their policymaking, using artificial intelligence (AI) to simulate the economy, and the
effects of new policies.
“Agent-based” models simulate the behaviour of different types of participants in the
economy by allowing them to respond to each other over time: if a public servant can
get away with pocketing more money, or a taxpayer with paying less tax, then they will
do so. Some simulate surprisingly realistic behaviour by using machine learning to
“train” the model using vast sets of data. One such approach is Policy Priority Inference,
developed by researchers in Britain and Mexico and sponsored by the UN’s
development programme. Already used in Mexico, it takes governments’ spending plans
across a range of categories and works out, based on its simulation of corruption,
inefficiencies and spillovers, whether a government is likely to hit its development goals,
and where more (or less) money should be spent. More poor countries could see the
appeal of such an approach.
Interest in rich countries could be piqued, too. Researchers at Salesforce, a software
company, and Harvard University have used simulations to show that, much as
computers can learn to play Go and develop strategies that might not occur to humans,
they can also suggest combinations of tax and spending that maximise economic
performance, and which bureaucrats might not have dreamed up. So why not turn to ai
for fresh ideas?
None of this means that economists or bureaucrats will find themselves out of work in
- Interpreting the models’ results requires expertise. Politicians will not cede their
power to raise and lower tax rates. But policymakers and researchers keen to
experiment in the aftermath of the pandemic will have an opportunity to expand their
toolkits.
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