Future Tense

What the Pandemic Is Telling Us About Science, Politics, and Values

The novel coronavirus offers up a powerful and extremely clear lesson about the appropriate role of science in helping to guide us toward a better future.

U.S. Capitol Dome with coronavirus spikes all over it
Photo illustration by Slate. Photos by Getty Images Plus.

For the past 30 years, I have tried to make sense of the interactions between science and politics, especially the challenge of making decisions under conditions of uncertainty and disagreement. Why—despite huge and ever-expanding bodies of relevant scientific research—is it so impossible to resolve disagreements around climate change, nuclear energy, mammograms, K–12 public education, chemicals in the environment, genetically modified organisms, nutritional guidelines, trade policy, and on and on? Why, despite all the research and expertise, do the opposing sides of these debates remain fixed in their values and interests, certain in their own version of the facts and immovable in their sense of what should or should not be done?

Thanks to the COVID-19 pandemic, the relation between science and politics is now at the center of the world stage. The novel coronavirus offers up a powerful and extremely clear lesson about the appropriate role of science in helping to guide us toward a better future—a lesson that sharply contradicts standard thinking about science and politics. Above all, we are learning that science’s place in politics is determined not by the logic of facts, but by the fundamental influence of human values. To understand why, we have to start by recognizing how the COVID-19 crisis differs in almost every important respect from more familiar controversies at the intersection of science and politics.

For once, we all agree.

Most importantly: The COVID-19 threat is immediate, global, and existential. The protection of one’s own life depends on the protection of the lives of others. We are thus unified by the shared value of preserving life, which in turn means we are all actually talking about the same thing when we talk about the COVID crisis. A similar condition of value convergence emerges during time of war, but because the threat now is a pandemic virus and not an enemy nation, the desired goal of preventing loss of life is universally shared. My point is not that COVID-19 signals the dawning of the age of Aquarius—the battles in Congress this week make it clear that rancor remains. It’s that as the reality of what we are facing sinks in, people everywhere are showing that they are increasingly willing to put their immediate interests and conflicting values aside in the service of achieving a much larger, shared goal of slowing the pandemic.

We can see what caused the crisis.

There’s a second reason we are talking about the same thing: Causation can be attributed. Some wacko conspiracy theorists notwithstanding, the causal link between the novel coronavirus and the emergence of a new strain of potentially acute respiratory illness is clear, as is the exponential increase in both disease incidence and deaths resulting from the illness. Uncertainties about ease of transmission, asymptomatic cases, and misdiagnoses do not undermine knowledge of the fundamental chain of causation, which is simple, linear, and unmistakable: The virus is identified, people are getting sick, hospitals are filling up, patients are dying, and the number of deaths can be counted and communicated unequivocally.

The facts, that is, are being made authoritative not through scientists telling us what to believe about an invisible virus, but by occurrences in the real world, visible for all to see. If a researcher claims that a certain chemical in the environment, like the glyphosate in Roundup, will cause a certain number of increased cancer deaths per year or that a particular economic policy will lead to a certain number of new jobs, in most cases no one will ever be able to confirm that prediction. Even if the mechanism by which the chemical causes some variety of cancer is clear in lab rats, it is likely to have many plausible causes in humans. Even if the new jobs do appear, the cause might be trade decisions made by other countries or the expansion of new industries. In the years that might be necessary to test such claims (though usually they cannot be tested), other researchers may come up with entirely new explanations. No wonder scientific and political debates about such matters never seem to end. But for COVID-19, the basic scientific inferences quickly play out—through changing incidence of the disease and its consequences—in ways that allow both scientists and the public to assess the current level of scientific understanding and the facts on the ground.

The facts are good enough.

What we know now has to be the basis for action. The facts have to be good enough—even if some of them turn out to be wrong. We can’t wait around for more research. We can watch learning occur in real time as the consequences of actions are assessed and interpreted. For example, as I was writing the first draft of this essay, new evidence from Italy was showing that death rates were for the first time beginning to fall below the exponential curve that previous death rates were following. Fact: The nation’s radical social isolation policies were beginning to take hold. But a day later, death rates were on the rise again. Fact: Apparently the policies weren’t making a difference, at least not yet. What makes the science good enough on any given day is that we need it to be good enough if we are to act on behalf of the values we share. We don’t use the facts because they are right, but because they are what we have, and because we must act, and because we all want the same thing.

Indeed, scientists and policymakers are for the most part being open about the significant uncertainties surrounding the disease and its future course. These uncertainties range from basic facts about the virus (how will it behave in warmer weather?) to inferences about the course of the disease (how many undiagnosed cases are out there? What’s the fatality rate?) to predictions about how policies (like social isolation) will slow the course of the pandemic. This openness about uncertainties may appear ironic or paradoxical; after all, if the experts are so worried, shouldn’t they hush up the uncertainties so as not to undermine the need for urgency and compliance?

