Prioritising which policy targets and strategies should be chosen is a daunting task for any government. Simultaneous objectives, a multitude of options, unexpected implementation inefficiencies, and complex interdependencies between policies all need to be considered. Through cutting-edge computational technologies, this project is developing analytic methods that can inform governments on how to prioritise public policies while accounting for the complex nature of socioeconomic development. Such tools are paramount in tackling the major societal challenges of the 21st century.
Explaining the science
Over the last 50 years, an increasing number of countries have used guidelines provided by international organisations in order to shape their development strategies. Today, the best example of these guidelines is the Sustainable Development Goals (SDGs) established by the United Nations.
The SDGs consist of 17 general goals that are monitored through 232 development indicators. Before the SDGs, development indicators were designed to measure different policy issues in isolation from each other. Today, this has changed with the official acknowledgement that “development challenges are complex are interlinked”
Currently, the statistical methods employed to advise governments on policy prioritisation are unable to handle the complexity of the SDGs. This is due to the fact that they rely on techniques that are not designed to account for interdependencies among indicators or to explicitly model the imperfect (and often disconnected) processes of policy design and implementation.
Furthermore, traditional statistical techniques, such as regression analysis, struggle with the coarse-grained nature of data on development indicators and fail to scale well with the creation of more indicators. Therefore, designing and implementing public policies to achieve the SDGs demands a new paradigm to advise governments on what policy areas to prioritise. This project brings together ideas and methods from computational social sciences in order to create such a paradigm.
The aim of the project is to build a set of new analytical tools that governments, international organisations, and development consultants can use to identify context-specific policy priorities. Such tools overcome the main limitations that traditional statistical analyses face when dealing with data on development indicators, such as the inability to provide country-specific recommendations, to scale with the number of indicators, and to model interactions among a large set of indicators.
The project overcomes these limitations by explicitly modelling the socioeconomic mechanisms of the policy-making process through agent-computing simulation and complex networks.
The applications of this project are diverse and keep growing as the research evolves.
The output of the work can be used to infer the policies that each government prioritised in the past, given a time series dataset of development indicators. This allows for estimation of the relative importance that governments assigned to specific policy issues during the time period reflected in the dataset. It can also be used to identify the policy priorities that governments need to set if they are to adopt a specific development strategy. These prospective inferences can then be used to evaluate and prescribe priorities.
The work also provides a rigorous framework for measuring how resilient a development strategy is to unexpected situations, such as when a natural disaster forces the government to reorganise its priorities. Resilience and prioritisation provide the basis needed to identify the development bottlenecks of specific countries.
Finally, this approach can also be applied at the sub-national level in order to identify complementarities between policy priorities in different regions.