Guidelines to support international development of developing countries are many. Today, the best example of these guidelines is the Sustainable Development Goals (SDGs) – a set of 17 general goals monitored through 232 development indicators. Achieving these goals is, however, a complex task. There are interdependencies between development goals as well as inefficiencies and uncertainties in policymaking processes (e.g. corruption). A clearer understanding of development is needed to evaluate SDGs, to align environmental policies, to coordinate anti-poverty policies, and to better understand the synergies and trade-offs between development goals.
The research project Policy Priority Inference (PPI) has been established to overcome these challenges. By specifying the policymaking process through a political economy game on a network of spill-over effects, PPI accounts for the network of interdependencies between policy issues (such as the sustainable development goals), as well as well-known political economy problems arising from budget assignment (e.g. corruption). At the core of the project is an agent-computing model that simulates – from bottom up – the observed dynamics of development indicators. This tool allows us to circumvent several limitations of traditional statistical methods (e.g. losing country-specificity from cross-national estimates).
In this talk, Omar will introduce the PPI methodology and demonstrate some of the various applications where it sheds new light on economic policies for development. This is for example the case in estimating policy resilience, studying ex-ante policy evaluation, quantifying policy coherence, and assessing the effectiveness of governance reforms in the fight against corruption. In addition, we will discuss the direction that PPI is taking towards the SDG 2030 agenda and ongoing collaborations with the United Nations Development Programme (UNDP). The talk concludes that agent modelling and data science can find positive applications for international development and data-driven policy-making.
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