As a machine-learning problem, it would be straightforward: The second learning algorithm (scientist) did it wrong. He’s supposed to train on half the data and test on the other half. Instead he trained on all of it and skipped validation. We’d also be able to measure how relatively complex the theories were, but the problem statement doesn’t give us that information.
As a human learning problem, it’s foggier. The second guy could still have honestly validated his theory against the data, or not. And it’s not straightforward to show that one human-readable theory is more complex than another.
But with the information we’re given, we don’t know anything about that. So ISTM the problem statement has abstracted away those elements, leaving us with learning algorithms done right and done wrong.
As a machine-learning problem, it would be straightforward: The second learning algorithm (scientist) did it wrong. He’s supposed to train on half the data and test on the other half. Instead he trained on all of it and skipped validation. We’d also be able to measure how relatively complex the theories were, but the problem statement doesn’t give us that information.
As a human learning problem, it’s foggier. The second guy could still have honestly validated his theory against the data, or not. And it’s not straightforward to show that one human-readable theory is more complex than another.
But with the information we’re given, we don’t know anything about that. So ISTM the problem statement has abstracted away those elements, leaving us with learning algorithms done right and done wrong.