Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence

Meng-Leong How, Yong Jiet Chan and Sin-Mei Cheah
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Meng-Leong How: National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637616, Singapore
Yong Jiet Chan: Faculty of Education, Monash University, Australia, 29 Ancora Imparo Way, Clayton VIC 3800, Australia
Sin-Mei Cheah: Centre for Management Practice, Singapore Management University, 81 Victoria Street, Singapore 188065, Singapore

Sustainability, 2020, vol. 12, issue 15, 1-14

Abstract: Unabated pressures on food systems affect food security on a global scale. A human-centric artificial intelligence-based probabilistic approach is used in this paper to perform a unified analysis of data from the Global Food Security Index (GFSI). The significance of this intuitive probabilistic reasoning approach for predictive forecasting lies in its simplicity and user-friendliness to people who may not be trained in classical computer science or in software programming. In this approach, predictive modeling using a counterfactual probabilistic reasoning analysis of the GFSI dataset can be utilized to reveal the interplay and tensions between the variables that underlie food affordability, food availability, food quality and safety, and the resilience of natural resources. Exemplars are provided in this paper to illustrate how computational simulations can be used to produce forecasts of good and bad conditions in food security using multi-variant optimizations. The forecast of these future scenarios is useful for informing policy makers and stakeholders across domain verticals, so they can make decisions that are favorable to global food security.

Keywords: artificial intelligence; global food security index; predictive modeling; machine learning; AI for social good; sustainability; resilience; Bayesian; counterfactual; cognitive scaffolding (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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