THE TL;DR KEY POINTS
We all make countless judgments, and our important life decisions depend on them.
My new book, “Human Judgment”, investigates these judgments, and it is now available to purchase online here.
The book concerns two topics to do with human judgment, as implied by the subtitle: How accurate is it, and how can it get better?
It has two somewhat newsworthy items, one bad and the other good.
The bad news is that the science suggests that human judgment is often much more inaccurate than we might hope or expect. For example, some researchers estimated as many as 40,000 to 80,000 US citizens will die because of preventable misdiagnoses—and that’s each year. If they are right, that’s a yearly death toll at least 13 times higher than the September 11th terrorist attacks. Unfortunately, medicine is not unique too: judgmental inaccuracy can afflict a number of other areas in society as well. As another example, some researchers estimate at least 4.1% of death sentence convictions in the US are actually false convictions; this implies that some people are trialed, convicted and executed for horrific crimes that they never actually committed. So that is a few of numerous studies painting a less than ideal picture of human judgment: we make inaccurate judgments about medical diagnoses, about criminal convictions and about a number of other areas.
the tl;dr key points
THE BAYESIAN CALCULATOR: WHY YOU SHOULD CARE ABOUT IT
Tomorrow, I'll be giving my last lecture on Bayesianism for the course "Phil 60: Introduction to Philosophy of Science" at Stanford University.
There, I'll be talking about a Bayesian solution to the problem of underdetermination, associated with Pierre Duhem and Willard van Orman Quine.
The problem essentially concerns the limited ability of evidence to support or rule out isolated hypotheses. For example, if you run an experiment to test whether a putative piece of iron melts at 1538 degrees Celsius, and the piece doesn't melt at that temperature, then you have at least two possible responses: you could rule out the hypothesis that iron melts at 1538 degrees Celsius, or you could instead rule out the hypothesis that the piece of metal was actually iron as opposed to another substance. As Duhem put it, the experiment itself does not tell you which specific hypothesis is false:
The TL;DR key points
2. When we do this, people are better forecasters than it initially appeared
3. And we are able to explain and predict accuracy better than it initially appeared
Good Judgment: Why you should care about it
We all make judgments every day. We all depend on them to make decisions and to live our lives. You might think someone is a good partner for you, and so you might marry them. Or you might think you will be happy in a particular career, and so you might spend countless hours of your life studying and working your way towards it.
But what happens if your judgments are wrong—if the person you married or the career you chose weren't good options?
We all know that this kind of thing happens: people make bad judgments and regret their decisions all the time. That is old news—and bad news, at that. What’s more, if we take a passing glance at the scientific study of reasoning, we’ll see that we are often biased in our judgments and we may not even realize it (check out Kahneman's fantastic book, for instance).
But there is good news: we can improve our judgments!
The TL;DR key points
2. Know our biases, such as overconfidence and availability biases
3. Use statistics, even simple ones
Estimating risk: Why you should care about it
Nowadays, we’re especially worried about risks—about the risk of getting COVID if we hop on a plane or go to an in-person class, or about the risk of dying if we get COVID. And some risks are worth taking, but others aren't; it depends partly on how we estimate the risks.
So, then, how good are we at estimating risk? And how should we estimate risks?