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They provide a pictorial form of systems, consisting of nodes (concepts) and node interconnections (weights), and perform system simulations for various input combinations. Due to their simplicity and quasi-quantitative nature, they can be easily used with and by non-experts. However, these features come with the price of ambiguity in output: recent literature indicates that changes in selected FCM parameters yield considerably different outcomes. Furthermore, it is not a priori known whether an FCM simulation would reach a fixed, unique final state (fixed point). There are cases where infinite, chaotic, or cyclic behaviour (non-convergence) hinders the inference process, and literature shows that the primary culprit lies in a parameter determining the steepness of the most common transfer functions, which determine the state vector of the system during FCM simulations. To address ambiguity in FCM outcomes, we propose a certain range for the value of this parameter, <jats:inline-formula><jats:alternatives><jats:tex-math>$${\\uplambda }$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>\u03bb<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, which is dependent on the FCM layout, for the case of the log-sigmoid and hyperbolic tangent transfer functions. The analysis of this paper is illustrated through a novel software application, <jats:italic>In-Cognitive<\/jats:italic>, which allows non-experts to define the FCM layout via a Graphical User Interface and then perform FCM simulations given various inputs. The proposed methodology and developed software are validated against a real-world energy policy-related problem in Greece, drawn from the literature.<\/jats:p>","DOI":"10.1007\/s12351-022-00717-x","type":"journal-article","created":{"date-parts":[[2022,5,29]],"date-time":"2022-05-29T14:02:21Z","timestamp":1653832941000},"page":"5733-5763","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Parameter analysis for sigmoid and hyperbolic transfer functions of fuzzy cognitive maps"],"prefix":"10.1007","volume":"22","author":[{"given":"Themistoklis","family":"Koutsellis","sequence":"first","affiliation":[]},{"given":"Georgios","family":"Xexakis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3656-4874","authenticated-orcid":false,"given":"Konstantinos","family":"Koasidis","sequence":"additional","affiliation":[]},{"given":"Alexandros","family":"Nikas","sequence":"additional","affiliation":[]},{"given":"Haris","family":"Doukas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,29]]},"reference":[{"key":"717_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s12351-020-00606-1","author":"M Abbaspour Onari","year":"2020","unstructured":"Abbaspour Onari M, Jahangoshai Rezaee M (2020) A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry. 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