Improving the impact of agricultural and health modelling through better behavioral models
Special Track Chairs: Jose Manuel Magallanes (University of Washington and Pontificia Universidad Catolica Del Peru) and Stanley Wood (University of Washington and Bill & Melinda Gates Foundation)
Climate and weather forecasts and epidemiological modelling have improved dramatically over the past several decades, with more knowledge of the underlying physical and biological systems at work. This ex-ante modelling can play an important role in preparing for what is expected to be an increasing future frequency of climate and disease shocks, and thereby in theory, reduce the human health and economic impacts. But if policymakers are to effectively use these models for planning purposes, they need to know how the information will be received and used by stakeholder groups. Climate and disease forecasts will need to be accompanied by better human behavior modelling and the challenges that poses. As COVID-19 has made clear, the trajectory of the virus, and therefore the most effective suite of policies for different health, economic and social outcomes, is intricately linked to the behavior of the individuals and communities in which the virus is circulating.
This track will feature work on advances in human behavioral models and the scope for cross-learning around end user decision making in health (e.g., vaccine acceptance, preventative behaviors) and agriculture (e.g., taking on board new climate resilient or productivity enhancing technologies and practices).
This track would fit into conference themes through the data available, and the model as influenced by, the domestic data ecosystem and aspects of individual and community behavior that are context specific. The data ecosystem would affect what data modelers would have access to. The domestic political economy (that may underlie the data ecosystem) would affect the behavioral model itself, and what choices were available to users.