a drug) generates harm in a fraction of users, resulting in a population whose experience may allow inference of defendant liability. Holding all else equal, the PIs have shown that reliance on population-based evidence results in a higher expected joint payoff to the plaintiff's side, a higher settlement demand, and a larger set of lawsuits (e.g., cases with lower damages) being filed, though not necessarily a higher overall likelihood of trial. The proposed research uses information economics and game theory to address two important issues in law and economics. First, the PIs construct a dynamic model of class-action formation, which accounts for the plaintiff's initial rational optimism and subsequent waves of filings, as more plaintiffs observe the public signal provided by previous filings. At the broadest level, a better theory of the process of class-formation may ultimately identify ways to improve how the legal system compensates victims and provides incentives for defendant precaution in the mass-tort setting. Second, users of the product who are not harmed will rationally have a downward-revised estimate of the fraction of users harmed. Although these assessments are not relevant to filing suits (since they were not harmed), users of mass-marketed products may appear as jurors in a trial. The second project will model the behavior of a jury composed of non-users and unharmed users hearing such a case, incorporating the litigation strategies of the parties as they try to respond to the likely composition of the jury.
View original record on NSF Award Search →