Abstract

http://ssrn.com/abstract=2217064
 
 

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Personalizing Default Rules and Disclosure with Big Data


Ariel Porat


Tel Aviv University; University of Chicago - Law School

Lior Strahilevitz


University of Chicago Law School

February 13, 2013

112 Michigan Law Review 2014, Forthcoming
University of Chicago Coase-Sandor Institute for Law & Economics Research Paper No. 634
U of Chicago, Public Law Working Paper No. 418

Abstract:     
This paper provides the first comprehensive account of personalized default rules and personalized disclosure in the law. Under a personalized approach to default rules, individuals are assigned default terms in contracts or wills that are tailored to their own personalities, characteristics, and past behaviors. Similarly, disclosures by firms or the state can be tailored so that only information likely to be relevant to an individual is disclosed, and information likely to be irrelevant to her is omitted. The paper explains how the rise of Big Data makes the effective personalization of default rules and disclosure far easier than it would have been during earlier eras. The paper then shows how personalization might improve existing approaches to the law of consumer contracts, medical malpratice, inheritance, landlord-tenant relations, and labor law.

The paper makes several contributions to the literature. First, it shows how data mining can be used to identify particular personality traits in individuals, and these traits may in turn predict preferences for particular packages of legal rights. Second, it proposes a regime whereby a subset of the population (“guinea pigs”) is given a lot of information about various contractual terms and plenty of time to evaluate their desirability, with the choices of particular guinea pigs becoming the default choices for those members of the general public who have similar personalities, demographic characterics, and patterns of observed behavior. Third, we assess a lengthy list of drawbacks to the personalization of default rules and disclosure, including cross-susidization, strategic behavior, uncertainty, stereotyping, privacy, and institutional competence concerns. Finally, we explain that the most trenchant critiques of the disclosure strategy for addressing social ills are really criticisms of impersonal disclosure. Personalized disclosure not only offers the potential to cure the ills associated with impersonal disclosure strategies, but it can also ameliorate many of the problems associated with the use of personalized default rules.

Number of Pages in PDF File: 57

Keywords: Default Rules, Disclosure, Personalized Default Rules, Personalized Disclosure, Intestacy, Wills, Contracts, Medical Malpractice, Informed Consent, Guinea Pigs, Big Data, Privacy, Psychology, Five Factor Model, Big Five

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Date posted: February 14, 2013 ; Last revised: March 25, 2014

Suggested Citation

Porat, Ariel and Strahilevitz, Lior, Personalizing Default Rules and Disclosure with Big Data (February 13, 2013). 112 Michigan Law Review 2014, Forthcoming; University of Chicago Coase-Sandor Institute for Law & Economics Research Paper No. 634; U of Chicago, Public Law Working Paper No. 418. Available at SSRN: http://ssrn.com/abstract=2217064 or http://dx.doi.org/10.2139/ssrn.2217064

Contact Information

Ariel Porat
Tel Aviv University ( email )
Ramat Aviv
Tel Aviv 69978, IL
Israel
972-3-6408283 (Phone)
972-3-6407260 (Fax)
HOME PAGE: http://www.law.tau.ac.il/Heb/?CategoryID=357&ArticleID=388
University of Chicago - Law School ( email )
1111 E. 60th St.
Chicago, IL 60637
United States
Lior Strahilevitz (Contact Author)
University of Chicago Law School ( email )
1111 E. 60th St.
Chicago, IL 60637
United States
773-834-8665 (Phone)
773-702-0730 (Fax)
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