Should I Listen to the Science or the Data?
First of all, let me say that I don’t know of any local politician that is prepared to deal with a virus pandemic or any other type of worldwide disaster. Most local politicians are clearly out of their league in trying to deal with global issues. That said, are there any useful skills that politicians could use to reduce the impact of a virus pandemic. You might think a politician with a medical background would be useful, but this would only help in understanding the descriptive nature of the virus; the type of virus, how it’s transmitted, and the types of outcomes from being infected. In other words, “listen to the science”. What would be missing is how to deal with the uncertainty or risk with the virus. What is the probability that I could become infected and what is the probability distribution of outcomes from being infected? Should I instead “listen to the probabilities and the data?”
In the field of economics, a distinction is made between risk and uncertainty. Risk refers to making decisions when you know the probability distribution of all possible outcomes. For example, when dice are rolled in a craps game, everyone assumes that the dice are fair and the outcomes follow a given probability distribution. Making decisions under uncertainty is where you may know the outcomes but not the probabilities. For example, what is the probability of an earthquake of magnitude 7.0 occurring next month in your city?
To see how risk and uncertainty work, let’s simplify things and assume you are asked to play a game. If you play there is a possibility of you dying or being injured. If you don’t play, nothing bad happens. The table below shows the possible outcomes and payoffs from playing the game. The payoff of doing nothing is fixed, but less than the amount you get if not killed or injured. There has to be some incentive to play the game.
_______________Outcomes_________________Min.____Max._____Exp.
Action---------------------Death-----Injury-----Safe-----Payoff-----Regret-----Value
A1________Play_____0_______50____100______0______60______99.63
A2_____Not Play____60_______60_____60_____60______40_______60
__________________________________Choose A2----------A2
Without knowing the probabilities there are a couple of methods used by decision-makers that are risk-averse. One method is the maximin criterion, also known as the criterion of pessimism, which chooses the action with the highest minimum. The Min. Payoff column shows that Not Play will be chosen.
A second method to deal with uncertainty is the regret criterion. Regret is defined as the difference between the outcome with the highest payoff and the other payoffs for the other actions. In other words, if you knew you would die you would regret your decision to play by 60. If you knew you would be safe you would regret your decision to not play by 40. The Max. Regret column shows that to minimize regret you would choose not to play.
It seems that if you are faced with uncertainty, it might make sense to not play the game. This represents the political and medical advice given to people to isolate themselves at the beginning of the Covid-19 outbreak. The medical experts told us the virus was highly contagious, you could die, you could get very sick, or be infected and show little or no serious symptoms. These experts did not appear to know the probabilities of these events. Granted these experts had studied previous virus outbreaks, but in this case, it seemed that the past could not be relied upon. Without knowing the probability distribution, decision making becomes very conservative with a preference for outcomes that would minimize risk or regret.
However, after observing the statistical data for several months and millions of cases, the probability distribution becomes more certain in terms of assigning probabilities to potential outcomes. In the example above, playing the game is driving your vehicle to a preferred destination. Based on national transportation statistics, the probability of dying during the year is 0.011% (11 deaths per 100,000 drivers) and the probability of an injury is 0.716%. Of course, you could modify the payoffs and adjust the probabilities to suit your own personal assessment of your driving skill, but in the end, an overwhelming number of people choose to drive. They are willing to take a very small risk of dying to obtain more satisfaction from driving to a destination and enjoying whatever it is they want. The last column of the above table reflects the expected long-run payoff from choosing to drive, which is slightly below the maximum payoff because the probability of death or injury is so small.
Unfortunately, politicians and medical experts still act as if the probability distribution is unknown and still recommend conservative actions. They still recommend isolation for the majority of people. However, it now appears that the risk of death from being infected is extremely low for individuals younger than 65 (0.0214% or 2.14 per 10,000 people less than 65), and significantly higher for older people (0.45% or 45 per 10,000 over 65).* Of the 301,679 deaths reported as of December 30, 2020, the CDC reports that 80% of the people who die are 65 and older. With more information about the probability distribution, we should be listening to the statistical data and not to medical experts that just tell us we could die from the virus.
People have different tolerances toward risk. We would be a lot better off listening to the data than listening to experts that just tell us we could die.
*Based on the weekly CDC estimates of December 30, 2020. https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm#AgeAndSex
