Everyone. Welcome to Nontrivial. I'm your host, Sean mcclure. In this episode, I talk about the current paradigm of science and challenge it by arguing that much of today's science is not growing knowledge over time. Contrary to what most people assume that while science keeps saying more, it only does. So by explaining less, stick around to find out what I mean.
OK. So let's talk about science in this episode and more precisely how people think about science, what do we think science is, it usually gets portrayed obviously as this enterprise that works towards the accumulation of knowledge for society, right? And it it takes a certain approach to do this.
It's uh the idea that we're going to gain knowledge objectively, we're going to use tools and techniques, methods that allow us to observe and make comments about nature in a relatively objective fashion. And by doing that, we're trying to get past, you know, the human biases and maybe superstition and other things that might otherwise presumably are supposedly get in the way of pursuing a kind of truly objective understanding of nature.
We see all kinds of things around us, different phenomena we experience things throughout our lives. And science is supposed to be there to try to figure out the, the the why and the how behind that or particularly the how, right? Not always, rarely the why but definitely the how, how does it tick, how do, what do you know, what are the underlying mechanisms that make what we experience happen? So science is the enterprise of, of accumulating knowledge objectively for society, right?
You could use a definition like that. And I think that's how most of us would think about science. That's definitely how it's portrayed in our, our schools, our institutions as we learn about, you know, the different topics in science and we consume the different textbooks and we interact with the various teachers across different fields. This is the story that is told and uh and different people will take science to different levels.
Some people will, you know, kind of stop at the high school level. In other words, will go on to graduate school and make a career out of it. But this is how relatively everyone thinks about science. Well, does it make sense to take science just at face value like that? Meaning regardless of how involved in science you are, whether you're a layman or whether you're actually going deep and doing a lot of research at kind of a uh almost a philosophical level? Does it make sense to just think that?
Yeah, regardless of what you're doing and what you're studying, if you're using the so-called scientific method, then you are accumulating knowledge for society. We are learning more, we are explaining more things with science and that's, that's a good thing to do, right? That's a, that is a worthwhile activity for humans because you know, the, the the narrative is that, that is that, that has led to many things.
You know, we have medicines, we have different machines, we've improved efficiencies, we've become more productive across a number of areas. Um You know, we've extracted resources that obviously could be painted as a good or a bad thing, but we've done a lot of things to presumably progress society uh owed a lot to science owing a lot to science. OK. So, but does that make sense? Well, what I want to talk about in this episode is what do we really mean when we say we are explaining things?
I mean, the idea that science is progressing and, and, and as a, as a kind of a byproduct of that, that society is progressing because of science. The idea is that we're explaining more and more things, right? You would have to accept that if you think science kind of has anything good about the current paradigm, you, you would, you would have to accept this idea that science is explaining more and more things as it progresses. Otherwise it wouldn't really be progressing, right?
What would science be if it's not? You know, if 30 years ago, we're saying the exact same thing that we're saying today, then you would say, well, science isn't really progressing if it was just saying the same thing, it was saying 200 years ago, right? You would start to say, well, you know, why are we doing this? Right. Uh Obviously, we, we all tend to kind of accept that face value that science is growing, that it's evolving, that it's progressing, that it.
And what we mean by that is that it is explaining more things as time passes. But I want to challenge that notion. OK, that science is explaining more things. And I'm not saying that I wholeheartedly wholeheartedly disagree with that idea and I'll explain why I want to challenge that.
But, but I, but I want to set up this episode so that, that the listeners understand that what I'm going to do is that if we take an intellectually honest, look at what the current paradigm of science is and how it works, that this idea that it's just explaining more and more things is problematic. And, and, and I would, I would argue actually downright faulty now, that isn't the same thing as saying all of science is, is like, you know, bad or not good or we shouldn't be doing it.
But it is challenging the current paradigm of science and, and, and towards the end of the episode, I will be arguing for a new paradigm, a new direction that science should actually take. Now, why does anybody care about this? I mean, if you're scientific, you probably want to hear something about this, you know, what's this guy talking about?
But I think this does affect everybody because, you know, as I, as I and others have mentioned before, I mean, science works its way into policies, right? Uh Things get defended as being scientifically as being more true because they are scientific. And so this does affect everyone's lives and and the quality of our lives and the way we go about making decisions.
So it's kind of something that everybody should have, you know, an operative knowledge about not the deep details of how science is necessarily happening, but more of a kind of meta level philosophical understanding of what does science mean, why is it important? Is it important, is it happening the right way? Is it worth trusting? Right? It gets into all those issues. So that's why I think it's important to understand this.
So I'm going to challenge this idea in this episode that science is explaining more and more things over time. OK. So, so let's let's get into it because what the heck am I talking about? Well, how could it not be explaining more things? I mean, our textbooks get thicker, right? We we we have more to say and I agree with all those things. Science definitely has more to say over time, but that's not necessarily the same as it, explaining more things.
OK. So to understand this, I want to talk about what I'm gonna call the great disconnect. OK? There is this great disconnect between things that go into a system and things that go or, or, or that come rather out of a system, right? The inputs and the outputs.
