Natural Language Heuristics—Implications for Managerial Cognition and AI

This article introduces natural language heuristics (NLH), which extend crisp heuristics across three domains: ‘nature of uncertainty,’ ‘linguistic variables,’ and ‘knowledge structure.’ The first domain is grounded in Knight’s seminal ideas, emphasizing uncertainty as a numerically immeasurable probability. It advocates shifting from crisp probability to fuzzy sets and possibility theory. The second domain regards transitioning from bivalent to fuzzy logic. The third domain relies on perceptual rather than amodal symbol systems. This article opens new research directions for heuristics, intuition, and dual-process theory while igniting fresh inquiries into the implications of NLH for artificial intelligence (AI) and managerial and organizational cognition.

• Publication year

  2025

• Venue

  IEEE International Conference on Fuzzy Systems

• Publication date

  2025-07-06

• Fields of study

  Business, Computer Science

• Identifiers
  DOI 10.1109/FUZZ62266.2025.11152255
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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