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Using prediction markets to generate LessWrong posts

by Matthew Barnett
1st Apr 2022
1 min read
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April Fool'sHumorCommunityRationality
Frontpage

85

Using prediction markets to generate LessWrong posts
34gwern
12AprilSR
22Matthew Barnett
6Yitz
18NunoSempere
15Viliam
3NunoSempere
4Zac Hatfield-Dodds
6elifland
1MichaelDickens
1kave
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[-]gwern3y340

Disappointed to see Manifold still suffering from long-shot bias. It's a good thing you're using greedy sampling.

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[-]AprilSR3y120

How did you decide on the image?

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[-]Matthew Barnett3y220

Before posting, I roll out another sequence of prediction markets that write instructions for me on what images and links I should use in the post, and where.

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[-]Yitz3y60

Oh, I was going to guess going pixel by pixel, with each separate RGB value as a separate market…

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[-]NunoSempere3y180

Good idea. One could also go Turing machine by Turing machine, voting on the ones which would produce the most upvoted content. Then you can just read the binary output as html.

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[-]Viliam3y150

Prediction market with . The theoretically most efficient way to do anything.

(I wonder if we just found a solution to AI alignment. Start with the simplest machines, and let the prediction markets vote on whether they will destroy the world...)

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[-]NunoSempere3y30

Right, just seed a prediction market maker using a logarithmic scoring rule, where the market's prior probability is given by Solomonoff. There is the small issue of choosing a Turing interpreter, but I think we just choose the Lisp language as a reasonably elegant one.

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[-]Zac Hatfield-Dodds3y40

Ah, a sort of multi-modal decision-market-only transformer.

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[-]elifland3y60

Given the success of this experiment, we should propose a modified version of futarchy where laws are similarly written letter by letter!

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[-]MichaelDickens2y10

One concern I have with this method is that it's greedy optimization. The next character with the highest probability-of-curation might still overly constrain future characters and end up missing global maxima.

I'm not sure the best algorithm to resolve this. Here's an idea: Once the draft post is fully written, randomly sample characters to improve: create a new set of 256 markets for whether the post can be improved by changing the Nth character.

The problem with step 2 is you'll probably get stuck in a local maximum. One workaround would be to change a bunch of characters at random to "jump" to a different region of the optimization space, then create a new set of markets to optimize the now-randomized post text.

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[-]kave3y10

Wonderful method! I am a poop brain. Manifold rules ~~~

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Solomonoff prior

I have a secret to reveal: I am not the author of my recent LessWrong posts. I know almost nothing about any of the topics I have recently written about.

Instead, for the last two months, I have been using Manifold Markets to write all my LessWrong posts. Here's my process.

To begin a post, I simply create 256 conditional prediction markets about whether my post will be curated, conditional on which ASCII character comes first in the post (though I'm considering switching to Unicode). Then, I go with whatever character yields the highest conditional probability of curation, and create another 256 conditional prediction markets for the next character in the post, plus a market for whether my post will be curated if the text terminates at that character. I repeat this algorithm until my post is complete.

Here's an example screenshot for a post that is currently in my drafts:

Looks interesting!

Even this post was generated using this method.

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