With a Bayesian perspective, the uncertainty is coded in randomness. The researchers initially assumed that the number of reproductions had different distributions (the priorities). Then they modeled the uncertainty using a random variable that fluctuates and takes a range of values from only 0.6 and 2.2 or 3.5. In a nesting process, the random variable itself has parameters that fluctuate randomly. and these parameters also have random parameters (hyperparameters) etc. The effects accumulate in a “Bayesian hierarchy” – “turtles at the bottom,” said Dr. Holmes.
The effects of all these random ups and downs multiply like compound interest. As a result, the study found that the use of random numbers for reproductive numbers more realistically predicts the risky tail events, the rarer they are more significant superspreader events.
“People, including very young children, can and do subconsciously with Bayesian conclusions,” said Alison Gopnik, a psychologist at the University of California at Berkeley. “But they need direct evidence of the frequency of events to do so.”
Much of the information that governs our behavior in the context of Covid-19 is probabilistic. For example from some estimatesIf you are infected with the coronavirus, there is a 1 percent chance that you will die. In reality, however, an individual’s chances can vary by a a thousand times or more, depending on age and other factors. “With something like an illness, most of the evidence is usually indirect, and people are very hard to deal with explicit probabilistic information,” said Dr. Gopnik.
Even with evidence, it’s not easy to revise beliefs. The scientific community has sought to update its priorities about asymptomatic transmission of Covid-19, even if it has been found to be a factor and that masks are a helpful preventive measure. This probably contributed to sluggish response of the world to the virus.
“The problems arise if we don’t update,” said David Spiegelhalter, statistician and chair of the Winton Center for Risk and Evidence Communication at Cambridge University. “You can interpret affirmative bias and so many ways we react badly by being too slow to revise our beliefs.”