In the Fall of 2020, my colleague, Prof. Immaculata De Vivo of the Harvard School of Public Health, and I wrote an essay about the public perception of risk and uncertainty, especially with regard to COVID-19. In this post, we are gathering comments from students in the Spring 2021 edition of "GenEd 1112: The Past and Present of the Future," an undergraduate course I teach at Harvard. Students were asked to read the essay, and then comment here on which part(s) of the discussion they expect would be most illuminating for non-quantitatively-inclined readers --and/or to suggest another framing of the issues discussed that would be more effective.
I think the most interesting aspect of our discussion would be the general differences between risk and uncertainty, as discussed in the essay. As someone who isn't quantitatively inclined (despite the valiant efforts of this course), the essay did an excellent job in highlighting the distinction between these two concepts in a way that can be followed by those who struggle with mathematics. I think what will also be interesting is discussing how these concepts relate to decision making. If you have a low risk but high uncertainty situation, your choice of actions can go in a divvy of directions. While prediction to decision seems to be the most straightforward way of seeing things, I think risk and uncertainty play into our decision making in a way that is more stressful than predictions.
As a non-quantitatively inclined reader, I found the paragraph that contains the most numbers, in which you discuss the current uncertainty in the risk of dying from COVID-19, most interesting and helpful. In this paragraph, you describe different projections for COVID-19 related deaths and what that means for the uncertainty in the risk. Ultimately, this kind of reasoning relies on very simple, straightforward math that I find easy to follow. I haven't come across a clear description of what uncertainty means in terms of risk of dying from COVID-19, even with all of the articles I've read since the pandemic started. I think news outlets might shy away from this type of mathematical reasoning about risk because it could scare off readers. This essay shows that the math involved in calculating uncertainty in risk can be quite simple to explain and understand.
@William Foulkes i’m so glad you feel this way, as showing how simple math can help with understanding risk was exactly our goal in creating this essay. We are considering re-submitting it and updating it to another publication, so it’s good to know that it can accomplish its goal.
I think that the three paragraphs starting with "Looking at current projections for March 1, 2021--likely just before widespread vaccine distribution--" and ending with "and people misunderstand uncertainty" are the most illuminating paragraphs for non-quantitatively inclined people. This passage breaks down raw pandemic data and contextualizes it using jargon-free language and historical examples that most lay readers could understand. One of the biggest sources of misleading information were sensational news articles that provided raw statistics and "what-if" scenarios without contextualizing the data in terms of certainty, historical examples, or warranted level of concern. This article demonstrates that one can explain risk and uncertainty to lay readers in a digestible format that fully acknowledges the danger of the pandemic without causing undue anxiety about possible but exceedingly unlikely scenarios.
@Cici Williams thanks! After reading your comment and some others, I am encouraged to resubmit an updated version of this article somewhere to offer it to a wider public.