At the end of March, SFR staff writer Katherine Lewin, a Report for America fellow, interviewed me for the paper's "Reported" podcast on the role of journalism during crises such as COVID-19. I can't bear to listen to my own voice at the moment (I hear it enough while talking to myself at home and transcribing interviews), but recall mentioning that the current coronavirus outbreak is just as subject to confirmation bias as any other news event.
By confirmation bias, I'm referring to the phenomena by which people choose to cherry pick or remember only information that confirms what they already believe. And in this particular case, I mean the growing COVID-19 conspiracy theories in which, as is usual with conspiracy theories, a complex network of government, tech and private entities have devised this virus as a means of enslaving the human race. I have no idea why this scenario is somehow more appealing than the actual situation, which I think is factually complex and alarming enough.
In a recent Texas State University article, Twister Marquiss, a senior lecturer and director of the school's Common Experience program, says that often people who subscribe to conspiracy theories, "want to believe there is a structure to the world, that there's an order that's easy to follow, even if it's complex." The danger, he says, is that doing so "creates a false understanding about an event or about people. It can feed stereotypes. It can feed racism. It can feed xenophobia." (Marquiss also has a compelling Twitter thread on this topic, relating conspiracy theories to pre-modern narrative structure, which made this former English grad student ridiculously happy).
Moreover, sociology professor Nathan Pino says in the same article, "Those who believe in conspiracy theories often erroneously believe they are exercising independent critical thinking. They believe they're thinking outside of the box. The fact is their critical thinking skills are not up to par and they're not weighing evidence."
Lastly, Department of Philosophy lecturer Elizabeth Kanon differentiates concisely the difference between conspiracy and scientific theories, noting: "What marks a scientific theory is the possibility that it could be replaced by a better theory down the road. New evidence will test the theory to see if it will fail. Most conspiracy theories are not like that. They use every piece of evidence as confirmation of the conspiracy."
Long before this pandemic, I've grown used to calling on the Santa Fe Institute for scientific insights, so it was natural after falling down a dispiriting rabbit hole of nonsensical paranoid posts about COVID-19, I turned there to see what its scientists are saying about the current situation. I plan to follow up in the coming weeks with more of the work that is happening there, but plenty of information is already available to the public.
For example, last week, I sat in on a Zoom workshop, "After the First Wave," in which more than 130 researchers heard presentations from scientists in the field who are actively working on modeling the COVID-19 outbreak (you can also watch it on SFI's website, www.santafe.edu)
One of them, Lauren Ancel Meyers, a professor of Integrative Biology at the University of Texas at Austin and a member of SFI's External Faculty and Scientific Advisory Board, recently co-authored a new paper paper highlighted in an April 3 New York Times story mapping estimated "hidden transmissions" of the virus. The study estimates, among other statistics, "70 percent of all counties in the United States—making up 94 percent of the country's population—are likely to have epidemics." Santa Fe County, by the way, was among them. In fact, the story's mapped data shows we have a 100% probability of already having one here.
In welcoming the virtual group, co-organizer Sam Scarpino, an assistant professor in network science at Northwestern University and former SFI post-doc, noted one of the reasons for the gathering was: "Santa Fe is a very special place for meetings like this. Since the early 1980s, it has been a hub of complexity science gathering researchers across the disciplines to tackle some of the world's most challenging problems, one of which is pandemics; pandemics both from the sense of trying to understand their ecology, their evolution, their population dynamics, their epidemiology, but also to understand the feedback on our economies, on our global societies, on our mental and physical well being."
To that end, the scientists are working on how to approach the myriad challenges we will all face after this first wave.
For similar reasons, SFI also has launched a new article series titled "Transmissions" intended to introduce "ideas from complexity science [including] why systems collapse, the nature of an evolving virus and its ecology, how networks spread disease and economic instability, the mathematics of modeling outbreaks, the way decision-making modifies disease spread, and many other ideas that touch on the disease," SFI President David Krakauer writes in his introduction.
Two batches of these articles include scientists considering timely topics such as how to reduce mortality from COVID-19 while easing "economic decline" and one that examines and argues for the use of disease modeling in guiding policy (and as an added bonus, I am sure by the time I finish reading and understanding many of these essays, we will definitely be over the peak…it may even be next year).
Ever the English major, I gravitated toward first reading a piece by SFI Davis Professor of Complexity Melanie Mitchell (also a professor of Computer Science at Portland State University who lectured in Santa Fe last year on artificial intelligence). Mitchell writes:
"The way people conceptualize a situation drives their behavior in that situation. This means that the analogies we use to make sense of new situations can be powerful forces—for better or worse—in determining how we act."
I thought of Mitchell's essay as I reluctantly turned, again, to looking at some of the more disturbing posts I'd seen in the last week postulating various pandemic conspiracies (none of which I am going to repeat here). I looked for analogies but mostly found metaphors and hyperbole: "lab rats," "rabbit holes" and "villain class," for example.
I wondered why I found irrational paranoia more worrisome than the actually worrisome data scientists such as Meyers are producing.
The answer, of course, is simple. Science will lead to solutions; conspiracy theories, by design, will lead nowhere.