How detailed of a model are you thinking of? It seems like there are at least easy and somewhat trivial predictions we could make e.g. that a human will eat chocolate instead of motor oil.
How about a prediction that a particular human will eat bacon instead of jalapeno peppers? (I’m particularly thinking of myself, for whom that’s true, and a vegetarian friend, for whom the opposite is true.)
This model seems to be reducible to “people will eat what they prefer”.
A good model would be able to reduce the number of bits to describe a behavior, if the model requires to keep a log (e.g. what particular humans prefer to eat) to predict something, it’s not much less complex (i.e. bit encoding) than the behavior.
It seems to me that your original prediction has to refer either to humans as a group, in which case Luke’s counterexample is a good one, or humans as individuals, in which case my counterexample is a good one.
It also seems to me that either counterexample can be refined into a useful prediction: Humans in general don’t eat petroleum products. I don’t eat spicy food. Corvi doesn’t eat meat. All of those classes of things can be described more efficiently than making lists of the members of the sets.
No, because preferences are revealed by behavior. Using revealed preferences is a good heuristic generally, but it’s required if you’re right that explanations for behavior are mostly post-hoc rationalizations.
So:
People eat what they prefer. What they prefer is what they wind up having eaten. Ergo, people eat what they eat.
I think “vague” is a poor word choice for that concept. “(not) informative” is a technical term with this meaning. There are probably words which are clearer to the layman.
How detailed of a model are you thinking of? It seems like there are at least easy and somewhat trivial predictions we could make e.g. that a human will eat chocolate instead of motor oil.
I would classify such kinds of predictions as vague, after all they match equally well for every human being in almost any condition.
How about a prediction that a particular human will eat bacon instead of jalapeno peppers? (I’m particularly thinking of myself, for whom that’s true, and a vegetarian friend, for whom the opposite is true.)
This model seems to be reducible to “people will eat what they prefer”.
A good model would be able to reduce the number of bits to describe a behavior, if the model requires to keep a log (e.g. what particular humans prefer to eat) to predict something, it’s not much less complex (i.e. bit encoding) than the behavior.
Maybe I’ve misunderstood.
It seems to me that your original prediction has to refer either to humans as a group, in which case Luke’s counterexample is a good one, or humans as individuals, in which case my counterexample is a good one.
It also seems to me that either counterexample can be refined into a useful prediction: Humans in general don’t eat petroleum products. I don’t eat spicy food. Corvi doesn’t eat meat. All of those classes of things can be described more efficiently than making lists of the members of the sets.
No, because preferences are revealed by behavior. Using revealed preferences is a good heuristic generally, but it’s required if you’re right that explanations for behavior are mostly post-hoc rationalizations.
So:
People eat what they prefer. What they prefer is what they wind up having eaten. Ergo, people eat what they eat.
Consistency of preferences is at least some kind of a prediction.
I think “vague” is a poor word choice for that concept. “(not) informative” is a technical term with this meaning. There are probably words which are clearer to the layman.
I agree vague is not a good word choice. Irrelevant (using relevancy as it’s used to describe search results) is a better word.