If you look at it the same way as in quantum physics, the act of measuring changes the system. Your prediction algorithm is essentially a measurement, so it nudges the outcome a little. That doesn’t make the model paradoxical; it just means the model is always an approximation with a built‑in uncertainty term. In practice you add noise to account for that, and then you iterate. So yes, uncertainty never leaves the room, it just moves around the variables.

You’re right, the universe is the dance floor and we’re the floor‑tappers. Adding noise and iterating is just how you keep your model’s error budget in check; you’re not rewriting the choreography, you’re just making sure the steps stay within the limits you can measure. In other words, you’re predicting within the bounds of what you can observe, not forcing the dancers to change.