This post is once again inspired by a Ted Chiang short story. This time, it’s “The Evolution of Human Science”, which imagines a world in which super-intelligent “metahumans” have become incomprehensible to the ordinary humans they’ve left behind. Human scientists in that world practice “hermeneutics“: instead of original research, they try to interpret what the metahumans are doing, reverse-engineering their devices and observing their experiments.
It’s a thought-provoking view of what science in the distant future could become. But it’s also oddly familiar.
You might think I’m talking about machine learning here. It’s true that in recent years people have started using machine learning in science, with occasionally mysterious results. There are even a few cases of physicists using machine-learning to suggest some property, say of Calabi-Yau manifolds, and then figuring out how to prove it. It’s not hard to imagine a day when scientists are reduced to just interpreting whatever the AIs throw at them…but I don’t think we’re quite there yet.
Instead, I’m thinking about my own work. I’m a particular type of theoretical physicist. I calculate scattering amplitudes, formulas that tell us the probabilities that subatomic particles collide in different ways. We have a way to calculate these, Feynman’s famous diagrams, but they’re inefficient, so researchers like me look for shortcuts.
How do we find those shortcuts? Often, it’s by doing calculations the old, inefficient way. We use older methods, look at the formulas we get, and try to find patterns. Each pattern is a hint at some new principle that can make our calculations easier. Sometimes we can understand the pattern fully, and prove it should hold. Other times, we observe it again and again and tentatively assume it will keep going, and see what happens if it does.
Either way, this isn’t so different from the hermeneutics scientists practice in the story. Feynman diagrams already “know” every pattern we find, like the metahumans in the story who already know every result the human scientists can discover. But that “knowledge” isn’t in a form we can understand or use. We have to learn to interpret it, to read between the lines and find underlying patterns, to end up with something we can hold in our own heads and put into action with our own hands. The truth may be “out there”, but scientists can’t be content with that. We need to get the truth “in here”. We need to interpret it for ourselves.
Sabine Hossenfelder had an explainer video recently on how to tell science from pseudoscience. This is a famously difficult problem, so naturally we have different opinions. I actually think the picture she draws is reasonably sound. But while it is a good criterion to tell whether you yourself are doing pseudoscience, it’s surprisingly tricky to apply it to other people.
Hossenfelder argues that science, at its core, is about explaining observations. To tell whether something is science or pseudoscience you need to ask, first, if it agrees with observations, and second, if it is simpler than those observations. In particular, a scientist should prefer models with fewer parameters. If your model has so many parameters that you can fit any observation, you’re not being scientific.
This is a great rule of thumb, one that as Hossenfelder points out forms the basis of a whole raft of statistical techniques. It does rely on one tricky judgement, though: how many parameters does your model actually have?
Suppose I’m one of those wacky theorists who propose a whole new particle to explain some astronomical mystery. Hossenfelder, being more conservative in these things, proposes a model with no new particles. Neither of our models fit the data perfectly. Perhaps my model fits a little better, but after all it has one extra parameter, from the new particle. If we want to compare our models, we should take that into account, and penalize mine.
Here’s the question, though: how do I know that Hossenfelder didn’t start out with more particles, and got rid of them to get a better fit? If she did, she had more parameters than I did. She just fit them away.
The problem here is closely related to one called the look-elsewhere effect. Scientists don’t publish everything they try. An unscrupulous scientist can do a bunch of different tests until one of them randomly works, and just publish that one, making the result look meaningful when really it was just random chance. Even if no individual scientist is unscrupulous, a community can do the same thing: many scientists testing many different models, until one accidentally appears to work.
As a scientist, you mostly know if your motivations are genuine. You know if you actually tried a bunch of different models or had good reasons from the start to pick the one you did. As someone judging other scientists, you often don’t have that luxury. Sometimes you can look at prior publications and see all the other attempts someone made. Sometimes they’ll even tell you explicitly what parameters they used and how they fit them. But sometimes, someone will swear up and down that their model is just the most natural, principled choice they could have made, and they never considered anything else. When that happens, how do we guard against the look-elsewhere effect?
The normal way to deal with the look-elsewhere effect is to consider, not just whatever tests the scientist claims to have done, but all tests they could reasonably have done. You need to count all the parameters, not just the ones they say they varied.
This works in some fields. If you have an idea of what’s reasonable and what’s not, you have a relatively manageable list of things to look at. You can come up with clear rules for which theories are simpler than others, and people will agree on them.
