People have expressed concern about confusion resulting from multiple models and predictions. I will share some of my perspectives, but please read other perspectives as well - statisticians do not always agree with one another...

1. Simulations or models are used to illustrate assumptions and hypotheses. The exact predictions will never be perfect. They are meant to guide decisions or at least aid in decision making

2. To quote the late David Freedman, "something is not always better than nothing" when it comes to modeling. We need to be honest about our uncertainty, assumptions and the limitations of our approach

3. Although I could be wrong in my predictions, as a statistician and former college athlete, I am not willing to sit on the sidelines and blame my teammates for the outcome. If you have something useful to contribute, you should have skin in the game

4. Multiple models and different approaches are fine with me. They show just how uncertain we should be in any one prediction. I do NOT believe in one model to rule them all!