{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T09:09:11Z","timestamp":1775552951031,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Traditional fuzzy decision-making often relies on manual expert calibration, which is labor-intensive and susceptible to subjective bias. This study addresses these limitations by proposing a novel framework that transforms the intrinsic probabilistic outputs of Large Language Models (LLMs) into Triangular Fuzzy Numbers (TFNs). We introduce a multi-temperature sampling strategy coupled with weighted quantile aggregation and an adaptive interval adjustment mechanism to systematically map model stochasticity to fuzzy possibility distributions. Empirical validation on a structured prototype dataset demonstrates that the proposed method achieves high consistency with expert consensus, with GPT-4.2 exhibiting superior central accuracy and Gemini-2.5 excelling in uncertainty coverage. Furthermore, in complex unstructured scenarios involving business public opinion, the integration of Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) significantly corrects cognitive biases and converges uncertainty boundaries. This research establishes a rigorous pathway from generative AI probabilities to fuzzy decision theory, offering a robust automated solution for quantitative risk assessment and intelligent decision support.<\/jats:p>","DOI":"10.3390\/info17040349","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T08:09:00Z","timestamp":1775549340000},"page":"349","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging Stochasticity and Fuzziness: Automated Construction of Triangular Fuzzy Numbers via LLM Temperature Sampling for Managerial Decision Support"],"prefix":"10.3390","volume":"17","author":[{"given":"Meng","family":"Zhang","sequence":"first","affiliation":[{"name":"PLA Naval Logistics Academy, Tianjin 300450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Bai","sequence":"additional","affiliation":[{"name":"PLA Naval Logistics Academy, Tianjin 300450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanfei","family":"Guo","sequence":"additional","affiliation":[{"name":"PLA Naval Logistics Academy, Tianjin 300450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlong","family":"Xu","sequence":"additional","affiliation":[{"name":"PLA Naval Logistics Academy, Tianjin 300450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ranjun","family":"Wang","sequence":"additional","affiliation":[{"name":"PLA Naval Logistics Academy, Tianjin 300450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingdong","family":"Chen","sequence":"additional","affiliation":[{"name":"PLA Naval Logistics Academy, Tianjin 300450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuliang","family":"Zhao","sequence":"additional","affiliation":[{"name":"PLA Naval Logistics Academy, Tianjin 300450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","article-title":"Fuzzy sets","volume":"8","author":"Zadeh","year":"1965","journal-title":"Inf. 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