I don't like the term "money is a shared illusion" (as it misguides the real concept), money is the materialization of credit. The history of money is pretty long to this comment, but to summarize: money is the idea that someone owns you some amount of resources (work/material/etc), and as the society agreed with this common denominator (money), so the society also owns you some amounts of resources.
Price formation (or price system) consider a lot of things, geographical resources, comparative advantages, technology and many more, all this knowledge we call market information.
So the "unidentified strangers" will want (or not) this credit with society so they can exchange in something that the society could provide to them, the amount requested by this stranger is based on how he evaluates the information it haves, with his own perspective and when it decides the X amount, this will also add to the information above and update the weights in this high-dimensional network of values.
When you pass this credit to someone else, this person could use his subjective value to offer X amount of his credit to satisfies his interests as he knows that society will also have personal interests and want the credit too. This could be pretty obvious but I introduce now what problem the "money" solves and no economist could figure out another solution to this: https://en.wikipedia.org/wiki/Economic_calculation_problem
So money and market is a super distributed computer of calculation and allocation of resources, considering subjective value, socioeconomic factors and many more things that is by now impossible to account in any other known way.
A video of intuitive explanation: https://www.youtube.com/watch?v=zkPGfTEZ_r4
"Why can I have little information but still have to search a huge state space, why can't I go straight to the conclusion/action?"
There's no answer for this question, if you find one, pick your prize of 1million dollars:
https://en.wikipedia.org/wiki/P_versus_NP_problem
Here is a video explaining it better: https://www.youtube.com/watch?v=YX40hbAHx3s
Usually the RandomSearchCV, I've tryied the bayesian optimization from skopt, but empirically I did not see advantages in this over random search, not sure if I used it wrong, someone with good results?
The sample efficiency is not a formal claim, like, RL algorithms are claimed to be sample inefficient as only takes 10 games of Pacman to a human get good at it, but we can't isolate this knowledge in human brain. The point a human learns to play Pacman it already learned many things, like GPT-3, and we don't know what things contribute to playing Pacman, is it motor skills? spacial skills? Knowing all the skills that enable human to play Pacman in only ten games and passing this as a pre-training for the RL algorithm then training it to play Pacman would be a fair comparison of how sample efficient it is. The same applies for the names example, could we really measure how many times a human heard a name or maybe a similar name?