Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

*(A longer text-based version of this post is also available on MIRI's blog* *here, and the bibliography for the whole sequence can be found* *here.)*

*The next post in this sequence, 'Embedded Agency', will come out on Friday, November 2nd.*

*Tomorrow’s AI Alignment Forum sequences post will be 'What is Ambitious Value Learning?' in the sequence 'Value Learning'.*

This confuses me even more. You can imagine act contrary to your own algorithm, but the imagining different possible outcomes is a side effect of running the main algorithm that takes $10. It is never the outcome of it. Or an outcome. Since you know you will end up taking $10, I also don't understand the idea of playing chicken with the universe. Are there any references for it?

Wait, what? We started with the assumption that examining the algorithm, or running it, shows that you will take $10, no? I guess I still don't understand how

is even possible, or worth considering.

Hmm, maybe this is where I miss some of the logic. If the predictions are accurate, the map is bijective. If the predictions are inaccurate, you need a better algorithm analysis tool.

To me this screams "get a better algorithm analyzer!" and has nothing to do with whether it's your own algorithm, or someone else's. Can you maybe give an example where one ends up in a situation where there is no obvious algorithm analyzer one can apply?