Yet the speed with which the pandemic is advancing and policies are being enacted also means that the consequences of decisions are likely to be revealed fairly quickly. Unfolding events will help reduce uncertainties and improve learning about what works and what doesn’t. These conditions rarely hold for the more conventional sorts of controversies I’ve mentioned, where causal inferences are often impossible to validate, and facts, decisions, and consequences—and the links among them—are themselves mired in controversy and disagreement. But with COVID-19, convergent values about what we want to accomplish means that uncertainty (about the science and about the decisions that are being be taken) does not block action—everyone agrees both on the need to act and on the desired goal. Scientists share those values (they’re people too!), so even if they disagree over aspects of the disease and its mitigation, they may not need to feel compelled to overstate the certainty of their results and beliefs, unlike more conventional interactions between science and politics, where competing sides enlist their own experts who then have a strong incentive to speak with more than warranted certainty.

Models can be used appropriately.

For many problems at the intersection of science and policy, scientists use mathematical models to make inferences about the future, for time periods ranging from decades to centuries or more: How can new energy technologies best be deployed to reduce greenhouse gas emissions? How will nuclear waste behave in a geological repository over coming millennia? How much will economic productivity increase if more investments are made in research? But such questions always involve enormous uncertainties, and the models used to try to answer them are laden with assumptions about more basic questions that are themselves unanswerable: How will the price of solar panels change in the coming decades? How many centuries will it take for groundwater to corrode the nuclear waste storage vessels? How efficiently do universities create economically valuable knowledge? Different assumptions about these sorts of questions allow models to fuzz the boundary between science and politics by providing competing views of the future, in support of competing political agendas.

While epidemiological models used for predicting the future of COVID-19 are also assumption-laden and highly uncertain, they can be constantly tested and refined based on data that is emerging on a daily basis, to accomplish what everyone agrees must be done. For the most part, models are being used to help put boundaries around the range of plausible futures that we face, and we can see different versions of these futures unfold as different countries implement different policies at different speeds. The models are valuable because they allow us to test our assumptions about both the behavior of the virus and the impacts of different policy approaches, in real time. They are not crystal balls deployed to make the case for one preferred future or another, but navigation charts that help us narrow the plausible pathways to the future that we all hope for.

Political agendas fall away.

Complex policy issues around problems as diverse as K–12 education, climate change, health care, and immigration are all accompanied by a diversity of ideological and political sub-agendas that rarely get articulated yet may importantly influence why certain positions are supported or opposed. Different ideological theories—for example, about the role of government versus the private sector in problem-solving—are available to support competing interests, and they justify disagreement about what actions should be taken. Disagreement can be sustained because no one really knows what to do, because short-term testing of alternative policies is impossible. The problems are so complex that even defining them is controversial. Is climate change a problem of lifestyle or technological innovation or population growth? Is poor K–12 public education a reflection of underpaid teachers and inadequate government investment, or too powerful teachers unions and insufficient focus on the basics, or racial and economic inequalities whose origins go deeper than anything that can be resolved at the level of school reform?

Your view on what counts as a policy solution will reflect your underlying ideological beliefs about what causes the problem. We’re seeing this dynamic play out in spades right now as the Senate battles over an economic response package, Republicans and Democrats intent on spending the needed billions in ways that advance their ideological beliefs and political constituencies.

But when it comes to fighting COVID itself, rather than fixing the economy, the combination of shared values and clear chains of causation makes it tough to import second-order political agendas into debates about what actions to take—despite the ongoing and acknowledged uncertainties. Politicians as ideologically distinct as New York Mayor Bill de Blasio, a liberal Democrat, and Ohio Gov. Mike DeWine, a conservative Republican, are implementing essentially equivalent strategies for addressing the pandemic. While President Donald Trump is at the moment threatening to loosen up social distancing rules, his spasmodic approach to pandemic policies isn’t turning out to be significantly different from that of many other national political leaders. For this crisis, the things that unite us are outranking those that divide us; pandering and opportunism, while never absent from politics, are being brought to heel by the pincer combination of shared values and facts on the ground.

COVID-19 is a hard problem, but not a complex one. We know what COVID-19 is because we see it around us. Experts, in expressing their deep concerns, are also talking candidly about the great uncertainties and exercising humility. Politicians are nonetheless listening to experts and taking action. They are, on the whole, acting for the common good. The tired, unhelpful, ever-wishful tropes of “evidence-based policy” and “political will” actually seem to have some meaning under these special conditions.

None of this is to say that catastrophe can or will be averted. But we can say that the threat of COVID-19 is bringing out the best in both science and politics. The lesson is not that we need to always listen to experts and that science will show us the way to go. It’s that a shared sense of our commonality as humans is the essential condition of a society that has the tools to deal with its problems. Common values, not expert assertions about facts, are what make science good enough to act on. Whether this message carries into the post-COVID world, we will someday see. But if it does not, no amount of research or expertise will ever take its place.

This article also appears on the website of Issues in Science and Technology.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society.