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Cited by 1,222

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

A Behavioral Approach to Law and Economics

Open access
Jolls, Christine, Sunstein, Cass R., Thaler, Richard·1998·Stanford Law Review

Abstract

Economic analysis of law usually proceeds under the assumptions of neoclassical economics. But empirical evidence gives much reason to doubt these assumptions; people exhibit bounded rationality, bounded self-interest, and bounded willpower. This article offers a broad vision of how law and economics analysis may be improved by increased attention to insights about actual human behavior. It considers specific topics in the economic analysis of law and proposes new models and approaches for addressing these topics. The analysis of the article is organized into three categories: positive, prescriptive, and normative. Positive analysis of law concerns how agents behave in response to legal rules and how legal rules are shaped. Prescriptive analysis concerns what rules should be adopted to advance specified ends. Normative analysis attempts to assess more broadly the ends of the legal system: Should the system always respect people's choices? By drawing attention to cognitive and motivational problems of both citizens and government, behavioral law and economics offers answers distinct from those offered by the standard analysis.

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Metadata

Authors
Jolls, C., Sunstein, C. R., & Thaler, R.
Venue
Stanford Law Review
Publisher
JSTOR
Published
1998-05
Volume
50
Issue
5
Pages
1471
ISSN
0038-9765 (ISSN-L)
OpenAlex
W1792494105
Cited by
1,222
References
2
FWCI
71.46
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A Behavioral Approach to Law and Economics. https://app.cochat.ai/papers/10.2307/1229304
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Jolls, C., Sunstein, C. R., & Thaler, R. (1998). A Behavioral Approach to Law and Economics. Stanford Law Review. https://doi.org/10.2307/1229304

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.