The classical rule of thumb here is Occam’s Razor, in which simpler models that fit the known facts are preferred.
A more modern (but less practical) take is Solomonoff induction, in which models have prior weightings that exponentially decay based upon the length of their description in some suitable language.
A model constructed specifically to fit a large number of facts must necessarily be as long as the fully specified description of those facts, and is exponentially penalized for that length. What’s more, every new fact that has to be explained generates a new, larger model with worse penalty. Smaller alternative models that don’t predict those exact facts may still end up preferred due to the exponential penalty of the more complex model.
This is rather mathematical, and we almost never explicitly reason exactly according to such a rule, but we often reason like this in a qualitative sort of way.
The main problem is that people mostly don’t bother to keep in mind multiple models at all. The closest we get most of the time is in moments of confusion when our current default model fails, and we need to search for a new one. This is why I value being consciously aware that “I notice I am confused”. Such moments are some of the very few times that we compare alternative models, and paying attention to that process is very important for rationality.
The classical rule of thumb here is Occam’s Razor, in which simpler models that fit the known facts are preferred.
A more modern (but less practical) take is Solomonoff induction, in which models have prior weightings that exponentially decay based upon the length of their description in some suitable language.
A model constructed specifically to fit a large number of facts must necessarily be as long as the fully specified description of those facts, and is exponentially penalized for that length. What’s more, every new fact that has to be explained generates a new, larger model with worse penalty. Smaller alternative models that don’t predict those exact facts may still end up preferred due to the exponential penalty of the more complex model.
This is rather mathematical, and we almost never explicitly reason exactly according to such a rule, but we often reason like this in a qualitative sort of way.
The main problem is that people mostly don’t bother to keep in mind multiple models at all. The closest we get most of the time is in moments of confusion when our current default model fails, and we need to search for a new one. This is why I value being consciously aware that “I notice I am confused”. Such moments are some of the very few times that we compare alternative models, and paying attention to that process is very important for rationality.