[praestigiare]

The central thesis of the article is supported mostly by the argument "Yeah, it is dude." Ignoring the weakness of the analogy, there is a reason science is "about models" and not about truth: We can measure and test models, and we cannot measure and test truth.

A theory that is more complex and fits the data worse is a less good theory because it is easier to get stuck in. If I have two new theories with this property, how do I choose between them? Imagining a situation where the less predictive theory is closer to the underlying reality does not change this - the fact that it is closer to the underlying reality is irrelevant until and unless we have measurements that allow us to distinguish those cases, at which point, we will have evidence for the (now) more predictive theory.

This article is actually arguing for a system that would result in more dogmatic acceptance and less critical testing.

[karmakaze]

This was the only part of an otherwise great post I objected to. Science may have a side effect of uncovering truth but it shouldn't be the driving force. We should be striving for better predictive ability that doesn't go against our observations. Arguing which is more real is not science.

[jjk166]

Drop some heavy objects and some light objects you have lying around. You will see that the heavier ones tend to fall faster than the light objects. If all you care about is predicting which object will fall faster, you could simply say gravity pulls the heavier one faster and call it a day.

But if you were to forego predictive power and seek understanding, you'd eventually see that objects fundamentally fall at the same rate, and a confounding factor of air resistance was the reason for the observed discrepency. With this deeper understanding, you can make models of gravity and fluid dynamics which are not just more accurate for the specific cases you measured, but also extensible to other cases like the orbits of planets.

[karmakaze]

The second step would be noticing that objects of the same weight fall at different rates. When your predictive power fails you seek more refined models. For everyday phenomena we can guess at truth, maybe 'the wind'. The deeper you go the less clear it becomes as you don't have any direct interaction with the things you're modelling.

[praestigiare]

You are imagining this situation from the perspective of already having a more powerful / general model and looking backwards, so it seems obvious. But that is not how things work. Without data to support it, you can imagine all kinds of possible theories - that is not science, and it certainly gets you no closer to understanding.

[jjk166]

Yes, the entire point is that what was obviously correct in hindsight was not clearly advantageous to begin with, the author's thesis is that exploring models which may seem worse than what we currently have can ultimately lead to better overall results. The author is specifically criticizing the narrow definition of science which says every incremental step no matter how small must improve your models' predictive power rather than exploring the possibility space.

[praestigiare]

Neither you nor the OP have provided a reasonable example of a model which is less predictive and also more "true." the OPs analogy has been discussed to death here, and yours is explicitly of a model which is in fact more predictive.

[karmakaze]

I didn't read it so specifically. If optimizing for X you may be in a local maxima so deviation looking in other regions seems fine.