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.
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.