These feedback loops are further complicated by the asymmetry in the way we see information and integrate it into our behavior as individuals. Optimists can update their information as part of optimistic update bias (to take more risks). Pessimists may be more risk averse even when presented with an “optimistic” model. It’s no different from confirmation bias. Our behavior also depends on epistemic trust: if we decide to trust one expert forecast rather than another enough to change our minds and behavior. This recently arose with the push back against a controversial article in Atlantic, written by an economist, on the risks of transmission of Covid-19 in children.
Science, and epidemiology in particular, is concerned with measurement and truth. Accurate models are important. But at time A, if a group of individuals listens to the worst case / pessimistic / precautionary principle model, the probability of the worst case actually happening may decrease following a change in the group’s behavior to minimize risks. The reverse is also true: at the same time, if a group of individuals listens to the “dynamic causal” / optimistic model and changes their behavior to be more liberal, the model changes. towards the worst of cases.
“The pandemic forecast is similar to the weather forecast, which is good for a 10-day perspective, but I couldn’t tell you what the weather will be like in the third week of July,” Lessler told me. With infectious disease, “we can’t say what will happen in three months, because we have feedback loops with politics and behavior and the uncertainty in the underlying data.”
Back to J: In situation 1, he may decide to take this pessimistic model as a helping hand to quit smoking. The reverse can happen in situation 2. Ideally, her doctor would share the two screenings, and it would be up to J to weigh the two options.
Public health is more delicate, because the decisions made by the individual have repercussions on their community. It can be argued that it is better to be over-prepared and over-cautious than under, where millions of lives are at risk, even though the externalities to individual freedoms and the economy are also significant and impact our choices and our risk assessment.
Here’s the good news: Over time, the forecasting models of both optimists and pessimists may appear Converge. So the scenario and the dynamic causal models are, in a sense, correct: overall and gradually, we tend to make more accurate predictions together. This suggests that once the number of cases drops, the patterns will look the same, signaling the end of the pandemic or simply appearing to reflect it. Lessler then shared in an email: “All the models arrive at a very low case destination. It’s just a matter of timing and what happens along the way. “
As such, a more “pragmatic” perspective, which advocates the continued use of masks, vaccines, and social distancing, may best lead to an optimistic outcome of herd immunity and life returning to “normal”. more enjoyable later this year.
When I ran a Twitter poll earlier this month, more than two thirds of some 700 people questioned seem to be of the more optimistic opinion that in North America, the end of the pandemic is near. At first I felt relieved, but then realized that this view could lead to more pessimistic results if that same optimism dictated less cautious behavior. Instead, carefully balance evidence-based pessimism in the present, with the idea that it could give rise to a raison d’être optimistic in the future, maybe it would be better to dictate the behavior so that we can go out together, like the others seems to be. Which is another way to sum up recent words from writer Ezra Klein Tweeter: “Hope looks like a dangerous emotion lately. Personally and professionally, I don’t want to be optimistic to be crushed as deaths increase. Pessimism is more certain. “
Perhaps pragmatism, with a healthy dose of tolerance for change and uncertainty, is even safer.
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