JOURNAL ARTICLE

C. S. Peirce on Jeremy Bentham: "A shallow logician" confined to analysis of "lower motives".

  • Published In: Theoria: A Swedish Journal of Philosophy, 2024, v. 90, n. 3. P. 264 1 of 3

  • Database: Humanities Source Ultimate 2 of 3

  • Authored By: Zhang, Yanxiang 3 of 3

Abstract

C.S. Peirce offered an evaluation of Bentham's philosophy to the effect that on some points Bentham's performance was of great value, but essentially, he was 'a shallow logician' confined to analysis of 'lower motive'. This paper argues that Bentham's logic is deeply metaphysically based, multi‐levelled, and comprehensive. There are at least three constituent parts in his utilitarian logic: the first is his ontology, with its distinction between real and fictitious entities, and with pain and pleasure constituting the core real entities; the second is his reductionism in, and analytical view of, simple and complex pleasures and pains; the third is the distinction between private ethics and public ethics. Bentham's logic is staunchly based on empiricism and truth and he developed a pragmatic utilitarian solution to overcome the potential impasse of Hume's scepticism through a mechanism of reflection. Even the doctrines of belief and abduction embraced and developed by Peirce are contained in Bentham's utilitarian logic. Bentham would certainly take Peirce's philosophy as ipse dixitism. Peirce was not in fact a serious reader of Bentham and failed to employ the distinction between argument and argumentation in his study of Bentham's logic. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Theoria: A Swedish Journal of Philosophy. 2024/06, Vol. 90, Issue 3, p264
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:00405825
  • DOI:10.1111/theo.12515
  • Accession Number:177929569
  • 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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