My idea is simple.
Inspired by an old philosophical question. I believe its better to assume something not divisible in the limit. Since you end up with something that still exist.
Then try to divide an odd number with 2 and you end up with a decision. Which side should have one more.
3/2 “equals” (1 and 2) or (2 and 1) compare 1.5*2 with 1+2
1/2 “equals” (0 and 1) or (1 and 0) compare 0.5*2 with 0+1
This can be used in algorithms or math I think.
In machine learning you can use this ?easily. I believe every ratio is a decision number. This means that you also got a decision for derivatives dy/dx.
So for differential equations I wonder if you can have a decision model(data) for when to use the perticular finite difference, f’=(f(x+h)-f(x))/h. Left or Right = model(data). That is. The model outputs wether to take the left or right side of the point. So its a classification problem.
So I called it a decision number because it comes directly from a model worthy decision.