Physics doesn’t have it so easy. We don’t have any pre-set rules for what kind of model is “reasonable”. If we want to parametrize every “reasonable” model, the best we can do are what are called Effective Field Theories, theories which try to describe every possible type of new physics in terms of its effect on the particles we already know. Even there, though, we need assumptions. The most popular effective field theory, called SMEFT, assumes the forces of the Standard Model keep their known symmetries. You get a different model if you relax that assumption, and even that model isn’t the most general: for example, it still keeps relativity intact. Try to make the most general model possible, and you end up waist-deep in parameter soup.
Subjectivity is a dirty word in science…but as far as I can tell it’s the only way out of this. We can try to count parameters when we can, and use statistical tools…but at the end of the day, we still need to make choices. We need to judge what counts as an extra parameter and what doesn’t, which possible models to compare to and which to ignore. That’s going to be dependent on our scientific culture, on fashion and aesthetics, there just isn’t a way around that. The best we can do is own up to our assumptions, and be ready to change them when we need to.
There’s an attitude I keep seeing among physics crackpots. It goes a little something like this:
“Once upon a time, physics had rules. You couldn’t just wave your hands and write down math, you had to explain the world with real physical things.”
What those “real physical things” were varies. Some miss the days when we explained things mechanically, particles like little round spheres clacking against each other. Some want to bring back absolutes: an absolute space, an absolute time, an absolute determinism. Some don’t like the proliferation of new particles, and yearn for the days when everything was just electrons, protons, and neutrons.
In each case, there’s a sense that physicists “cheated”. That, faced with something they couldn’t actually explain, they made up new types of things (fields, relativity, quantum mechanics, antimatter…) instead. That way they could pretend to understand the world, while giving up on their real job, explaining it “the right way”.
I get where this attitude comes from. It does make a certain amount of sense…for other fields.
An an economist, you can propose whatever mathematical models you want, but at the end of the day they have to boil down to actions taken by people. An economist who proposed some sort of “dark money” that snuck into the economy without any human intervention would get laughed at. Similarly, as a biologist or a chemist, you ultimately need a description that makes sense in terms of atoms and molecules. Your description doesn’t actually need to be in terms of atoms and molecules, and often it can’t be: you’re concerned with a different level of explanation. But it should be possible in terms of atoms and molecules, and that puts some constraints on what you can propose.
Why shouldn’t physics have similar constraints?
Suppose you had a mandatory bottom level like this. Maybe everything boils down to ball bearings, for example. What happens when you study the ball bearings?
Your ball bearings have to have some properties: their shape, their size, their weight. Where do those properties come from? What explains them? Who studies them?
Any properties your ball bearings have can be studied, or explained, by physics. That’s physics’s job: to study the fundamental properties of matter. Any “bottom level” is just as fit a subject for physics as anything else, and you can’t explain it using itself. You end up needing another level of explanation.
Maybe you’re objecting here that your favorite ball bearings aren’t up for debate: they’re self-evident, demanded by the laws of mathematics or philosophy.
Here for lack of space, I’ll only say that mathematics and philosophy don’t work that way. Mathematics can tell you whether you’ve described the world consistently, whether the conclusions you draw from your assumptions actually follow. Philosophy can see if you’re asking the right questions, if you really know what you think you know. Both have lessons for modern physics, and you can draw valid criticisms from either. But neither one gives you a single clear way the world must be. Not since the days of Descartes and Kant have people been that naive.
Because of this, physics is doing something a bit different from economics and biology. Each field wants to make models, wants to describe its observations. But in physics, ultimately, those models are all we have. We don’t have a “bottom level”, a backstop where everything has to make sense. That doesn’t mean we can just make stuff up, and whenever possible we understand the world in terms of physics we’ve already discovered. But when we can’t, all bets are off.
Both points operate on a shared kind of logic. They picture understanding something as modeling it in your mind, with every detail clear. If you’ve already used all your mind’s power to design code, you won’t be able to model when it goes wrong. And modeling your own mind is clearly nonsense, you would need an even larger mind to hold the model.
The trouble is, this isn’t really how understanding works. To understand something, you don’t need to hold a perfect model of it in your head. Instead, you translate it into something you can more easily work with. Like explanations, these translations can be different for different people.
To understand something, I need to know the algorithm behind it. I want to know how to calculate it, the pieces that go in and where they come from. I want to code it up, to test it out on odd cases and see how it behaves, to get a feel for what it can do.
Others need a more physical picture. They need to know where the particles are going, or how energy and momentum are conserved. They want entropy to be increased, action to be minimized, scales to make sense dimensionally.
