Good property of scientific theory is that it serves as a data compression. Les bits you need to explain the world around you, the better theory. This is IMO very good definition of what explanation is.
Also, the compression usually is lossy, such as Newtonian mechanics.
Agreed that this points in the right direction. I think there's more to it than that though. Consider for example a three-body problem under Newtonian mechanics. Then there's a sense in which specifying the initial masses and velocities of the bodies, along with Newton's laws of motion, is the best way to compress the information about these chaotic trajectories.
But there's still an open question here, which is why are three-body systems chaotic? Two-body systems aren't. What makes the difference? Finding an explanation probably doesn't allow you to compress any data any more, but it still seems important and interesting.
(This seems related to a potential modification of your data compression standard: that good explanations compress data in a way that minimises not just storage space, but also the computation required to unpack the data. I'm a little confused about this though.)
Yeah, I think you're right. There are two types of explanations:
The three-body systems is the example of the latter. As is lots of math and computer science.
Firstly, on a historical basis, many of the greatest scientists were clearly aiming for explanation not prediction.
Could you expand a bit more on how you view explanation as distinct from prediction?
(As I think about the concepts, I'm finding it tricky to draw a crisp distinction between the two.)
(Just an attempt at an answer)
Both an explanation and a prediction seek to minimize the loss of information, but the information under concern differs between the two.
For an explanation, the goal is to make it as human understandable as possible, which is to say, minimize the loss of information resulting from an expert human predicting relevant phenomena.
For a prediction, the goal is to make it as machine understandable as possible, which is to say, minimize the loss of information resulting from a machine predicting relevant phenomena.
The reason there isn't a crisp distinction between the two is because there isn't a crisp distinction between a human and a machine. If humans had much larger working memories and more reliable calculation abilities, then explanations and predictions would look more similar: both could involve lots of detail. But since humans have limited memory and ability to calculate, explanations look more "narrative" than predictions (or from the other perspective, predictions look more "technical" than explanations).
Note that before computers and automation, machine memory and calculation wasn't always better than the human equivalent, which would have elided the distinction between explanation and prediction in a way that could never happen today. e.g., if all you have to work with is a compass and straight edge, then any geometric prediction is also going to look like an explanation because we humans grok the compass and straightedge in a way we'll never, without modifications anyway, grok the more technical predictions modern geometry can make. The exceptions that prove the rule are very long geometric methods/proofs, which strain human memory and so feel more like predictions than methods/proofs that can be summarized in a picture.
As machines get more sophisticated, the distinction will grow larger, as we've already seen in debates about whether automated proofs with 10^8 steps are "really proofs" - this gets at the idea that if the steps are no longer grokable by humans, then it's just a prediction and not an explanation, and we seem to want proofs to be both.
Firstly, on a historical basis, many of the greatest scientists were clearly aiming for explanation not prediction.
In all of your examples, the new theory allowed making predictions, either more correct than previous ones (relativity, astronomy) or in situations that were previously completely un-predictable (evolution). Scientists expected good predictions to follow from good explanations, and they were in large part motivated by it.
Wiener, on the other hand, is saying it doesn't matter what explanation you choose if all explanations yield the same prediction, in a particular field of study or experiment. And you don't need explanations at all if they can't ever yield different predictions (in any possible experiment). That's a different statement.
I think that taking prediction to be the point of doing science is misguided in a few ways.
This seems to be just a matter of definitions. Scientists are human beings, they have a wide variety of interests and goals. You can label a more narrow subset of them "science", and then say that some of what they're doing "isn't science". Or you can label everything they tend to do as "science", because it tends to come together. But the question "what is the real point of doing science?" is just a matter of definition.
When pointing to a name like Wiener it would be great to have the full name to be able to google who you mean. In this case Norbert Wiener seems me best guess?
When you have a deep explanation sure there are points that tell that it's deep. However I wouldn't exactly use the word "evidence" for that.
I think it's pretty hard to define "mathematical cleaniness". One is almost guaranteed to discriminate against undiscovered forms of math.
There is also the problem of where can you stop if matter of fact is not a good stopping point. That is if I have a theory that makes perfect predictions and someone comes and says "but you theory doesn't explain the phenomena" under which kind of conditions can I say "no, it does explain the phenomena?". I am reminded of quantum mechanics where there exist multiple formulaitons which are proven to be equivalent. Would one have to start discrimanting between these which are "explanining" formulations and which are "non-explaining" formulations? What would be the critera to raise one above others?
It would also be weird if biology was incomplete until it answered the quesition "why life?" in the "meaning of life" sense. That is other disciplines than science make use of explanation and it's not immidiately obvious which parts of that cluster is relevant to science.
“The important thing is to be able to make predictions about images on the astronomers’ photographic plates, frequencies of spectral lines, and so on, and it simply doesn’t matter whether we ascribe these predictions to the physical effects of gravitational fields on the motion of planets and photons [as in pre-Einsteinian physics] or to a curvature of space and time.”
“If we gave it the design of a spaceship, and the details of a proposed test flight, it could tell us how the spaceship would perform on such a flight. But it could not design the spaceship for us in the first place. And even if it predicted that the spaceship we had designed would explode on take-off, it could not tell us how to prevent such an explosion. That would still be for us to work out. And before we could work it out, before we could even begin to improve the design in any way, we should have to understand, among other things, how the spaceship was supposed to work. Only then would we have any chance of discovering what might cause an explosion on take-off. Prediction – even perfect, universal prediction – is simply no substitute for explanation.”