Deterministically combine more than one source of entropy What is the proper/canonical way to do this?
For example, $0 &lt; r1 &lt; 1$ and $0 &lt; r2 &lt; 1$. Presuming uniformly distributed probabilities for the two, combining/averaging them is going to bias very quickly towards $0.5$.
The specific use case I have is a game/simulation I'm programming. An entity has a random number it was given that's specific to it; Their individual behaviours also have random numbers associated with them. For the purposes of most behavioural calculations, I only use the behaviour random number, but for a few I want to incorporate the entity's random number. Simply averaging them will result in less randomness.
Desired Outcome : Deterministically combine more than one source of entropy
So “Entity A” may have a small random number, and so most of its attributes are skewed small. It's behaviour random number happens to be big, so most of its behaviour attributes are skewed large. When combined the two random numbers deterministically result in $n$, and so some of its behaviour attributes skew towards $n$.
RE: Off-Topic
My question is not specific to game development. The particular use case is. How do cryptographers deterministically combine sources of entropy? I assume they have uses case for this outcome.
Set $r = r_1 + r_2 - \mathsf{floor}(r_1 + r_2)$. One (possible) advantage of this approach is that the result will be uniformly random in [0, 1] even if only one of $r_1$, $r_2$ is uniformly random. (Assuming the two values are independent.)

In Python you can also express this as

r = (r1 + r2) % 1.0