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 everyday. 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?