Others in turn are more mathematical. They want to start with definitions and axioms. To understand something, they want to see it as an example of a broader class of thing, groups or algebras or categories, to fit it into a bigger picture.
Each of these are a kind of translation, turning something into code-speak or physics-speak or math-speak. They don’t require modeling every detail, but when done well they can still explain every detail.
So while yes, it is good practice not to write code that is too “smart”, and too hard to debug…it’s not impossible to debug your smartest code. And while you can’t hold an entire mind inside of yours, you don’t actually need to do that to understand the brain. In both cases, all you need is a translation.
No experiment is a sure thing. No matter what you do, what you test, what you observe, there’s no guarantee that you find something new. Even if you do your experiment correctly and measure what you planned to measure, nature might not tell you anything interesting.
Still, some experiments are more sure than others. Sometimes you’re almost guaranteed to learn something, even if it wasn’t what you hoped, while other times you just end up back where you started.
The first, and surest, type of experiment, is a voyage into the unknown. When nothing is known about your target, no expectations, and no predictions, then as long as you successfully measure anything you’ll have discovered something new. This can happen if the thing you’re measuring was only recently discovered. If you’re the first person who manages to measure the reaction rates of an element, or the habits of an insect, or the atmosphere of a planet, then you’re guaranteed to find out something you didn’t know before.
If you don’t have a total unknown to measure, then you want to test a clear hypothesis. The best of these are the theory killers, experiments which can decisively falsify an idea. History’s most famous experiments take this form, like the measurement of the perihelion of Mercury to test General Relativity or Pasteur’s tests of spontaneous generation. When you have a specific prediction and not much wiggle room, an experiment can teach you quite a lot.
“Not much wiggle room” is key, because these tests can all to easily become theory modifiers instead. If you can tweak your theory enough, then your experiment might not be able to falsify it. Something similar applies when you have a number of closely related theories. Even if you falsify one, you can just switch to another similar idea. In those cases, testing your theory won’t always teach you as much: you have to get lucky and see something that pins your theory down more precisely.
Finally, you can of course be just looking. Some experiments are just keeping an eye out, in the depths of space or the precision of quantum labs, watching for something unexpected. That kind of experiment might never see anything, and never rule anything out, but they can still sometimes be worthwhile.
There’s some fuzziness to these categories, of course. Often when scientists argue about whether an experiment is worth doing they’re arguing about which category to place it in. Would a new collider be a “voyage into the unknown” (new energy scales we’ve never measured before), a theory killer/modifier (supersymmetry! but which one…) or just “just looking”? Is your theory of cosmology specific enough to be “killed”, or merely “modified”? Is your wacky modification of quantum mechanics something that can be tested, or merely “just looked” for?
For any given experiment, it’s worth keeping in mind what you expect, and what would happen if you’re wrong. In science, we can’t do every experiment we want. We have to focus our resources and try to get results. Even if it’s never a sure thing.
Science is by definition empirical. We discover how the world works not by sitting and thinking, but by going out and observing the world. But sometimes, all the observing we can do can’t possibly answer a question. In those situations, we might need “non-empirical science”.
The blog Slate Star Codex had a seriesof posts on this topic recently. He hangs out with a crowd that supports the many-worlds interpretation of quantum mechanics: the idea that quantum events are not truly random, but instead that all outcomes happen, the universe metaphorically splitting into different possible worlds. These metaphorical universes can’t be observed, so no empirical test can tell the difference between this and other interpretations of quantum mechanics: if we could ever know the difference, it would have to be for “non-empirical” reasons.
What reasons are those? Slate Star Codex teases out a few possible intuitions. He points out that we reject theories that have “unnecessary” ideas. He imagines a world where chemists believe that mixing an acid and a base also causes a distant star to go supernova, and a creationist world where paleontologists believe fossils are placed by the devil. In both cases, there might be no observable difference between their theories and ours, but because their theories have “extra pieces” (the distant star, the devil), we reject them for non-empirical reasons. Slate Star Codex asks if this supports many-worlds: without the extra assumption that quantum events randomly choose one outcome, isn’t quantum mechanics simpler?
Ultimately, we trust science because it allows us to do things. If we understand the world, we can interact with it: we can build technology, design new experiments, and propose new theories. With this in mind, we can judge scientific theories by how well they help us do these things. A good scientific theory is one that gives us more power to interact with the world. It can do this by making correct predictions, but it can also do this by explaining things, making it easier for us to reason about them. Beyond empiricism, we can judge science by how well it teaches us.
