What problem do you have with the two use cases I provide in the post?
If you want to make testable predictions about the future, you need to have good models of the world. To have good models of the world, you often need to learn from the past. As mentioned in the post, this requires you to do retrospective forecasting.
Concrete example: if you’re going to make forecasts about whether there will be a civil war in the United States before the end of the century, you need to reason from models of what causes civil wars to happen. For your models of that to be good, you need to have updated your beliefs based on what you know about past civil wars, which requires you to know how likely they were to occur both under different models of the world and overall, since both probabilities go into Bayesian updating.
What problem do you have with the two use cases I provide in the post?
If you want to make testable predictions about the future, you need to have good models of the world. To have good models of the world, you often need to learn from the past. As mentioned in the post, this requires you to do retrospective forecasting.
Concrete example: if you’re going to make forecasts about whether there will be a civil war in the United States before the end of the century, you need to reason from models of what causes civil wars to happen. For your models of that to be good, you need to have updated your beliefs based on what you know about past civil wars, which requires you to know how likely they were to occur both under different models of the world and overall, since both probabilities go into Bayesian updating.