If you read two articles this week, let it be the following two pieces.
A few sentences have shaken a century of science.
A week ago, more than a year after the World Health Organization declared that we face a pandemic, a page on its website titled “Coronavirus Disease (Covid-19): How Is It Transmitted?” got a seemingly small update.
The agency’s response to that question had been that “current evidence suggests that the main way the virus spreads is by respiratory droplets” — which are expelled from the mouth and quickly fall to the ground — “among people who are in close contact with each other.”
The revised response still emphasizes transmission in close contact but now says it may be via aerosols — smaller respiratory particles that can float — as well as droplets. It also adds a reason the virus can also be transmitted “in poorly ventilated and/or crowded indoor settings,” saying this is because “aerosols remain suspended in the air or travel farther than 1 meter.”
The change didn’t get a lot of attention. There was no news conference, no big announcement.
But mostly Kahneman credits chance for the success of Thinking, Fast and Slow. Sometimes a book catches on and its popularity becomes a self-reinforcing loop. Things could easily have turned out differently. It is a modest claim, but it does neatly lead us to his new book, Noise, written with Olivier Sibony, a business school professor and former McKinsey partner, and Cass Sunstein, who is a law professor, co-author of Nudge and has been an official in both the Obama and Biden administrations.
Kahneman has spent much of his life studying bias in decision-making, but noise is the other source of error. If you imagine firing arrows at a paper target, bias would be a systematic tendency for the arrows to land (say) below the bullseye. Noise would be a tendency for the arrows to err in any direction, purely at random. In some ways, noise is easier to detect. You can measure it from the back of the target, without knowing where the bullseye is. And yet noise is often overlooked.
From the viewpoint of a social scientist, this oversight is understandable. Bias feels like the thing to observe, while noise is the fog obscuring the view. Experimental methods are designed to remove noise to allow bias to be measured more clearly.