The standard “game design” thing to do would be push the probabilities through a sigmoid function (to reward correct changes much more often than not, as well as punish incorrect choices more often than not).
I don’t understand. You’re applying a sigmoid function to probabilities… what are you doing with the resulting numbers?
Hopefully normalizing them so they sum to 1 again and using them to draw an outcome :)
I assume the intent was to say that in normal games, if the goal is to choose the “smartest” action (highest EV, or whatever the objective fn is) under uncertainty, and the player makes the optimal choice, they should always on average be noticeably higher rewarded (not just slightly more rewarded).
It’s fine (maybe more addictive?) for right decisions to sometimes not result in a win, so long as there enough chances for a masterful player to recover from bad luck.
I still don’t see why you would want to transform probabilities using a sigmoidal function. It seems unnatural to apply a sigmoidal function to something in the domain [0, 1] rather than the domain R. You would be reducing the range of possible values. The first sigmoidal function I think of is the logistic function. If you used that, then 0 would be transformed into 1⁄2.
I have no idea how something like this could be a standard “game design” thing to do, so I think we must not be understanding Chimera correctly.
Yes, no fixed sigmoidal would have the effect I assumed was his intent.
You could set a very steep sigmoidal filter that just catches the modal region of the pdf, but that’s clunky, and you have to have exactly the right filter for the particular distribution.
A simpler way to achieve “sharpening” a non-uniform probability distribution (making the modal region even more likely to pay off) is to raise it to some power >1, then renormalize.
I don’t understand. You’re applying a sigmoid function to probabilities… what are you doing with the resulting numbers?
Hopefully normalizing them so they sum to 1 again and using them to draw an outcome :)
I assume the intent was to say that in normal games, if the goal is to choose the “smartest” action (highest EV, or whatever the objective fn is) under uncertainty, and the player makes the optimal choice, they should always on average be noticeably higher rewarded (not just slightly more rewarded).
It’s fine (maybe more addictive?) for right decisions to sometimes not result in a win, so long as there enough chances for a masterful player to recover from bad luck.
I still don’t see why you would want to transform probabilities using a sigmoidal function. It seems unnatural to apply a sigmoidal function to something in the domain [0, 1] rather than the domain R. You would be reducing the range of possible values. The first sigmoidal function I think of is the logistic function. If you used that, then 0 would be transformed into 1⁄2.
I have no idea how something like this could be a standard “game design” thing to do, so I think we must not be understanding Chimera correctly.
Yes, no fixed sigmoidal would have the effect I assumed was his intent.
You could set a very steep sigmoidal filter that just catches the modal region of the pdf, but that’s clunky, and you have to have exactly the right filter for the particular distribution.
A simpler way to achieve “sharpening” a non-uniform probability distribution (making the modal region even more likely to pay off) is to raise it to some power >1, then renormalize.