Modeling Sustainable Food Systems Under Uncertainty: Computational and Macroeconomic Approaches to Nutrition Security

Food systems occupy a central position at the crossroads of climate change, energy and digital transitions, demographic evolution, trade disruptions, and financial shocks. Each of these global trends affects agricultural production, food prices, consumption behavior, and the capacity to meet nutrition needs. However, traditional analytical frameworks often fail to capture the complex interdependencies and dynamic uncertainties underlying these processes. Recognizing food systems as Complex Adaptive non-linear Systems is increasingly important for understanding how localized disruptions can trigger systemic shifts. Recent methodological advances, such as Complex Adaptive Systems frameworks, dynamic stochastic general equilibrium (DSGE) models, Bayesian estimation, network analysis, stochastic optimization, and machine learning, offer a comprehensive way to represent how shocks propagate through food systems and impact nutrition outcomes. These tools have begun to link macroeconomic and micro-level nutrition research, but their applications remain limited. Expanding their use could shed light on persistent global challenges related to food security, sustainable diets, and nutrition resilience in an era of overlapping crises.This Research Topic aims to unite quantitative, computational, and policy-oriented studies that model how food systems, understood as complex adaptive non-linear systems, respond to economic, climatic, and technological transformations. It seeks to bridge macroeconomic and nutrition sciences by exploring how energy volatility, digitalization, and climate adaptation influence dietary affordability, food distribution, and health outcomes. Central questions include how different policy instruments, such as agricultural subsidies, trade regulations, fiscal measures, and digital platforms, affect access to nutritious food under uncertainty; how machine learning, Bayesian approaches, and non-linear dynamics can forecast malnutrition risks or dietary transitions; and how macro-financial modeling can enhance the design of interventions that promote sustainable nutrition. The overarching objective is to strengthen the computational and theoretical foundations of nutrition security and to support progress toward the UN Sustainable Development Goals on zero hunger and good health.To gather further insights into the complex and dynamic mechanisms driving food system sustainability and nutrition outcomes, this Research Topic welcomes contributions addressing, but not limited to, the following themes: o Complex Adaptive non-linear Systems approaches to food security and sustainable food systems o Macroeconomic and DSGE modeling of food prices, dietary affordability, and nutrition outcomes o Climate and energy shocks transmitted to food security and sustainable diets o Bayesian and machine-learning methods for forecasting food demand, malnutrition risk, and dietary transitions o Trade, agricultural, and food-policy evaluation under uncertainty o Digitalization, e-commerce, and AI effects on food access, consumer choice, and diet quality o Network and heterogeneous-agent models of food supply chains, food waste, and resilience o Computational scenario analysis for sustainable diets and nutrition policy design o Macro-financial drivers of obesity, undernutrition, and the double burden of malnutritionWe welcome Original Research, Methods, Review, Policy and Practice, and Perspective articles. Computational studies based on public datasets must include appropriate validation in line with the Frontiers Standards for research methodology. Topic editors: Yugang He, RENHONG WU Source: https://www.frontiersin.org/research-topics/82173/modeling-sustainable-food-systems-under-uncertainty-computational-and-macroeconomic-approaches-to-nutrition-security