To generate the Sierpinski triangle illustrated below, there are at least $3$ approaches:
- The Deterministic IFS Algorithm: Determine the affine transformations $T_1,T_2,T_3$ that characterize the fractal, and given an initial compact $P_0$, iteratively compute $$ P_n= \bigcup_{i=1}^3T_i(P_{n-1}) $$
- The Random IFS Algorithm: Determine the affine transformations $T_1,T_2,T_3$ that characterize the fractal, and given an initial compact $P_0$, iteratively compute $$ P_n= T_i(P_{n-1}), $$ where $i\in \{1,2,3\}$ is randomly chosen at each step.
- The Chaos Game: Given a starting point on the edge of the largest triangle of the fractal, iteratively move half-way between the current point and a corner of the triangle.
It makes sense to me why the first two approaches generate the same set: roughly speaking, the random IFS algorithm applies the same sequence of transformations as the deterministic one, but in a different order. What I don't understand is why the Chaos game produces the same result, without using any information from the transformations $T_1,T_2,T_3$, hence my question:
My question:
Why does the Chaos Game on a triangle generate the Sierpinski triangle? Given that the Random IFS algorithm is a generalization of the Chaos game, how can we relate the two methods ?

Someone hinted the answer to me, but deleted it. This person was absolutely correct, only the answer was incomplete (as it was a hint). This person said:
When you perform the chaos game, you actually perform transformations $T_1,T_2$, and $T_3$.
Here is why this is correct: the transformations for the Sierpinski triangle are: \begin{align*} T_1(x,y)&=(x/2,y/2)\\ T_2(x,y)&=(x/2+1/2,y/2)\\ T_3(x,y)&=(x/2+1/4,y/2+\sqrt{3}/4)\\ \end{align*}
And when you iteratively move half-way between the current point $(x,y)$ and a corner $(X_i,Y_i)$ of the triangle , you get the point with coordinates: $$ (X_i+\frac{1}{2}(x-X_i),Y_i+\frac{1}{2}(x-Y_i)) $$
If we replace $X_i,Y_i$ with the coordinates of the corners, we end up with exactly $T_1,T_2,T_3$.