A General Theory of Evidence and Proof

This book reframes the fundamentals of decisionmaking under uncertainty. For almost a century, theorists have spoken of truth-finding in terms of probability. They have said things like some past fact was 51% certain or proclaimed that in a civil dispute a fact must be shown to exceed a 50% likelihood. But such talk is a misleading misconception. The reason is that traditional probability fails to distinguish epistemic uncertainty from aleatory uncertainty. This conflation leads to mistakes such as invoking probability's product rules, which calculate a conjunction's likelihood as being low. From there, the theorists have argued that in a myriad of ways, the law violates the probability calculus unforgivably.

Today, other theorists are newly realizing that in large part the law does not deal in probability. They now can defend the way that law has found facts since long before the invention of probability and on to the present. They are also reevaluating such intuitive practices as those that humans use in daily life to combine inferences upon inferences. A hotly contested literature has emerged.

In a significant, comprehensive, and original contribution, this book develops a theoretical justification for the intuitive approaches that humans deploy across a broad range of decisionmaking. Instead of probability, the book focuses on degrees of belief that estimate, given the state of the evidence, how far a proposition has been fully proven. Instead of combining findings by the rules of probability, the book uses the rules of multivalent logic. The aim is to illuminate decisionmaking outside statistical analysis, showing that our ancient wisdom is in fact theoretically solid. The target is everyone interested in improving decisionmaking.

Tells how to conceive of and account for the various kinds of uncertainty encountered in the process of evidential proof Uses psychology to explore how humans settle on truth and uses logic to establish how humans should settle on truth Deconstructs legal factfinding as the prime example, but applies the analysis to all decisionmaking under uncertainty

Auteur

Prof. Kevin M. Clermont is the Ziff Professor of Law at Cornell Law School. A graduate of Princeton University and Harvard Law School, he also attended law school in France as a Fulbright Scholar. Next there was a judicial clerkship and practice on Wall Street, before entry into teaching at Cornell University. He was awarded the French Legion of Honor in 2008. His field is civil procedure, with a specialty in the theory of proof. Professor Clermont has published extensively, including coauthoring books both on introduction to law and on civil procedure, Law for Society: Nature, Functions, and Limits (2010) and Materials for a Basic Course in Civil Procedure (14th ed. 2023), as well as sole authoring and editing many other books on civil procedure. On proof, he has written the book Standards of Decision in Law: Psychological and Logical Bases for the Standard of Proof, Here and Abroad (2013) and nineteen journal articles over a period of thirty years.

Contenu

Introduction.- Kinds of Uncertainty.- Bivalent Probabilities.- Multivalent Beliefs.- Factfinding Deconstructed Logically.- Generalizing Beyond Factfinding.- Conclusion.