This gives us an objection to the “supernova theory” of Slate Star Codex’s imagined chemists: it’s much more confusing to teach. To teach chemistry in that world you also have to teach the entire life cycle of stars, a subject that students won’t use in any other part of the course. The creationists’ “devil theory” of paleontology has the same problem: if their theory really makes the right predictions they’d have to teach students everything our paleontologists do: every era of geologic history, every theory of dinosaur evolution, plus an extra course in devil psychology. They end up with a mix that only makes it harder to understand the subject.
Many-worlds may seem simpler than other interpretations of quantum mechanics, but that doesn’t make it more useful, or easier to teach. You still need to teach students how to predict the results of experiments, and those results will still be random. If you teach them many-worlds, you need to add more discussion much earlier on, advanced topics like self-localizing uncertainty and decoherence. You need a quite extensive set of ideas, many of which won’t be used again, to justify rules another interpretation could have introduced much more simply. This would be fine if those ideas made additional predictions, but they don’t: like every interpretation of quantum mechanics, you end up doing the same experiments and building the same technology in the end.
I’m not saying I know many-worlds is false, or that I know another interpretation is true. All I’m saying is that, when physicists criticize many-worlds, they’re not just blindly insisting on empiricism. They’re rejecting many-worlds, in part, because all it does is make their work harder. And that, more than elegance or simplicity, is how we judge theories.
Earlier this year, I made a list of topics I wanted to understand. The most abstract and technical of them was something called “Wilsonian effective field theory”. I still don’t understand Wilsonian effective field theory. But while thinking about it, I noticed something that seemed weird. It’s something I think many physicists already understand, but that hasn’t really sunk in with the public yet.
There’s an old problem in particle physics, described in many different ways over the years. Take our theories and try to calculate some reasonable number (say, the angle an electron turns in a magnetic field), and instead of that reasonable number we get infinity. We fix this problem with a process called renormalization that hides that infinity away, changing the “normalization” of some constant like a mass or a charge. While renormalization first seemed like a shady trick, physicists eventually understood it better. First, we thought of it as a way to work around our ignorance, that the true final theory would have no infinities at all. Later, physicists instead thought about renormalization in terms of scaling.
Imagine looking at the world on a camera screen. You can zoom in, or zoom out. The further you zoom out, the more details you’ll miss: they’re just too small to be visible on your screen. You can guess what they might be, but your picture will be different depending on how you zoom.
In particle physics, many of our theories are like that camera. They come with a choice of “zoom setting”, a minimum scale where they still effectively tell the whole story. We call theories like these effective field theories. Some physicists argue that these are all we can ever have: since our experiments are never perfect, there will always be a scale so small we have no evidence about it.
One theory like this is Quantum Chromodynamics (or QCD), the theory of quarks and gluons. Zoom in, and the theory looks pretty much the same, with one crucial change: the force between particles get weaker. There’s a number, called the “coupling constant“, that describes how strong a force is, think of it as sort of like an electric charge. As you zoom in to quarks and gluons, you find you can still describe them with QCD, just with a smaller coupling constant. If you could zoom “all the way in”, the constant (and thus the force between particles) would be zero.
This makes QCD a rare kind of theory: one that could be complete to any scale. No matter how far you zoom in, QCD still “makes sense”. It never gives contradictions or nonsense results. That doesn’t mean it’s actually true: it interacts with other forces, like gravity, that don’t have complete theories, so it probably isn’t complete either. But if we didn’t have gravity or electricity or magnetism, if all we had were quarks and gluons, then QCD could have been the final theory that described them.
And this starts feeling a little weird, when you think about reductionism.
Philosophers define reductionism in many different ways. I won’t be that sophisticated. Instead, I’ll suggest the following naive definition: Reductionism is the claim that theories on larger scales reduce to theories on smaller scales.
Here “reduce to” is intentionally a bit vague. It might mean “are caused by” or “can be derived from” or “are explained by”. I’m gesturing at the sort of thing people mean when they say that biology reduces to chemistry, or chemistry to physics.
What happens when we think about QCD, with this intuition?
QCD on larger scales does indeed reduce to QCD on smaller scales. If you want to ask why QCD on some scale has some coupling constant, you can explain it by looking at the (smaller) QCD coupling constant on a smaller scale. If you have equations for QCD on a smaller scale, you can derive the right equations for a larger scale. In some sense, everything you observe in your larger-scale theory of QCD is caused by what happens in your smaller-scale theory of QCD.
