An Investigative Study of Fuzzy Cognitive Maps for Strategic Farm Management

2026-02-06T08:45:56+00:00

Strategic farm management is increasingly challenged by the growing complexity of socio-economic dynamics, environmental uncertainties, climate variability, and the rapid integration of precision agriculture technologies. In this context, effective decision-support tools are essential for managing interconnected agricultural systems. This review article investigates the application of Fuzzy Cognitive Maps (FCMs) as a robust and flexible modeling approach for strategic decision-making in agriculture. FCMs uniquely bridge the gap between qualitative expert knowledge and quantitative computational modeling by representing complex causal relationships among system variables in an intuitive and transparent manner. Through a structured analysis of recent literature, this study identifies key applications of FCMs in areas such as crop yield prediction, sustainability and resource-use assessment, risk and resilience evaluation, and agricultural policy analysis. The findings indicate that FCMs offer superior interpretability and explainability compared to traditional “black-box” machine learning models, making them particularly suitable for participatory farm management and stakeholder-driven planning. Nevertheless, challenges remain, including sensitivity to weight assignment, scalability, and the need for systematic methods to achieve expert consensus. Overall, this review highlights current advances in FCM-based agricultural research and outlines future directions for developing more resilient, adaptive, and data-informed farm management systems.

Type-2 Fuzzy Logic Systems for Robust Medical Diagnosis Under Deep Clinical Uncertainty: A Comparative Study with Type-1 Models

2026-01-21T10:21:15+00:00

Medical diagnosis as a problem of uncertainty is caused by subjectivity in the description of symptoms, noisy clinical measurements and non-uniformity in the interpretation by experts. The widely studied imprecision has traditionally been modelled using fuzzy logic systems, although conventional Type-1 fuzzy systems assume strictly defined membership functions, which do not accommodate high levels of uncertainty in clinical environments. The paper is indicative of a comparative analysis of Type-1 and Type-2 fuzzy logic framework in medical diagnostic decision support under the presence of profound clinical uncertainty in a systematic fashion. The two models use an integrated rule base with the aim that a clinically motivated diagnostic problem is formulated in a manner that both models are given a fair comparison. It has a Type-2 system, the representation of membership functions in the form of interval-valued representations directly involves uncertainty, providing an explicit representation of clinician disagreement and population heterogeneity. Massive experiments are performed on a carefully designed synthetic data that emulates diagnostic uncertainty within the real world, an example of which includes measurement randomness, missing data, and perturbations to membership. The results have shown that Type-2 fuzzy logic system is more sensitive, specific, and larger AUC than Type-1 counterpart when the uncertainty is larger. The findings have provided practical and theoretical evidence on the application of Type-2 fuzzy logic to provide sound medical diagnosis under deep and protracted uncertainty.

Journal of Fuzzy Sets and Fuzzy Logic Design (e-ISSN: 3049-0227)

An Investigative Study of Fuzzy Cognitive Maps for Strategic Farm Management

Type-2 Fuzzy Logic Systems for Robust Medical Diagnosis Under Deep Clinical Uncertainty: A Comparative Study with Type-1 Models