The paper presents the joint use of machine learning methods and agent-based modeling for analysis and scenario forecasts to reduce the impact of trade flow destabilization on food security in Russia in increasing sanctions pressure. The authors propose a conceptual scheme of a software and analytical complex for forecasting indicators of agri-food supply chains. The obtained results can form the basis of a socio-economic multi-agent model for ensuring food security. Using the proposed approach in the situation centers can help counter external threats and ensure Russia's national security.
DOI: 10.52605/16059921_2024_04_21
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Otmakhova, Y. S., Devyatkin, D. A. ML-driven agent-based simulation of agri-food supply chain // Information Society (4), 2024, pp. 21–32.