The Information-forcing Dilemma in Damages Law

نویسنده

  • TUN-JEN CHIANG
چکیده

Courts assessing compensatory damages awards often lack adequate information to determine the value of a victim’s loss. A central reason for this problem, which the literature has thus far overlooked, is that courts face a dilemma when applying their standard information-forcing tools to the context of damages. Specifically, the standard method by which courts obtain information is through a burden of proof. In the context of damages, this means a rule requiring plaintiffs to prove the value of a loss. But courts will often face a situation where a plaintiff can clearly prove the existence of a loss, yet cannot prove the value of the loss with any precision. A court that strictly enforces the burden of proof would award zero damages in such a case, producing a harsh result. But a court that avoids this result by instead awarding its best guess at the correct amount—effectively forgiving the inadequacy of plaintiff’s proof—then undermines future incentives for plaintiffs to produce rigorous evidence. The result of this dilemma is that courts oscillate between strict and forgiving approaches, causing much confusion. Explaining the dilemma helps alleviate the confusion and points to a solution. In principle, courts should require a party to produce more rigorous damages evidence if, and only if, the party is the lower-cost provider of that evidence, and the benefit of having the evidence (in facilitating a more accurate damages award) outweighs the cost of collecting it. The messy legal standards for calculating damages in various * Associate Professor, George Mason University Antonin Scalia Law School. Thanks to Michael B. Abramowicz, Adam Badawi, John M. Golden, Herbert Hovenkamp, Keith N. Hylton, Douglas Laycock, Jonathan S. Masur, Lisa Larrimore Ouellette, Hillary A. Sale, Brian Z. Tamanaha, and participants at the IP Scholars Conference, the Works-in-Progress IP Colloquium, and a workshop at Washington University School of Law for comments.

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تاریخ انتشار 2017