Prediction and truth: two ways to measure scientific progress
by Alan Cohen
Samuel Arbesman has a really interesting piece in Slate today about scientific progress and computers that may make discoveries that humans can’t understand. One of the issues he raises is that science sometimes overhauls what is considered as “truth,” but nonetheless we continue to make progress, despite the fact that everything we believe today may be considered false tomorrow.
Perhaps the classic example of this is Newton’s laws of motion, which were overturned by Einstein. Einstein showed that Newton’s laws were good approximations at low speeds, but that as objects approached the speed of light they broke down; Einstein’s special relativity theory proposed equations that are valid at all speeds. Einstein not only proposed better equations; those equations implied a different understanding of the universe, an understanding that allowed scientists to pursue new avenues of inquiry.
This example is famous, and shows two important principles. First, as science progresses, prediction gets better and better. In terms of predictive power, Einstein’s contribution was an incremental one, not a revolutionary one. Newton’s laws still apply nearly perfectly at most speeds experienced in daily life, though many cosmological and sub-atomic phenomena can only be predicted with Einstein’s equations.
Second, in terms of our fundamental understanding of what the universe is like, Einstein’s theory was revolutionary. But there is always the possibility that it will be completely overturned by the next revolutionary theory. In this sense, the paradigm shift of Einstein is perhaps most useful in that it opened up new avenues for research, not in that it is a better approximation of the truth itself.
A relativist or a critic of science can always claim, correctly, that the current scientific consensus risks being overturned by the next big discovery, and that therefore scientific claims to understand the truth of the universe are weak. What is harder for a relativist to critique is the improvement in predictive capacity that has been achieved over the course of science history. That should count for something, even if the “truth” of science is at risk of being overturned.
However, the discussion above is predicated on a model of science that originates largely in physics. It is striking to me how many lay discussions of the philosophy of science assume that all science is like physics, that the best scientific design is always a controlled experiment, and that Karl Popper’s idea of falsification of hypotheses is supposed to be the one true scientific method (as mentioned in the Arbesman article, to my chagrin).
In fact, there has been no major paradigm shift in biology since Darwin, despite many in physics. And unlike in physics, where it seems the next great theory might overturn everything we think, in biology it is clearer and clearer that the work that remains is to iron out the details, not find the next grand theory. Yes, evolutionary theory has been gradually refined, and we have largely rejected some spurious ideas such as widespread group selection. Yes, biology is still informed by theory, and sometimes a creative new theory can change a field. But the fields that get changed are narrower and narrower, as the high level theories become more strongly confirmed.
Likewise, controlled experiments are rare in fields such as ecology and evolution, where it is generally impossible to rewind history: most of our knowledge of these fields is based on observational data and on a gradual accumulation of evidence rather than clear yes-no, up-or-down tests of hypotheses.
We never know what tomorrow will bring, and in a formal sense no aspect of scientific theory can be considered to be 100% proven beyond all doubt. (Perhaps God is faking our data for reasons unknown to us, after all…). But it seems less and less likely that there are any impending scientific revolutions outside physics. So, while we can certainly measure scientific progress as an increase in our predictive power (regardless of the underlying truth), it is also probable that in many domains we are not too far off from an accurate description of “truth,” even if we will never know for certain.
Thoughts on science, truth, and prediction? Leave them in the comments and I’ll respond…