JOURNAL ARTICLE
Truth and Finite Conjunction.
Published In: Mind (0026-4423), 2024, v. 133, n. 532. P. 1121 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Luo, Guanglong; Horsten, Leon; Roberts, Sam 3 of 3
Abstract
This article critically examines Kentaro Fujimoto's new conservativeness argument against deflationism about truth, which relies on formalizing finite conjunctions within first-order arithmetic. Deflationism holds that truth is an insubstantial, quasi-logical notion, often explicated via proof-theoretic conservativeness—meaning adding truth axioms does not yield new theorems about truth-free subject matter. Fujimoto's argument claims that adding the principle of conjunctive correctness (that a finite conjunction is true if and only if each conjunct is true) leads to non-conservativeness, thus challenging deflationism. However, the authors argue that this conclusion depends heavily on the specific first-order formalization of finiteness; when finiteness and finite conjunctions are instead formalized naturally in set theory or second-order logic, the resulting truth theories remain conservative over the base theory. They further analyze Fujimoto's informal "blind deduction" arguments, showing that for the first two, finiteness assumptions are not essential, and for the third, a second-order treatment preserves conservativeness. The article concludes that the non-conservativeness result is sensitive to the formal framework chosen and that deflationism about truth is not decisively refuted by Fujimoto's argument when broader formalizations of finiteness are allowed.
Additional Information
- Source:Mind (0026-4423). 2024/10, Vol. 133, Issue 532, p1121
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2024
- ISSN:0026-4423
- DOI:10.1093/mind/fzae042
- Accession Number:180471488
- Copyright Statement:Copyright of Mind (0026-4423) is the property of Oxford University Press / USA 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.