JOURNAL ARTICLE

Revisiting the tautology problem in rational choice theory: What it is and how to move forward theoretically and empirically.

  • Published In: European Journal of Criminology, 2024, v. 21, n. 4. P. 513 1 of 3

  • Database: Sociology Source Ultimate 2 of 3

  • Authored By: Steinmetz, Kevin F.; Pratt, Travis C. 3 of 3

Abstract

This article critically examines the tautological nature of criminological rational choice theory, which posits that if a crime occurs, the perceived benefits must exceed the costs, rendering the theory unfalsifiable. It highlights how rational choice theorists often address this issue by expanding the concept of rationality to include bounded or imperfect decision-making but ultimately maintain the core assumption that criminal behavior results from cost–benefit analyses. The authors argue for a reconceptualization of rationality as a variable reflecting the degree of thoughtful and reflective decision-making rather than an inherent assumption, suggesting that perceptions of the costs and benefits of crime should be treated as outcomes influenced by factors like self-control, social bonds, and community context. The article also explores alternative theoretical frameworks—such as symbolic interactionism, situational action theory, and dual-process models—that accommodate varying levels of rationality and decision-making complexity. It concludes that moving beyond tautological cost–benefit models could enhance the empirical and theoretical robustness of rational choice perspectives and reduce their alignment with punitive criminal justice policies.

Additional Information

  • Source:European Journal of Criminology. 2024/07, Vol. 21, Issue 4, p513
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2024
  • ISSN:1477-3708
  • DOI:10.1177/14773708241226537
  • Accession Number:177899603
  • Copyright Statement:Copyright of European Journal of Criminology is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.