Comment by abeppu | Hacker News Reader

Comment by abeppu | Hacker News Reader

I think an important assumption underlying this is that the goal of the predictor is to be as accurate as possible. In a number of cases that's probably not true. Other goals for making (public) predictions:

- influencing investor or consumer confidence. Even if you don't have high confidence in what you're saying in the long term, it may be necessary in the near term to make statements that project confidence

- building a brand as a thought-leader, futurist, whatever. In part this depends on saying something interesting that people will want to read or hear about. People can enjoy this kind of prediction without believing it. A book review for Kurzweil's The Singularity is Near said "exhilarating speculation is great fun to read, but needs to be taken with a huge dose of salt." Buckminster Fuller was wrong about many things but people still enjoy his work because it's interesting.

https://en.wikipedia.org/wiki/The_Singularity_Is_Near#Review...

- influencing policy in your preferred direction. No one can predict the number of severe zoonotic diseases that will have epidemics in the next 30 years, but some experts think they'll happen more frequently as the climate crisis progresses. We might all be better off if experts understate the uncertainty in their projections when they make their case to policy-makers.

I'm going to go out on a limb and say Dan knows people bullshit, but it would be very weird to say "well, I guess I can't criticize the bullshit" just because someone else doesn't care about the truth.

To your point about people enjoying the work, they're still allowed to. Anyone can read what Dan wrote, and say "I enjoy this work despite the fact that (almost) none of it is true." I do this with Marvel movies, for instance. I do suspect that many readers did not realize how phenomenally bad Kurzweil's projections are, and would be disappointed by realizing that they are.

I also think there's a key point being missed. That is: being a futurist with high accuracy is useless if your predictions were already obvious.

I can score a 100% accuracy rate by predicting things that everyone knows are going to happen (humans will still exist in 2025, the sun will rise in 2050, etc.). A futurist is not useful when they are merely accurate, but when they are accurate about events that no one else expected.

It is inappropriate to compare someone like Kurzweil to Caplan. Caplan is just trying to be correct. Kurzweil is trying to make predictions about black-swan events -- the things people don't see coming. These are different ballgames, and the ballgame of predicting unexpected events is inherently more difficult.

> We might all be better off if experts understate the uncertainty in their projections when they make their case to policy-makers.

Frustrating, but true. I was recently asked at work to stop adding statements on certainty, to be more concise. I suppose they were in essence delegating the decision of whether something matters to me. That's in some ways a reasonable request, because if I make a nuanced presentation, the audience doesn't have the education/knowledge to evaluate the nuances.

I think an assumption underlying this comment is that people are grading the accuracy of futurist gurus in order to give those gurus helpful feedback.

Nobody needs to be reminded that people who make public predictions might want to deceive investors or customers, to be famous and make money, or to deceive voters or legislators into pursuing their favored policies. People need to be reminded that their current psychics (and pundits for that matter) have a long history of only getting the things right that everybody got right, and even getting a few of those wrong for a bit of contrarian attention.

> I think an assumption underlying this comment is that people are grading the accuracy of futurist gurus in order to give those gurus helpful feedback.

No, my assumption is that the author is interested in understanding what contributes to good vs bad predictions, and how credible long range predictions are.

The author points out factors such as whether the predictor appears to learn from past prediction errors, whether they have a deep expertise in the area they're making a prediction about, whether they depend on extrapolating exponential trends etc. I am guessing that "Do they sell a lot of popular press books?" and "Do they receive performance based compensation from an organization invested in the domain under discussion?" are at least as important explanatory factors as the ones the author explicitly discusses.

So it's a grift at worst, and Kardashians-tier mindless entertainment at best?

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