Abstract

 


 



Precedent


Larry Alexander


University of San Diego School of Law

Emily L. Sherwin


Cornell University - Law School


U San Diego Legal Studies Research Paper No. 05-14

Abstract:     
This essay analyzes and compares different approaches to the problem of legal precedent. If judges reasoned flawlessly, the ideal approach to precedent would give prior judicial opinions only the weight they naturally carry in moral reasoning. Given that judges are not perfect reasoners, the best approach to precedent is one that treats rules established in prior decisions as authoritative for later judges. In comparison to the natural model of precedent, a rule-based model minimizes error. A rule-based model is also superior to several popular attempts at compromise, which call on judges to reason from the results of prior cases or from principles immanent in the body of precedent.

The principal drawback of a rule-based model of precedent is its seeming resistance to change. After defending the rule model against its competitors, we discuss a variety of refinements that clarify the model and make it more amenable to legal reform. Topics covered include identification of precedent rules, preconditions for authority, decision-making in the absence of a precedent rule, and overruling.

Number of Pages in PDF File: 36

Keywords: legal precedent, judicial reasoning, rule-based precedent, legal decision making

JEL Classification: K00, K10,

working papers series


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Date posted: September 16, 2004  

Suggested Citation

Alexander, Larry and Sherwin, Emily L., Precedent. U San Diego Legal Studies Research Paper No. 05-14. Available at SSRN: http://ssrn.com/abstract=591666 or http://dx.doi.org/10.2139/ssrn.591666

Contact Information

Lawrence Alexander (Contact Author)
University of San Diego School of Law ( email )
5998 Alcala Park
San Diego, CA 92110-2492
United States
619-260-2317 (Phone)
619-260-4728 (Fax)
Emily L. Sherwin
Cornell University - Law School ( email )
524 College Ave
Ithaca, NY 14853
United States
Feedback to SSRN (Beta)


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