Case study demonstrates fuzzy cognitive mapping accurately models dynamic famine drivers in food-insecure settings, indicating enhanced early warning capabilities for humanitarian response.
Abstract This paper explores the limitations of current famine analysis methodologies and introduces Fuzzy Cognitive Mapping (FCM) as a novel approach to understanding famine dynamics. Traditional methods, such as the Integrated Food Security Phase Classification (IPC), often fail to capture the complex, non-linear interactions among social, economic, political, and environmental factors that drive famine. These methods rely heavily on cross-sectional survey data and expert judgment, which are prone to errors and biases. This study demonstrates the utility of FCM in analyzing famine systems, using the 2017 Ayod County crisis in South Sudan as a case study. FCM integrates qualitative and quantitative data, enabling real-time analysis of dynamic systems with limited data availability. The results highlight how FCM can reveal critical linkages and feedback loops, offering a more nuanced understanding of famine trajectories and enhancing early warning systems. The paper argues for the adoption of FCM in humanitarian contexts to improve the timeliness and accuracy of famine response and prevention strategies.
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Matthew Day (2026) studied this question.
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