WHY THIS MATTERS IN BRIEF
There are many inequalities in the world, but sometimes governments don’t know which investment or programs will make the biggest impact, now they have help.
As we see all too clearly today the world is full of inequalities and problems. In 2019 the US military began creating an AI called KAIROS that could track “global chaos” and monitor the entire world for threats, and in a more benign use of AI in 2015 United Nations member states signed up to a set of 17 UN sustainable development goals (SDG) that aim to solve many of them as possible by the year 2030. The SDG’s include things like “Zero Poverty,” “No Hunger,” and “Affordable and Clean Energy,” and even though we’re making progress and have a way to solve many of these challenges today, something I discuss in more depth in the 311 Institute’s Future World Series, ambitious is an understatement.
In order to try to help member states hit these ambitious goals the UN has now unveiled a new tool that they how will help member states make the right policy decisions to hit these goals. Called Policy Priority Inference (PPI), the software uses agent-based modelling to predict what would happen if policymakers spent money on one project rather than another.
This makes it easier for governments to choose which policies to prioritize, according to the UN and the Alan Turing Institute in London, which is also supporting the project. The tool is being tested by authorities in Mexico and Uruguay, with Colombia next in line. The UK’s Department for International Development is interested too.
While I’ve written about the rise of virtual nations before PPI works by drawing on economics, behavioural science, and network theory to simulate a virtual government which allocates a pot of money, and “bureaucrats,” who spend what they are given on different projects. The model, which was built by economists in the UK and Mexico, takes in a range of data, such as government budgets, the impact spending has had on particular policies in the past, the effectiveness of a country’s legal system, estimated losses due to known inefficiencies, and so on. It then suggests which policies are worth investing in most.
The idea is that the tool will help policymakers anticipate the ripple effects of their decision-making. For example, investing in education may alleviate gender inequality, but investing in GDP growth may not be good for reducing greenhouse-gas emissions.
And whether it will make a difference or not, well, PPI should be a step up in terms of analysing the potential effects of different policy choices, but it’s obviously got limitations. Models are only as good as the data put into them, for example, and some governments will be more willing than others to provide it. Simulations also work with a massively simplified version of reality, which affects accuracy. But with a decade to go and huge gaps in progress on most of the UN’s goals, the agency, and the world, can use all the help it can get.
Source: Alan Turing Institute