I have a friend who makes a (great) living as a professional gambler. He’s originally from outside the US, but was living there during 2016 and, like everyone else in our friend group, started following the election. Since he loves to gamble, he also decided to bet on the election.
I’m not sure what his political leanings are exactly, but he’s a sensible guy and made fun of Trump like the rest of us. But he also ended up betting on Trump to win, and won enough money to pay for a trip to Houston to watch the Super Bowl live in January (flights+hotel+tickets...I told you he does well).
When I asked him why he bet on Trump, he said he didn’t really understand American politics, but that it seemed extremely complicated, with random things like whether it rains in certain parts of Pennsylvania potentially being important. If it’s extremely complicated and arbitrary, then it’s probably also hard to model. The projections he saw had the most likely outcome being a moderate win by Hilary, but since he didn’t trust the models, he figured the value was in the tails: the models are likely off, which likely affects the tails more than the mean. So a landslide win by Hilary or a Trump victory seemed undervalued by gambling markets. He wished he could hedge his bets by betting on both outcomes (as opposed to a moderate Hilary win), but the only option he had was to bet on Trump to win.
This logic has stuck with me ever since: in the face of large uncertainty, we’re probably getting the tails wrong more than the mean. The value is in the tails.
For a fast-moving, highly uncertain crisis—like COVID-19 --the value being in the tails means that we’re probably underestimating the risk. We’re probably getting the worst-case scenarios, and the odds of them happening, wrong, and we should be weighing our expectations more heavily towards worst-case scenarios than what models, expert assessments, etc. tell us. The virus could suddenly mutate (like SARS did in a good way), there could be compounding natural disasters, the economic recovery could look more like an L than a U.
I don’t claim to know how we should be responding to the epidemic, in terms of stopping the spread of the virus or combating its economic impacts, but whatever we do, we should assume the virus’ damage will be worse than we think.
The Value Is In the Tails
I have a friend who makes a (great) living as a professional gambler. He’s originally from outside the US, but was living there during 2016 and, like everyone else in our friend group, started following the election. Since he loves to gamble, he also decided to bet on the election.
I’m not sure what his political leanings are exactly, but he’s a sensible guy and made fun of Trump like the rest of us. But he also ended up betting on Trump to win, and won enough money to pay for a trip to Houston to watch the Super Bowl live in January (flights+hotel+tickets...I told you he does well).
When I asked him why he bet on Trump, he said he didn’t really understand American politics, but that it seemed extremely complicated, with random things like whether it rains in certain parts of Pennsylvania potentially being important. If it’s extremely complicated and arbitrary, then it’s probably also hard to model. The projections he saw had the most likely outcome being a moderate win by Hilary, but since he didn’t trust the models, he figured the value was in the tails: the models are likely off, which likely affects the tails more than the mean. So a landslide win by Hilary or a Trump victory seemed undervalued by gambling markets. He wished he could hedge his bets by betting on both outcomes (as opposed to a moderate Hilary win), but the only option he had was to bet on Trump to win.
This logic has stuck with me ever since: in the face of large uncertainty, we’re probably getting the tails wrong more than the mean. The value is in the tails.
For a fast-moving, highly uncertain crisis—like COVID-19 --the value being in the tails means that we’re probably underestimating the risk. We’re probably getting the worst-case scenarios, and the odds of them happening, wrong, and we should be weighing our expectations more heavily towards worst-case scenarios than what models, expert assessments, etc. tell us. The virus could suddenly mutate (like SARS did in a good way), there could be compounding natural disasters, the economic recovery could look more like an L than a U.
I don’t claim to know how we should be responding to the epidemic, in terms of stopping the spread of the virus or combating its economic impacts, but whatever we do, we should assume the virus’ damage will be worse than we think.