There is No Truth, Only Predictive Power
As much as I strive to get at the “truth” in whatever I do, I hate the word. I prefer to acknowledge that everything we know about the universe is based on the models (aka theories) which are imperfect. As we study more about a system, we refine our models, we take models from other systems and try to apply them to the new realm, sometimes with surprising illumination. I’d rather talk about the predictive power of models than talk about truth.
One of the most subtle yet profound things I learned (am still learning) is that the universe is not the same thing as the models we use to describe and analyze it. By “universe” I mean everything in it as well, everything that we can hope to “know” something about, including humans beings, societies, time, space, knowledge, truth — everything. By models I mean “facts,” “universal truths,” scientific theories, hypotheses, common sense, mental models, anything we refer to either explicitly or implicitly when we say we know or believe something. That the map and the terrain it describes are not one and the same may seem obvious, but many of the greatest misunderstandings, paradoxes and scientific or philosophical debates over the centuries can be explained by realizing that the people involved are trying to say something important about the universe, while the best they can ever do is say something important about their model of the universe. And by their very nature, models are either not entirely accurate or they are incomplete. Often times, in the hard sciences, the models are extremely good and rarely, if ever, fail to predict what they are trying to describe. Which makes it all the more difficult for us to accept evidence suggesting that these models are in need of revision. In addition, we humans seem intent on over-generalizing, or applying a model created to describe one realm to trying to describe another (seemingly) similar realm. The failure to apply Newtonian physics to the atomic and subatomic realms is just one example in a history replete with misapplications and over-generalizations of scientific models.
These thoughts didn’t really click until I took a class with an emeritus professor of mechanical engineering named Stephen Kline. It’s difficult to find a more cut and dry “hard” science than M.E., yet this very well-respected leader in his field was suggesting that the only way to make breakthroughs in understanding in his own field was to start looking at things from a multidisciplinary perspective, by which he meant the judicious and critical application of new models to old domains. Consequently, if I believe one thing and you believe another, yet your beliefs (aka model) have better predictive power than mine, I’m going to adopt your beliefs over my own. But those new beliefs are bound to change when even better predictive models come along.