Purists love to talk about what is – and is not – a science. Clearly, for example, physics is a science, because it allows us to offer theories, and test them against data. And we learn from the results.
By way of contrast, economics or sociology or psychology are not sciences. Of course not! Those soft squishy subjects have no real predictive power after all, right?
Not so fast. Sure, physics will tell you, with astonishing accuracy what happens when a billiard ball hits another one. But if you replace the target billiard ball with a kitten, physics is not so helpful. And if we replace the kitten with a person, then physics has nothing at all useful to tell us.
On the other hand, some of those squishier subjects, albeit with large error bars, do have some predictive powers when it comes to people. When we scare people in a pandemic, we know some of the likely outcomes. We know how people tend to react to scarcity and plenty, how they change as a result of marriage or divorce. We don’t learn these things from physics, but we can learn them from the study of mankind through these softer “sciences.”
And aren’t people ultimately more interesting than billiard balls? After all, the physical world is at least partially deterministic. The more predictable the natural world is, the more boring it is. Billiard balls, writ large or small, are still inanimate forces acting on each other.
Of course, the physical world is not really deterministic, not all the way down or all the way up. And as we leave the realm of simple mechanics, we see that the parts wherein the “hard” sciences end up unable to give definitive answers at all, resembling distributive answers that look more like statistical spreads in sociology than Newtonian certainty. In other words, science stops telling us what will happen, and instead tells us what is more or less likely to happen!
Indeed, if you come right down to it, if “All Models are Wrong, but Some Models are Useful,” then there is another variation from the math-grounded physics down through chemistry to sociology: the errors bars get larger. All answers to all predictive questions in every field end up offering a statistical range of answers. The difference between physics and sociology is found not in whether the operative models are predictive, but in how large the error bars are.
“Ah!” you might say. “But at least Science is falsifiable! That is what makes the difference!”
This sounds nice. But how falsifiable is physics, really? If 97 or 99% of the mass in your galactic model is not actually directly detectable at all, but is instead measurable only by its assumed effects on other objects (see Matter:Dark), then where is the falsification?
Or take Climate Change. All the models have been wrong. None have been useful. Does that stop the Science Train from continuing to double-down on nonsense? Not so far.
There is no objective scientific discipline, free from human interference and biases. We might argue that this is because people are the practitioners of science. But we cannot be sure. After all, anything can be described in more than one way, so why should there be an “objective” way to describe a leaf? In a language not bounded by human models of physics and chemistry and biology and dendrology and even poetry, is there such a thing as a “leaf”? And if there is, does it even matter?
I would like to offer that the ideal scientific metric of “predictive authority” is itself a false goal, since it can never be absolutely, 100%, no-wiggle-room-whatsoever- TRUE. We instead should be very happy with an engineering standard: Either it works, or it does not.
And one of the really cool things about engineering is that there is a natural constraint on wasted time: engineers have to, sooner or later, make something that someone else will pay for. That is the true measure of a “useful model.”
Creating new things is not scientific. Engineers care about what works, not what is True. Nor do engineers, unlike, say, mathematicians, often make things that are perfect, that can never be improved-upon. Instead, I offer that engineers are doing something much more open-ended and interesting: engineers always have to keep working and growing and improving. There is no “best for evermore” mousetrap or software program or packaging plant.
In engineering, there is a falsifiable check at all times: are people paying for your product? As any study of the history of technology shows, it is not simple to predict what will work – at least not in advance. This trend holds in absolutely every field, from the internal combustion motor to cooling technologies to software languages. Dozens of people built flying machines before the Wright Brothers, and even after Orville and Wilbur broke the barrier, the next iteration in aerospace engineering did not retain the Wright approach to controlling flight.
Engineering consists of betting on the future, using all the tools we have to hand. Those tools include the tools of the harder sciences, but they also require substantial teams comprised of a vast range of human talent. A new drug requires not just biologists, but lab techs and quality teams, lobbyists, regulatory experts, marketing… and all the support staff to support them as well as all the tools used in drug development, tests, approvals, production and distribution. The result are companies that themselves resemble biological entities, possessing staggering capabilities, but at the cost (and even as a result) of complex and unpredictable systems and teams and individuals.
Predictive powers … your mileage will vary. On the other hand, I am personally entranced by prescriptive powers: the ability to create and shape and carve the future based on what we decide we want it to be.
There is, for example, no denying that with Elon Musk, electric cars would not be where they are now (and this is from a guy who thinks that electric cars will never compete, on a utilitarian valuation, with internal combustion-engined cars). Musk applied his vision, and sold it to people. Nobody predicted Elon Musk.
Similarly, Steve Jobs (and other great visionaries) took this one step further: he did not give people what they needed. He TOLD people what they needed, and created entirely new markets for things that people now cannot live without – but somehow had functioned perfectly well without in the past. Coupled with a great engineering company, Jobs showed that his prescriptive vision could alter the course of human history. That is impressive.
Ultimately, it is the popularization of tools that enables maximal human prescriptive powers. Edison invented the phonograph, but he thought the purpose of a phonograph was to record last wills and testaments! It was everyone else who pioneered so many other uses for analog storage systems.
From a societal level down to the individual person, visionaries create everything from new drugs and software to personalized curtains. The modern age, with our unprecedented wealth and access to tools and the knowledge of how to use them, opens the gates of heaven for every person who dares look upward.
For me, the archetypal prescriptive tool is the Torah. The text does not tell us what the natural world is, or how to use an abacus. There are no predictive tools in the Torah. But as a prescriptive document, it forms the basis of Western Civilization. The Torah tells us how we can grow, how we are to build productive and constructive and beautiful relationships with each other and with our Creator. It tells us to be holy, and then explains what holiness means.
If we think of our underlying religious presuppositions as guidance for our lives (e.g. Do we think our lives should have meaning and purpose? Can we seek to understand what that purpose can be?), then we can work to ask ourselves those questions and make something of ourselves. Not because the world is predictable, but because we each have the opportunity to help shape its future. And the sooner we all recognize and embrace this way of seeing the world, the better our future looks.