But this isn’t quite the reductionism you’re used to. When we say biology reduces to chemistry, or chemistry reduces to physics, we’re thinking of just a few layers: one specific theory reduces to another specific theory. Here, we have an infinite number of layers, every point on the scale from large to small, each one explained by the next.
Maybe you think you can get out of this, by saying that everything should reduce to the smallest scale. But remember, the smaller the scale the smaller our “coupling constant”, and the weaker the forces between particles. At “the smallest scale”, the coupling constant is zero, and there is no force. It’s only when you put your hand on the zoom nob and start turning that the force starts to exist.
It’s reductionism, perhaps, but not as we know it.
Now that I understand this a bit better, I get some of the objections to my post about naturalness a while back. I was being too naive about this kind of thing, as some of the commenters (particularly Jacques Distler) noted. I believe there’s a way to rephrase the argument so that it still works, but I’d have to think harder about how.
I also get why I was uneasy about Sabine Hossenfelder’s FQXi essay on reductionism. She considered a more complicated case, where the chain from large to small scale could be broken, a more elaborate variant of a problem in Quantum Electrodynamics. But if I’m right here, then it’s not clear that scaling in effective field theories is even the right way to think about this. When you have an infinite series of theories that reduce to other theories, you’re pretty far removed from what most people mean by reductionism.
Finally, this is the clearest reason I can find why you can’t do science without an observer. The “zoom” is just a choice we scientists make, an arbitrary scale describing our ignorance. But without it, there’s no way to describe QCD. The notion of scale is an inherent and inextricable part of the theory, and it doesn’t have to mean our theory is incomplete.
Experts, please chime in here if I’m wrong on the physics here. As I mentioned at the beginning, I still don’t think I understand Wilsonian effective field theory. If I’m right though, this seems genuinely weird, and something more of the public should appreciate.
On one hand, the practical benefits of a 53-qubit computer are pretty minimal. Scott discusses some applications: you can generate random numbers, distributed in a way that will let others verify that they are truly random, the kind of thing it’s occasionally handy to do in cryptography. Still, by itself this won’t change the world, and compared to the quantum computing hype I can understand if people find this underwhelming.
Ok, I’m actually just re-phrasing what I said before. The Extended Church-Turing Thesis proposes that a classical computer (more specifically, a probabilistic Turing machine) can efficiently simulate any reasonable computation. Falsifying it means finding something that a classical computer cannot compute efficiently but another sort of computer (say, a quantum computer) can. If the calculation Google did truly can’t be done efficiently on a classical computer (this is not proven, though experts seem to expect it to be true) then yes, that’s what Google claims to have done.
So we get back to the real question: should we be impressed by quantum supremacy?
Well, should we have been impressed by the Higgs?
The detection of the Higgs boson in 2012 hasn’t led to any new Higgs-based technology. No-one expected it to. It did teach us something about the world: that the Higgs boson exists, and that it has a particular mass. I think most people accept that that’s important: that it’s worth knowing how the world works on a fundamental level.
Google may have detected the first-known violation of the Extended Church-Turing Thesis. This could eventually lead to some revolutionary technology. For now, though, it hasn’t. Instead, it teaches us something about the world.
It may not seem like it, at first. Unlike the Higgs boson, “Extended Church-Turing is false” isn’t a law of physics. Instead, it’s a fact about our capabilities. It’s a statement about the kinds of computers we can and cannot build, about the kinds of algorithms we can and cannot implement, the calculations we can and cannot do.
Facts about our capabilities are still facts about the world. They’re still worth knowing, for the same reasons that facts about the world are still worth knowing. They still give us a clearer picture of how the world works, which tells us in turn what we can and cannot do. According to the leaked paper, Google has taught us a new fact about the world, a deep fact about our capabilities. If that’s true we should be impressed, even without new technology.
I’ve been reading a biography of James Clerk Maxwell, who formulated the theory of electromagnetism. Nowadays, we think about the theory in terms of fields: we think there is an “electromagnetic field”, filling space and time. At the time, though, this was a very unusual way to think, and not even Maxwell was comfortable with it. He felt that he had to present a “physical model” to justify the theory: a picture of tiny gears and ball bearings, somehow occupying the same space as ordinary matter.
Maxwell didn’t think space was literally filled with ball bearings. He did, however, believe he needed a picture that was sufficiently “physical”, that wasn’t just “mathematics”. Later, when he wrote down a theory that looked more like modern field theory, he still thought of it as provisional: a way to use Lagrange’s mathematics to ignore the unknown “real physical mechanism” and just describe what was observed. To Maxwell, field theory was a description, but not an explanation.
