Oatmeal Agitante

How do you tell the difference between an agent that reasons about "the external world" and

And an agent that uses models to make efficient and accurate predictions of its sense data, and uses model states to make predictions?

the former can be useful for more than making predictions, like making explanations

I made a typo, last clause should be "and uses model states to make decisions"

not sure I understand the distinction you're making, specifically, what is the distinction between model and explanation?

if a model is optimized for making predictions, it may not be useful for e.g. producing counterexamples to other models

by "explanation" I mean a thing that acts on existing models to change/improve them