A pioneering policy priority tool for tracking progress towards the United Nations’ Sustainable Development Goals (SDGs), has been developed by UCL in collaboration with a UK-Mexico team.
Policy Priority Inference (PPI) uses AI to model the impact of a variety of policy decisions against the UN’s SDGs. The tool has the potential to supercharge the effectiveness of government-backed sustainable development for the benefit of billions of people and the future of our planet.
The tool, which is supported by the Alan Turing Institute and championed by the United Nations Development Programme (UNDP), is already being implemented across Latin America, with plans to bring it to other global regions.
The PPI was developed by Dr Omar Guerrero (UCL Economics and ESRC/Turing Fellow) with his collaborator Professor Gonzalo Castañeda (Centre for Research and Teaching in Economics, Mexico), who have together led a team of researchers in the UK and Mexico.
Dr Omar Guerrero, Policy Priority Inference project leader, based at UCL Economics and The Alan Turing Institute, said: "Governments around the world have had to re-direct substantial resources in order to fight the Covid-19 pandemic, preventing them from achieving their original goals.
"Our Policy Priority Inference tool helps leaders to reach the UN’s Sustainable Development Goals at a critical time. Using AI our tool helps policymakers correctly prioritise public expenditure to ensure that the multi-dimensional global goals can be met."
All over the world, countries are striving to meet the UN’s 17 SDGs by 2030. The goals seek to address the myriad of global challenges faced by humanity, including inequality, to healthcare and education, climate change, and the Covid-19 global pandemic, which has changed our world in ways previously unimagined.
Individual countries are responsible for targeting resources and funding towards meeting the SDGs, and the UN is monitoring progress against them by collecting data on over 200 ’development indicators’.
Prioritising issues for maximum impact is an enormous challenge for governments. The range of development policy options is countless, often with unanticipated inefficiencies that expend resources. And, crucially, there are complex interdependencies between policies that should be taken into consideration. For example, investing in industrialisation tends to also produce negative outcomes for the environment, while investing in public transport might also boost education outcomes because more children are able to school.
Previously, economists focused on GDP as a measure of development, but this is increasingly considered a blunt measure that is ill-equipped to monitor progress on the 17 SDGs and over 200 development indicators.
PPI builds on a behavioural computational model, taking into account the learning process of public officials, coordination problems, incomplete information, and imperfect governmental monitoring mechanisms. The approach is a unique mix of economic theory, behavioural economics, network science and agent-based modelling.
Annabelle Sulmont, Public Policy Project Coordinator for the UNDP office in Mexico, said: "The results of this project show the potential the Policy Priority Inference model has for providing governments with concrete information on how to increase the effectiveness of public spending and accelerate the achievement of development goals. The model also provides a common language that enables its implementation in other parts of the world, and facilitates and comparing results across regions and countries."
Dr Guerrero concluded: "Government expenditure data will take this technology to a whole new level. As well as rolling out the model to other global leaders, we also want to bring these tools to NGOs for them to assess the actions of governments and check that governments are prioritising the right policies."
Read the full impact story on The Alan Turing Institute website : www.turing.ac.uk/research/impact-stories/supercharging-sustainable-development