This attitude surprised me. I would have thought physicists in Maxwell’s day could have accepted fields. After all, they had accepted Newton.
In his time, there was quite a bit of controversy about whether Newton’s theory of gravity was “physical”. When rival models described planets driven around by whirlpools, Newton simply described the mathematics of the force, an “action at a distance”. Newton famously insisted hypotheses non fingo, “I feign no hypotheses”, and insisted that he wasn’t saying anything about why gravity worked, merely how it worked. Over time, as the whirlpool models continued to fail, people gradually accepted that gravity could be explained as action at a distance.
You’d think that this would make them able to accept fields as well. Instead, by Maxwell’s day the options for a “physical explanation” had simply been enlarged by one. Now instead of just explaining something with mechanical parts, you could explain it with action at a distance as well. Indeed, many physicists tried to explain electricity and magnetism with some sort of gravity-like action at a distance. They failed, though. You really do need fields.
The author of the biography is an engineer, not a physicist, so I find his perspective unusual at times. After discussing Maxwell’s discomfort with fields, the author says that today physicists are different: instead of insisting on a physical explanation, they accept that there are some things they just cannot know.
At first, I wanted to object: we do have physical explanations, we explain things with fields! We have electromagnetic fields and electron fields, gluon fields and Higgs fields, even a gravitational field for the shape of space-time. These fields aren’t papering over some hidden mechanism, they are the mechanism!
Are they, though?
Fields aren’t quite like the whirlpools and ball bearings of historical physicists. Sometimes fields that look different are secretly the same: the two “different explanations” will give the same result for any measurement you could ever perform. In my area of physics, we try to avoid this by focusing on the measurements instead, building as much as we can out of observable quantities instead of fields. In effect we’re going back yet another layer, another dose of hypotheses non fingo.
Physicists still ask for “physical explanations”, and still worry that some picture might be “just mathematics”. But what that means has changed, and continues to change. I don’t think we have a common standard right now, at least nothing as specific as “mechanical parts or action at a distance, and nothing else”. Somehow, we still care about whether we’ve given an explanation, or just a description, even though we can’t define what an explanation is.
I was talking with some other physicists about my “Black Box Theory” thought experiment, where theorists have to compete with an impenetrable block of computer code. Even if the theorists come up with a “better” theory, that theory won’t predict anything that the code couldn’t already. If “predicting something new” is an essential part of science, then the theorists can no longer do science at all.
One of my colleagues made an interesting point: in the thought experiment, the theorists can’t predict new behaviors of reality. But they can predict new behaviors of the code.
Even when we have the right theory to describe the world, we can’t always calculate its consequences. Often we’re stuck in the same position as the theorists in the thought experiment, trying to understand the output of a theory that might as well be a black box. Increasingly, we are employing a kind of “experimental theoretical physics”. We try to predict the result of new calculations, just as experimentalists try to predict the result of new experiments.
This experimental approach seems to be a genuine cultural difference between physics and mathematics. There is such a thing as experimental mathematics, to be clear. And while mathematicians prefer proof, they’re not averse to working from a good conjecture. But when mathematicians calculate and conjecture, they still try to set a firm foundation. They’re precise about what they mean, and careful about what they imply.
“Experimental theoretical physics”, on the other hand, is much more like experimental physics itself. Physicists look for plausible patterns in the “data”, seeing if they make sense in some “physical” way. The conjectures aren’t always sharply posed, and the leaps of reasoning are often more reckless than the leaps of experimental mathematicians. We try to use intuition gleaned from a history of experiments on, and calculations about, the physical world.
At the same time, experimental theoretical physics has real power. Experience may be a bad guide to mathematics, but it’s a better guide to the mathematics that specifically shows up in physics. And in practice, our recklessness can accomplish great things, uncovering behaviors mathematicians would never have found by themselves.
The key is to always keep in mind that the two fields are different. “Experimental theoretical physics” isn’t mathematics, and it isn’t pretending to be, any more than experimental physics is pretending to be theoretical physics. We’re gathering data and advancing tentative explanations, but we’re fully aware that they may not hold up when examined with full rigor. We want to inspire, to raise questions and get people to think about the principles that govern the messy physical theories we use to describe our world. Experimental physics, theoretical physics, and mathematics are all part of a shared ecosystem, and each has its role to play.