Title

Aggregating Probabilities Across Offences in Criminal Law

Comments

Alon Harel and Ariel Porat, Aggregating Probabilities Across Offences in Criminal Law

Abstract

A defendant is charged with four unrelated offenses, allegedly committed at four different times and in four different places. The probability that he committed each one of the offenses is .9. Assume that the minimum threshold required for conviction is .95. Under prevailing evidence law, the defendant would be acquitted of all four charges since no offense can be specifically attributed to him. However, a simple calculation shows that the probability that the defendant committed no offense at all is only .01%! Consequently, it seems that convicting him for at least one offense without specifying what this offense is would be just and efficient.

We argue that under certain conditions, deterrence, efficient law enforcement, and minimization of adjudication errors would be better achieved were courts to aggregate probabilities across different offenses and convict defendants even for unspecified offenses. We show as well under what conditions aggregating probabilities will yield less, rather than more, convictions. Lastly, we explore the legitimacy of aggregation in light of retributivist and expressivist theories of punishment.

Disciplines

Courts | Criminal Law | Criminal Procedure | Evidence | Law and Economics | Law and Society | Law Enforcement and Corrections | Litigation | Public Law and Legal Theory

Date of this Version

August 2008

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