Understanding the Law of Evidence Through the Lens of Signal-to-Noise
Benjamin N. Cardozo School of Law
August 25, 2013
Cardozo Legal Studies Faculty Research Paper No. 380
Why set up evidentiary rules rather than allow factfinders to make decisions by considering all relevant evidence? This fundamental question has been the subject of unresolved controversy among scholars and policymakers since it was raised by Bentham at the beginning of the nineteenth century. This Essay offers a surprisingly straightforward answer: An economically minded legal system that processes many cases must suppress all evidence that brings along a negative productivity-expense balance. Failure to suppress inefficient evidence will result in serious diseconomies of scale. To operationalize this idea, I introduce a “signal-to-noise” method borrowed from statistics, science and engineering. This method focuses on the range of probabilities to which evidence falling into a specified category gives rise. Specifically, it compares the average probability associated with the given evidence (the “signal”) with the margins on both sides (the “noise”). This comparison allows policymakers to determine the signal-to-noise ratio (SNR) of evidence. When the evidence’s signal overpowers the noise, the legal system should admit the evidence. Conversely, when the noise emanating from the evidence drowns the signal, the evidence is inefficient and should therefore be excluded. I call this set of rules “the SNR principle.” Descriptively, I demonstrate that this principle best explains the rules of admissibility and corroboration by which our system selects evidence for trials. Prescriptively, I argue that the SNR principle should guide the rules of evidence-selection and determine the scope of criminal defendants’ constitutional right to compulsory process.
Number of Pages in PDF File: 50
Keywords: evidence, exclusionary rules, signal-to-noise, American exceptionalism, economics of information, diseconomies of scale
JEL Classification: D80, D83, K40, K41working papers series
Date posted: January 13, 2013 ; Last revised: September 25, 2013
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