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They serve to examine a system\u2019s structure (e.g., what concepts are critical to spreading an intervention\u2019s effects?) and long-term behavior (e.g., if we increase fruit availability, how will its consumption change?). When modelers build FCMs by leveraging participants\u2019 knowledge, the resulting participant-built FCMs can be analyzed and interpreted since participants report perceived causality. However, engaging enough knowledgeable participants to construct an FCM can be challenging. Alternatively, machine learning algorithms derive FCMs from data by selecting relationships to maximize a metric such as accuracy, which can produce overly dense FCMs where relationships may not represent valid causal mechanisms\u2014this hinders the critically important interpretability of FCMs. In this paper, we address the need for expert knowledge and the validity of causal mechanisms in FCMs. Specifically, we propose using Large Language Models (LLMs) to guide algorithms in building FCMs where valid pairs of concepts are connected in the right causal direction and with the correct causal type (increase\/decrease). Our approach combines LLMs with CMA-ES, a ubiquitous, state-of-the-art evolutionary algorithm. Using three real-world case studies and several LLMs, we show our method successfully (i) learns sparse FCMs that fit the data well and (ii) represents valid causal relationships. Moreover, the learned FCMs are sparser than the corresponding participant-built ones, demonstrating our method may help simplify existing FCMs and selectively includes causal relationships, which is essential to build trustworthy and interpretable FCMs.<\/jats:p>","DOI":"10.1007\/s00521-025-11157-x","type":"journal-article","created":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T02:04:30Z","timestamp":1742695470000},"page":"11891-11908","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Guiding evolutionary algorithms with large language models to learn fuzzy cognitive maps"],"prefix":"10.1007","volume":"37","author":[{"given":"Ryan","family":"Schuerkamp","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6816-355X","authenticated-orcid":false,"given":"Philippe J.","family":"Giabbanelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"issue":"1","key":"11157_CR1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0020-7373(86)80040-2","volume":"24","author":"B Kosko","year":"1986","unstructured":"Kosko B (1986) Fuzzy cognitive maps. 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