CASA Seminar Series: Omar Guerrero, Alan Turing Institute

About the talk

The explicit acknowledgement of the complexity of the Sustainable Development Goals (SDGs) is one of the main innovations of this international agenda. However, the formal analysis of complex systems in the SDG literature remains scant, as most of the focus is given to (top-down) aggregate models such as systems dynamics and networks of indicators. In this talk, I will argue that an adequate treatment of complexity needs to look at development as a bottom-up process, with macro-level outcomes emerging from micro-level interventions. From a quantitative point of view, popular methodologies such as statistical analysis and machine learning are inadequate to deal with this vertical causation since the existing data are aggregate and coarse grained (typically annual development indicators). To resolve this, models with explicit agent-level causal mechanisms are needed, and agent computing is the right tool to create them. I will present the research program of Policy Priority Inference (oguerr.com/ppi), which employs agent computing to model the SDGs from the perspective of public expenditure interventions. I will discuss several applications related to policy coherence, policy resilience, feasibility, fiscal federalism, accelerators, and bottlenecks; as well as the country-case studies in which they have been applied. This programme provides a fresh perspective to the challenges of multidimensional development, and a rigorous approach to exploit not only indicators, but also new sources such as open spending data.