Episode Details

This is a crosspost from my blog post.

Many EAs use extensive “bayesian reasoning.” The basic idea behind it is that you should do the following:

1. Be willing to assign probabilities to the likelihood of anything occurring or being true.
2. Update these probabilities when you learn new information.
3. Act on these probabilities if they suggest that certain actions are higher in value than all other actions.

Bayesian reasoning is helpful for a lot of everyday decision-making. If you’re trying to figure out whether to take a job in Los Angeles or in New York, it makes sense to try to guess how happy you’d be in each respective city. And, if you’re trying to compare career paths, you should assign probabilities to how likely you are to succeed in them.

But, to me, EAs take this kind of reasoning too far. EAs have variously tried to predict how many future humans there will be, asked non-domain experts how likely they think a catastrophic pandemic will be, and even tried to determine whether we should work on improving the lives of people in the far future.

Probably the most common (and representative) example of this, though, is the idea [...]

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Outline:

(01:43) Reason #1: If you know very little about something, your guesses are completely arbitrary.

(02:29) Reason #2: If your guesses are completely arbitrary, updating won't bring you to the correct probability.

(03:21) Reason #3: We should expect most guesses about the future to be wrong.

(04:19) Reason #4: If your guesses are based on other people's guesses, you might all be wrong.

(04:45) Reason #5: People rarely offer extraordinarily low probabilities.

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First published:
August 2nd, 2026

Source:
https://forum.effectivealtruism.org/posts/ttk9RJw9sbSp8upgE/eas-should-use-less-bayesian-reasoning

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Narrated by TYPE III AUDIO.