Application of Agent-Based Modeling for Policy Prioritization in Sub-Saharan Africa
Kindie Tesfaye-Fantaye of the International Maize and Wheat Improvement Center in Mexico will develop a computational model that incorporates the variable characteristics of households and farms to better predict the outcomes of agricultural interventions in Ethiopia in order to inform policy choices. Agriculture is central to the Ethiopian economy; it accounts for almost 50% of the gross domestic product and 80% of total employment, yet the industry struggles with limited infrastructure and environmental challenges. Prioritization of agricultural policies has generally relied on analysis of past observations, which are static and tend to ignore variability. They will build and validate an agent-based model that uses current data to model future outcomes, and input biophysical (e.g., soil, climate) and socioeconomic (e.g., household characteristics, land use, access to market and financing) data. They will test their model by comparing five candidate policy options under consideration by the government in terms of impact, effectiveness, efficiency, and inclusiveness. Once established, the model will be scaled up for policy intervention in other Sub-Saharan countries including Tanzania and Nigeria.