Policy Priority Inference
Policy Priority Inference (PPI) is a research programme that aims to model the causal link between government expenditure and policy outcomes, while simultaneously accounting for the multidimensionality and complexity of development.
PPI employs computational methods to overcome the limitations of coarse-grained data on development indicators and public spending. A key output from the programme is an analytical tool that helps governments measure the impact of public expenditure on development outcomes. It supports evidence-based decision-making in an environment characterised by budget constraints, concurrent and competing targets, multidimensional development, imperfect governance, and context-specific interdependencies between policy issues. This tool uses a specific type of artificial intelligence called agent computing (or agent-based modelling), which allows modelling of socioeconomic agents and their decision- making processes, providing a transparent alternative to black-box approaches.
The PPI toolkit has been deployed in various countries through collaborations with multilateral organisations and governments to support development planning and evaluation in multidimensional settings.
The PPI research programme emerged in 2018 from a collaboration between The Alan Turing Institute, the Centro de Investigación y Docencia Económica, and the United Nations Development Programme. Currently, PPI continues its development at The Alan Turing Institute as one of the research strands of the Computational Social Science Group, part of the Institute’s Public Policy Programme.
The PPI toolkit
There are two versions of the PPI tool. The original model, written in Python, has been adopted by various users in public organisations. However, as working with code still imposes a significant adoption barrier, the research programme has created an online ‘app’ version with a graphical model and data templates for easy usage.
You can read the details about the latest version of the PPI model in this publication.
The new PPI app has been developed to run a simplified version of the PPI model. It is a responsive, user-friendly AI tool that simulates the policymaking process that allows you to interpret and analyse your data through a simple, clear interface without the need for any coding, providing interactive visualisations of the outputs.
The Python code
You can read the details about the latest version of the PPI model in this publication. The original model has been written in Python, and the links below provide access to its source code as well as to a package that can be installed via PyPI. In addition, this website provides a series of tutorials to use the Python version of the PPI model.
Policy Priorities & AI for SDGs Data Challenge
In January 2024 The Alan Turing Institute hosted an event gathering 30 teams from around the world to engage in a challenge using open spending data, development indicators, and the PPI app. The teams developed different policy-prioritisation strategies towards the UN’s Sustainable Development Goals.