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A Likelihood Story: The Theory of Legal Fact-Finding

Authors: Sean Patrick Sullivan;

A Likelihood Story: The Theory of Legal Fact-Finding

Abstract

For over 50 years, courts and scholars have tried to conceptualize fact-finding, and burdens of persuasion, in terms of the probability of facts given the evidence. The exercise has not produced a satisfying theory of fact-finding. The problem is reliance on probability. Fact-finding is not about probability. It’s about likelihood. The difference between these concepts is substantial. Where probability theories of fact-finding ask about the probability of the facts given the evidence, the proposed likelihood approach asks about the probability of the evidence given different assumptions about the facts. Where probability theories measure subjective beliefs, the likelihood approach measures the relative weight of evidence alone. Using the statistical properties of likelihoods, I show that every burden of persuasion in use today can be reduced to the same simple rule of likelihood reasoning. This likelihood theory of fact-finding closely mirrors the procedure of adversarial litigation, and solves all of the paradoxes, difficulties, and unacceptable implications that have long frustrated probability theories of fact-finding.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
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