What policymakers should ask modelers during a pandemic
On April 8, New York Governor Andrew Cuomo announced that his state was “flattening the curve” of the COVID-19 pandemic. But only two weeks earlier, various models had projected that peak hospitalizations in New York could be several times higher than they in fact turned out to be.
Juxtaposing the actual number of COVID-19 hospitalizations with those projections, Cuomo wondered, “How do you come up with an actual curve that is so much different from what those experts predicted?”
Cuomo’s question encapsulates the challenge that decision-makers face when dealing with predictive models. When the stakes are high, and model-based projections are a primary guide, how should policymakers proceed?
It’s a relevant question not only during a pandemic. The 2008 financial crisis highlighted the power of economic and financial models and reliance on such tools will only continue to grow in an age of big data and big computing power.
As scientists who routinely build models for policy analysis, we propose four questions that decision-makers should ask when using such models’ results.
First, why was the model created? Every model simplifies reality, and its creator’s decisions regarding what to simplify (and how), what to include and what to leave out are based primarily on the questions the model was built to explore. A model’s specific purpose guides the choice of mathematical equations and methods used. When a model is repurposed to investigate questions for which it was not originally intended, the results will be only as good as the alignment of the questions and the model design.
Second, what are the model’s key assumptions and are they all valid for the situation at hand? It’s not only the question for which a model was designed that matters.
Other assumptions may relate to which aspects of the past will remain the same in the future. Many models simply provide a scenario of a possible future if things follow the same course they did previously. Such analyses are useful, because they show what may happen if we do not adapt.
Our societies are dynamic and most people constantly respond to new information with varying degrees of delay. As a result, the very introduction of a prediction into the public realm can alter a future trajectory. Policymakers therefore need to know which aspects of the real world a model assumes as fixed.
Third, where do the data that are fed into the model come from and how applicable are they in the current context?
Models use data to computer-specific results, so it is crucial to know where those data come from and how accurate they are. Ideally, the data should come from reliable sources, cover the region for which the policy in question is being considered and be as up-to-date as possible. In reality, data may be limited, not very granular or from a different context. If so, the modelers should make this clear.
Policymakers often must make the best use of available data, despite weaknesses in those data. Duly noting such flaws or shortcomings provides important context for decisions and also highlights the urgency of acquiring better data as soon as possible.
Finally, how uncertain are the results? Fixating on a single forecast without placing due emphasis on its uncertainty can be dangerous and costly. Modelers, and news reports referring to models, should clearly communicate the sources and extents of any uncertainties. At the same time, policymakers must seek to understand a model’s margin of error and keep that in mind when making a decision.
With decision-makers relying on a growing torrent of forecasts regarding COVID-19 and other important issues, it is more important than ever that they ask questions about how the projections were made.
Afreen Siddiqi is a visiting scholar and adjunct lecturer in public policy at the Belfer Center for Science and International Affairs at the Harvard Kennedy School. Kaveri Iychettira is a postdoctoral fellow at the Belfer Center for Science and International Affairs at the Harvard Kennedy School. Copyright: Project Syndicate, 2020. www.project-syndicate.org