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Theories of Uncertainty: Explaining the Possible Sources of Error in Inferences

  • Vern R. Walker
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 94)

Abstract

A central task in legal factfinding is evaluating the warrant for a finding or the soundness of an inference from the evidentiary propositions to a conclusion. This task is especially difficult when there is much at stake, but the evidence is incomplete and the soundness of the inference is uncertain. Analyses of how to improve such inferences have been made at various levels of generality, and for different types of evidence. For example, one general problem is distinguishing “scientific knowledge” from “junk science,” as required for admissibility in judicial proceedings under Federal Rule of Evidence 702, following the Daubert v. Merrell Dow Pharmaceuticals, Inc.1 decision.2 Another general problem is evaluating inferences about unique historical events, the kind of factfinding necessary in criminal cases.3 As opposed to such general problems, some theorists address only particular areas where inferences are difficult in law, such as the “lost chance” cases,4 cases involving “indeterminate plaintiffs,”5 inferences from “naked statistical evidence,”6 or inferences based on DNA identification.7 Such problems of correct inference cannot be solved purely by formal logic, nor can theorists merely duplicate the role of the factfinder by evaluating the specific evidence in a particular case. To be useful as theories of inference, accounts can be neither too general nor too specific. They must provide useful models for handling recurring types of inference in situations where findings must be warranted by incomplete evidence.8

Keywords

Supra Note Generic Causation Causal Theory Causal Dimension Sampling Uncertainty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Reference

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    The requirement of a “hedge” within a generalization is not a sufficient answer. A hedge term can indicate the intended quantification for the proposition — for example, that the generalization might be true in “all,” “many,” “forty-two percent,” or “at least one” of the cases. But there can still be uncertainty about whether the generalization (with its intended quantification) is true or false. Uncertainty is about the potential for error, whatever the generalization’s quantification.Google Scholar
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  • Vern R. Walker

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