Policy Priority Inference for Sustainable Development, Omar Guerrero

Explore the Policy Priority Inference (PPI) methodology for sustainable development in this 31-minute talk by Omar Guerrero from the Alan Turing Institute. Learn about the complexities of achieving Sustainable Development Goals (SDGs) and how PPI addresses challenges like interdependencies between goals, inefficiencies in policymaking, and corruption. Discover how this agent-computing model simulates development indicator dynamics from the bottom up, overcoming limitations of traditional statistical methods. Examine applications of PPI in estimating policy resilience, ex-ante policy evaluation, quantifying policy coherence, and assessing governance reforms' effectiveness against corruption. Gain insights into PPI's role in the SDG 2030 agenda and its collaboration with the United Nations Development Programme (UNDP). Understand how agent modeling and data science contribute to international development and data-driven policymaking, with a focus on the Mexican coherence index as an OECD case study.

Intro
Outline
SDG data
SDG networks (87 indicators, 20 years)
Complexity of policy prioritisation
What can PPI do? 1. Synergies and trade-offs
Model sketch
Advantages 1. Theoretical explanation of how policies are designed and implemented causal mechanisms
Development indicator data
Quantifying policy coherence
Measuring coherence
Mexican coherence index (OECD case)
Soft validation