Wouldn’t you like to know.
“The pandemic has exposed the messiness of science. … We all want answers today, and science is not going to give them. … Science is uncertainty. And the pace of uncertainty reduction in science is way slower than the pace of a pandemic.” —Brian Nosek
The math of probability is manageable, up to a point. In principle, you could calculate the odds of, for example (clockwise from top left) getting heads on the fake Trump coin, a novel coronavirus linking up to human proteins, surviving a round of Russian roulette, having someone with COVID-19 at your planned event, rolling 6-6-4-8-20 on the dice, have all the marbles fall under a normal curve on a Galton board, and then surviving a flight from New York to Los Angeles without hitting one of the thousands of other planes in the air. But that doesn’t mean we can tell where humanity will be a year from now.
Of course things are always too complicated to predict perfectly, but “normally” we bracket uncertainty and make simplifying assumptions so we can work with forecasts of say, tomorrow’s weather or next quarter’s economic growth — which are strongly bounded by past experience. These are “the models” we live by. The problem now is not that reality has become objectively harder to predict, it’s that the uncertainties in the exercise (those most relevant to our lives) involve events with such catastrophic consequences that a normal level of uncertainty now includes outcomes so extreme that we can’t process them meaningfully.
Once nominally predicable events start influencing each other in complex ways, the uncertainty grows beyond the capacity of simple math. Instead of crunching every possibility, we simplify the assumptions, based on past experience and the outcomes we consider possible. Today’s would-be predictions, however, involve giant centrifugal forces, so that small deviations can lead to disintegration. For example, if the pandemic further tanks the economy, which provides more unemployed people to populate mass protests, leading to more military crackdown and turning more people against Trump, it might make him lash out more at China, and then they might not share their vaccine with us, and an epidemic wave could overwhelm the election and its aftermath, giving Trump a pretext for nullifying the results. And so on.
To make matters more ungraspable, we personally want to know what’s going on at the intersection of micro and macro forces, where we don’t have the data to use even if we knew what to do with it.
For example, as individuals try to ballbark their own risks of covid-19 infection, and the likelihood of a serious outcome in that event — given their own health history — they might also want to consider whether they have been exposed to tear gas at the hands of police or the military, which might increase the chance of infection. In that case, both the individual and the state are acting without quantifiable information on the risks. For another example, Black people in America obviously have a reasonable fear of police violence — with potentially immeasurable consequences — but taking the risk to participate in protests might contribute to political changes that end up reducing that risk (for themselves and their loved ones). The personal risks are affected by policy decisions and organizational practices, but you can’t predict (much less control) the outcomes.
Individual risks are affected by group positions, of course, creating diverging profiles that splinter out to the individual level. Here’s an example: race and widowhood. We all know that as a married couple ages, the chance that one of the partners will die increases. But that relationship between age and widowhood differs markedly between Blacks and Whites, as this figure shows:
Before age 70, the annual probability of a Black woman being widowed is more than twice the chance that White women face. (After that, the odds are higher, but not as dramatically so.) Is this difference big enough to affect people’s decision making, their emotions, their relationships? I think so, though I can’t prove it. Even if people don’t map out the calculation at this level (though they of course think about their own and their partner’s specific health situation), it’s in there somewhere.
For most people, widowhood presents a pretty low annual probability of a very bad event, one that might turn your life upside down. On the other hand, climate change is certain, and observable over the course of a contemporary adult’s lifetime (look at the figure below, from 1980 to 2020). But although climate change presents potentially catastrophic consequences, the risks aren’t easily incorporated into life choices. If you’re lucky, you might have to think about the pros and cons of owning beachfront property. Or you could be losing a coal job, or gaining a windmill job. But I think for most people in the U.S. it’s in the category of background risk — which might motivate political participation, for example, but doesn’t hang over one’s head as a sense of life-threatening risk.
If not imminent fear, however, climate change undoubtedly contributes to a climate of uncertainty about the future. Interestingly, there is a robust debate about whether and how climate change is also increasing climate variability. Rising temperatures alone would create more bad storms, floods, and droughts, but more temperature fluctuation would also have additional consequences. I was interested to read this paper which showed models predicting greater change in temperature variability (on the y-axis) for the rest of the century in countries with lower per-capita income (x-axis). When it comes to inequality, it rains and it pours. And for people in poorer countries of the world, it’s raining uncertainty.
What comes next
I wrote about unequal uncertainties in April, and possible impacts on marriage rates, and I’ve commented elsewhere on fertility and family violence. But I’m not making a lot of predictions. Are other social scientists? My impression they’re mostly wisely holding off. My sense is also that this may be part of a longer-term pattern, where social scientists once made more definitive predictions with less sophisticated models than we do now that we’re buried in data. Is it the abundance of data that makes predicting seem like a bad business? I don’t think that’s it. I think it’s the diminished general confidence in the overall direction of social change. Or maybe predictions have just become more narrow — less world revolution and more fourth quarter corn prices.
One of the books I haven’t written yet, crappily titled Craptastic when I pitched it in 2017, would address this:
My theory for Craptastic is that the catastrophic thinking and uncontrollable feelings of impending doom go beyond the very reasonable reaction to the Trump shitshow that any concerned person would have, and reflect a sense that things are turning around in a suddenly serious way, rupturing what Anthony Giddens describes as the progress narratives of modernity people use to organize their identities. People thought things were sort of going to keep getting better, arc of the moral universe and all that, but suddenly they realize what a naive fantasy that was. It’s not just terrible, it’s craptastic. …
I suspect that if America lives to see this chapter of its decline written, Trump will not be as big a part of the story as it seems he is right now. And that impending realization is one reason for the Trump-inspired dysphoria that so many people are feeling.
Social science is unlikely anytime soon to be the source of reassurance about the future some people might be looking for — not even the reassurance that things will get better, but just confidence that we know what direction we’re headed, and at what speed. I don’t know, but if you know, feel free to leave it in the comments. (Which are moderated.)