That sounds like the gambler’s fallacy. Less runs than what? Most truly random input haa far more runs than what people “think” is random, and in fact that’s one of the statistical tests for whether a data set was random.
You’re essentially saying that a good neural network can predict the next value of a good random number generator. Good luck with that one!
Maybe while you’re at it, have neural networks invert cryptographically secure hash functions :)
Here’s what I’m thinking: The neural network doesn’t have to be correct about which one you pick, it has to be correct about which you don’t pick. Only one option they pick is a loss, so if the other party can be somewhat certain you won’t pick a specific option, it can at least tie. So if a randomizer has pretty even distribution, I think it can win more than half the time, because it can gather roughly how likely the same choice is to be played in a row.
I’m basically suggesting a predictable distribution can be exploited in RPS.
Honestly, the bot could always be winning against the RNG by dumb luck. More experimentation would be needed to be sure. I am just making guesses.
> I think it can win more than half the time, because it can gather roughly how likely the same choice is to be played in a row.
With a random choice, the chance of playing the same choice in a row is 1/3. This does not give you any advantage over having no information (where each choice has a 1/3 chance.)
I think the misunderstanding is in
> a randomizer has pretty even distribution
Here's a thought experiment that might help: imagine what you say is truly the case - that would mean you could "charge up" a dice by rolling it until you got a long run of a given number - lets pick something arbitrary, say you roll until you get 5 twos in a row. According to what you've said the chance of the next number rolled being a two is now lower than it was when you started "charging up" your dice.
How is this possible? Nothing is physically changing about the dice between rolls.
> So if a randomizer has pretty even distribution, I think it can win more than half the time, because it can gather roughly how likely the same choice is to be played in a row.
You’re essentially saying that a good neural network can predict the next value of a good random number generator. Good luck with that one!
Maybe while you’re at it, have neural networks invert cryptographically secure hash functions :)