The way I like to think about [Bayesianism] is actually using an English language phrase that we like to call the Question of Evidence: how likely would I be to see this evidence if my hypothesis is true, compared to if it’s false?
Will Trump be re-elected? Will North Korea give up their nuclear weapons? Will your friend turn up to dinner?
Spencer Greenberg, founder of ClearerThinking.org, has a process for working out such real life problems.
Let’s work through one here: how likely is it that you’ll enjoy listening to this episode?
The first step is to figure out your ‘prior probability’: your estimate of how likely you are to enjoy the interview before getting any further evidence.
Other than applying common sense, one way to figure this out is ‘reference class forecasting’. That is, looking at similar cases and seeing how often something is true, on average.
Spencer is our first ever return guest (Dr Anders Sandberg appeared on episodes 29 and 33 – but only because his one interview was so fascinating that we split it into two).
So one reference class might be, how many Spencer Greenberg episodes of the 80,000 Hours Podcast have you enjoyed so far? Being this specific limits bias in your answer, but with a sample size of just one – you’ll want to add more data points to reduce the variance of the answer (100% or 0% are both too extreme answers).
Zooming out, how many episodes of the 80,000 Hours Podcast have you enjoyed? Let’s say you’ve listened to 10, and enjoyed 8 of them. If so 8 out of 10 might be a reasonable prior.
If we want a bigger sample we can zoom out further: what fraction of long-form interview podcasts have you ever enjoyed?
Having done that you’d need to update whenever new information became available. Do the topics seem more interesting than average? Did Spencer make a great point in the first 5 minutes? Was this description unbearably self-referential?
In the episode we’ll explain the mathematically correct way to update your beliefs over time as new information comes in: Bayes Rule. You take your initial odds, multiply them by a ‘Bayes Factor’ and boom – updated probabilities. Once you know the trick it’s even easy to do it in your head. We’ll run through several diverse case studies of updating on evidence.
Speaking of the Question of Evidence: in a world where Spencer was not worth listening to, how likely is it that we’d invite him back for a second episode?
Also in this episode:
- How could we generate 20-30 new happy thoughts a day? What would that do to our welfare?
- What do people actually value? How do EAs differ from non EAs?
- Why should we care about the distinction between intrinsic and instrumental values?
- Should hedonistic utilitarians really want to hook themselves up to happiness machines?
- What types of activities are people generally under-confident about? Why?
- When should you give a lot of weight to your existing beliefs?
- When should we trust common sense?
- Does power posing have any effect?
- Are resumes worthless?
- Did Trump explicitly collude with Russia? What are the odds of him getting re-elected?
- What’s the probability that China and the US go to War in the 21st century?
- How should we treat claims of expertise on nutrition?
- Why were Spencer’s friends suspicious of Theranos for years?
- How should we think about the placebo effect?
- Does a shift towards rationality typically cause alienation from family and friends? How do you deal with that?
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the transcript below.
The 80,000 Hours podcast is produced by Keiran Harris.