Manipulating random decimals I've been slowly working my way into the world of AI and its representations of random. Well as you can guess this occurs from a call to random() resulting in a value between $[0, 1]$ to create a desired random effect/action.
What confuses me is that once we get a random number back, lets say $0.10831288644112647$ people tend to use it for one random action (flip a coin) but I was thinking why can't we keep using this number to dictate more actions? Maybe even flip a lot of coins??
Let's not just use the value we get back but the decimal place values too. Currently I'd expect that each value has a $1$ in $10$ chance to be a $[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]$ and thus we could flip a coin for each value?
Just speculating but wouldn't we now be able to flip $18$ coins still maintaining its random result?
Continuing off this idea could we take the result from one decimal place with another? ex 
if randomResult[0] && randomResult[1] == 3 then flip coin 19
Edit: The first value would not be $[0,\dots,9]$!
 A: Mathematically, yes. We could generate $10$-digit random numbers and use each digit to determine one flip.
Unfortunately, this concept doesn't quite work. Consider generating $17$-digit random numbers (without trimming zeros, that is, $1.23030000$ not $1.2303$). Then we get the following results 100000 runs:
N | Freq.
__|_______
0 | 225425
1 | 160930
2 | 163908    
3 | 164527    
4 | 165253
5 | 165161
6 | 165476
7 | 164510
8 | 163528
9 | 161282

This is probably because not all numbers are $17$ digits long, so the padding which is added significantly biases towards zeros. We cannot ignore or trim the padding because then it is biased away towards zeros.

However, if we instead generated integers between $[0, 2^{n})$, we could use this to do coinflips (heads if the $n$th bit is $1$, or zero otherwise). It is the intricacies of floating point arithmetic which makes this method of random decimals unviable.
A: In theory, you are correct.  If the number is truly a random real in $(0,1)$, you can use one call and get all the random numbers you need.  In practice, there are only a finite number of random numbers that can be generated (certainly no more than $2^{53}$ in usual floating point, or $2^{64}$ if you generate integers) and the low order bits tend to be much less random than the high order bits.  There may also be correlations between the various bit locations.  Better to use a separate call for each number you need.  There is a discussion of this in chapter 7 of Numerical Recipes and many monographs as well.
