At what prediction level do we call a physical world theory true?

Question

Alright, I am in a bind.

I know that prediction is the ultimate test of a theory in the physical world. You can make any assumptions, you can come up with useless things like the math in string theory, but ultimately it's only considered "true", as true as such theories could be if and when they make a testable prediction, and it comes out to be accurate.

Where do we draw the line on the accuracy of the prediction before we call it a true theory or model of the real world? Predictions can vary in accuracy, for eg: Sun rises in the east, vs. sun will rise tomorrow at this and that angle at this time and so on.. And then you have to consider the frequency of the data or pattern that you observe as well into consideration. Now I know that if the pattern is not seen often, then you'd want to make a far more accurate prediction to prove the theory true vs. when a pattern repeats often, is observed in multiple places, because then in the latter case, since you can make multiple predictions you could be lax with accuracy and make more general statements, for eg: things with mass are attracted to each other vs. things with mass at a distance, have a mutual attraction represented by a quantity called "force" which is inversely proportional to the square of the distance between them and their masses and so on bla bla.

Do we always need an idea based approach first? Since it has the power of prediction compared to a more data fitting based approach ( which might not have any underlying intuitive reasoning)? I like the idea based approach because there is less "peeking" at the data, and more coming up with sound reasoning.

Lastly falsifiability. This thing has been bothering me a little. I thought it was central to calling a physical theory of the world, a "theory". You need to have a concept of falsification otherwise it is in the same bucket as religion, but doesn't making a prediction encompass falsifiability? It seems to me that falsification is a test of validity and not truth. Am I correct in this? Because if you have a statement which says: if A then Y, then you automatically have the falsification criteria satisfied.

Answer 1

It's not so much the predictive success of theories that convinces us that they are true than the fact that they make novel unexpected predictions when they are extended to new area and when there's a convergence between different experimental area.

Take for example Newtonian physics. It failed to account for the trajectory of Mercury. It wasn't considered false for this reason, rather scientists postulated hidden planets or asteroids near the sun to account for this. If a theory fails in its predictions, it is always possible in principle to make additional hypothesis that will restore its truth. But what we're expecting is that at some point, these additional hypothesis will be confirmed by independent observations: they must make novel predictions, not only account for what we already observed.

Now take the theory of relativity. It can account for the trajectory of Mercury. But that's not what convinced scientists that it was true: it was really confirmed when we tested a novel unexpected prediction of the theory, that light is deflected by massive objects. This was observed during a solar eclipse (the apparent position of stars was altered by the presence of the sun) and that was the decisive experiment that led to a wide acceptance of this theory by the community.

So I wouldn't say that it's a question of level of accuracy (because we can always postulate noise or measurement errors to account for them), but rather the fact that there's a conjunction between very different area of experience and the theory unifies all these phenomena in a single simple framework that explains them all, and that it even continues to work when applied to new areas. That's what is decisive for theory acceptance, not a certain level of accuracy.

I think this is the sense following which we need an "idea based approach" as you say: a data fitting approach is incapable of unifying different area of experience in a simple explanatory way. Or to say it differently, science postulates unobservable things to explain observable phenomena (and unify them).

As for falsification, it is indeed a criteria of scientificity, not of truth, but this criteria can be criticised, precisely for the reason I gave: it's always possible to "save" a theory with additional hypothesis, so theories are actually not falsifiable (strictly speaking) and scientists never reject well accepted theories at the first experimental failure.

Answer 2

I agree this is an interesting question. I think you were wise to put the word "true" in quotation marks up there. If you look at the history of science, it seems that the truth of the future very, very often exceeds the truth of the present. So we can say that, in general, truth exceeds our scientific method of the time (truth exceeds method). (Gadamer). Many of our truths are therefore useful, but really just provisional, until the next "great one" comes along, etc. W.V. Quine, philosopher-logician had an interesting idea with his "Web of Belief" which can be read online I think. Don't know if there is a download or not.

Answer 3

As far as physics is concerned, accuracy varies with the matter at hand. Think particle physics vs Newtonian physics. I don't think there is a universally acceptable definition or some margin error in percentage terms that is universally applicable to all fields. All observations have a measurement limit and no theory can be absolute "truth" in the sense it can never be verified with 100% accuracy, ergo questioning the very existence of a "true" theory. Of course for practical purposes it is inconsequential.

Sure the idea based approach sounds elegant as it appears to be based on physical foundations but essentially there is no foundation. All basic theories are just observations (i.e data) described neatly and extended as ideas in other areas. So is there really a dichotomy? Take gravitation, would you call it an idea or simply a theory based on data? or for e.g. the postulate that speed of light is a universal constant, an idea on which relativity stands. It was basically an attempt to fit data (or observations). The key, however, lies in using data from one field and using them to predict altogether novel ideas (e.g. gravitational waves) and then verifying them. But it all starts with "data fitting" not the other way round.

I agree that falsifiability is more about the approach to describing truth rather than the truth itself. But does Prediction always encompass falsifiability ? If X then Y - to falsify this, y has to be verifiable by experiment or observation, at-least in Physical Sciences. If its not verifiable it is certainly not falsifiable but will it be considered a prediction ?

Answer 4

It's an interesting question. The prediction level is probably explicitly defined in certain circumstances - e.g. discovering a new particle in the Large Hadron Collider like the Higgs boson might only be accepted if the statistical tests are significant (I don't know exactly what the case was for the Higgs boson but you get the point). The interesting thing is that for a lot of other theories it's not explicit. When the results of the experiment don't match the predictions given by a theory, there are two options - one is to say the theory is wrong/insufficient. The other way out is to say your measurement is error or the machines are faulty. E.g. none of the chemistry experiments we tried out in school ever worked out perfectly ,we were always off by a couple of mls (from what was expected) so we always assumed that we made slight measurement errors, not that the theories are wrong. I think a lot of this is based around what is practical - perhaps the universal constants aren't constant, and differ slightly, or perhaps the measurement noise is error. It depends on which theory makes more sense at the time, and whether or not the predictions of each continue to work the next ten times you test them.

Typically in science it is never purely idea based as you put it. You need some data to work with to create the predictive model (perhaps you may find some philosophers who disagree with this and believe it is possible to have a priori scientific knowledge). Peeking at the data includes making any observation surely (like noticing the sun set in a certain direction today)?

Lastly, I think you're right about falsification in that prediction gives you a way to show a theory was false. Ie if it predicts things in a wrong way, then you can show it doesn't work. The difficult part is when people talk about "explanations" that claim to be scientific but do not make predictions, e.g. a psychoanalyst describing the discovery of unconscious thoughts which are inaccessible to everyone but influence our every decision. It's not a good scientific theory as it doesn't make predictions but attempts to explain something that can't be shown to be true or false.

Answer 5

To some degree, I would disagree with what you say about prediction. I say this because using the word prediction could be misleading, when what I think we care about is building a theory on an accurate model of the phenomena, which gives an adequately descriptive account of the phenomena. And one theory would succeed another when it is seen to give a more adequately descriptive account of the phenomena.

Answer 6

From a Kuhnian point of view, never. We choose an underlying theory that gives us a compelling story, and we hold onto that paradigm as long as it keeps netting us better and better results.

But we know from history that this paradigm will be displaced. We have yet to see a theory that just stayed the same, and was not either controverted or altered extremely at some point. If any of it was ever really true, that should not happen.

We can know how reliable a rule is, and even if we have a better rule, we can keep relying upon it to continue being as reliable as history proves it has been. It is as true as it is, it has a given statistical usefulness. We can know what theory is better, but we also know there is going to be a better theory yet, or science would just be in the process of digging its own grave.