JOURNAL ARTICLE

Sidgwick and Bentham's "double aspect" of utilitarianism revisited.

  • Published In: Theoria: A Swedish Journal of Philosophy, 2024, v. 90, n. 2. P. 161 1 of 3

  • Database: Humanities Source Ultimate 2 of 3

  • Authored By: Zhang, Yanxiang 3 of 3

Abstract

In "Sidgwick on Bentham: the 'Double Aspect' of Utilitarianism", Schofield argued that Bentham did not regard his psychological theory as part of his utilitarianism and that natural benevolence is at his disposal to mitigate the problem of the "double aspect" of utilitarianism. This paper argues that Bentham regarded his psychological theory as part of his utilitarianism and that, in a manner quite distinct from an internal, benevolence approach, he took advantage of self‐preference and thus adopted a self‐preference and artificial means‐based approach, thereby still maintaining an internal approach supported by external, institutional contrivance. In so doing, he argued first that self‐preference could add to the greatest happiness; second, that benevolence would tend to facilitate the achievement of the greatest happiness, with the consequence that he endorsed the artificial cultivation of benevolence; and third, that the artificial means of the junction‐of‐interests‐prescribing principle should be adopted to bridge the gap between self‐preference and the greatest happiness. Sidgwick failed to appreciate the depth and sophistication of Bentham's logic. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Theoria: A Swedish Journal of Philosophy. 2024/04, Vol. 90, Issue 2, p161
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:00405825
  • DOI:10.1111/theo.12517
  • Accession Number:176813010
  • Copyright Statement:Copyright of Theoria: A Swedish Journal of Philosophy is the property of Wiley-Blackwell 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.)

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