So let's just step back and think of any, anything you experience in life, anything you measure scientifically, anything you observe, anything you experience in any kind of way can be thought of as basically a system with inputs and outputs, right? There are things that went into the thing you're looking at and there are things that are being shown to you as outputs that come out of it, right?
If I look at a sunset, if I look at a rainbow, if I look at cloud formations, if I look at a buffalo herd, if I look at the dispersion of light through a prism, if I look at grass growing trees growing, you know, animal behavior, whatever it is, anything, anything scientifically across any field of science, I am looking at outputs being generated by the phenomenon, right? Outputs because my eyes are receiving the information, my ears are receiving the information.
So there are outputs being produced. And then of course, there's something that went into that system that I'm looking at as well, right? So if I'm looking at buffalo herd formation, there's individual buffaloes that came together to make the herd formation happen. If I'm looking at the dispersion of light through a prism, right? Photons must have been impinging on the prism and going through some kind of mechanism and then producing the output.
So there's always some input that comes in and output that comes out. What I want to talk about is the great disconnect between the input and the output. Now, what do I mean by great disconnect? Well, let's, let's step back and think about how science tries to understand the phenomenon that it looks at. OK, that we look at that we measure that we experience the way it does. The, the way it goes about this for the most part is essentially reverse engineering, right?
It takes the phenomenon and it starts to peel back the layers, it starts to strip things away, remove a lot of the context until it gets down to the individual components of the thing, right? I'm looking at buffalo herd formations. I'm gonna start to pick apart the little agents that come together to make that happen. I'm gonna try to understand light. I'm gonna try to understand it in a part, uh particulate and and maybe wavelike manner, right? What are the, what are the individual components?
What's the recipe of the thing? Right. I'm trying to understand water, you peel it back to the molecules, to the atoms that come together to make molecules, describe things in terms of intermolecular interactions and on and on whatever it is we're looking at science, we essentially are reverse engineering and, and taking the approach of isolating, extracting and stripping away until we've got these kind of isolated sterile individual components.
And we say here is the thing and the more that we can kind of strip away and isolate and define the more we have to say about the thing about the phenomenon, right? And so the progress in science so goes the narrative is that as we continue to peel back the layers and strip away and expose more of the components or more things about those components that we are advancing human knowledge.
But there's something wrong with this, this this idea of reverse engineering in order to say more about the thing. And I've alluded to that and this is a common theme obviously through my episode because I talk about complexity and anybody who is privy to, you know, issues of kind of a scientific look at complexity would be familiar with this. This is very much reductionism, right? We're, we're peeling things away, we're looking at the components.
And then we're kind of making this assumption that knowledge of the components is telling us something about what we actually experience what we actually measure at the output level. But that's not true, that's not true. And we know that's not true. We know that the properties of the individual components are rarely if ever reflected in the actual emergent properties that we experience. For most things that we do experience, most things are nontrivial. Most things are complex.
Most things are not simple sterile kind of laboratory style setups that you might use to extract and isolate the extraction and isolating that's happening in science is removing a lot of the complexity from the situation in order to study it, it's removing it as though it was superfluous, but it's not super superfluous is it that complexity is absolutely critical.
And in in fact, it's very much the thing itself, we we, you know, I've talked about hidden dependencies and interconnections between how components come together in ways that we don't really know, we know they come together, we know at the top level that produce properties, we know at the low level, the properties of the individual components. But the connection between the properties of the components and the top level stuff we experience is virtually nonexistent.
Now, it might actually exist in some deterministic fashion, right? The components are presumably coming together doing something and producing output, right? I don't think anybody's suggesting that magic is happening in the middle. The point is we don't have access to the path that happens between input and output. We don't.
But this is an assumption that many people make layman, definitely most laymen would probably assume that science is doing a pretty good job at connecting the inputs to the outputs, right? They would assume that when people are talking about particle physics that that's leading to an explanation of some of the big stuff we see in the universe.
They're assuming that when we talk about genetics and we look at the little isolated informational components that go into uh you know, life, right, that go into biology, that there's this connection between those individual little pieces, uh you know, those genes and the stuff that gets experienced and measured and observed at the life level. That is a drastic, drastic assumption, which quite frankly isn't backed up by any of the history of science.
Now, people are gonna challenge that because that makes a lot of people uncomfortable and we have statistical techniques that presumably try to tease out a lot of the causality between input and output. We have randomized controlled trials, we have all that kind of stuff. OK? But a lot of that is, is hints at what might be associated.
It is filled with a lot of narrative that assumes that the input is connected to the output that the properties of what we see on the small scale are somehow being reflected at the large scale. Let's use the water example, I use this a lot just to make sure the listeners know what I'm talking about. So when I say the large scale, the high scale, you know the top level scale, you think about the wetness of water, right?
This is a common example, you you can pick apart water all you want look at the molecules, the atoms, the intermolecular interactions, but you can't use that to tell a story of wetness, right? I mean, you could because you can always use anything to tell any story you want, right? A complete lie is a story too. And I'm not saying scientists are lying, but you could come up with a narrative, but you can't actually make the connection between the water molecules and wetness.
OK. Now, that's not to say that there aren't techniques that try to kind of do this. But it's, it's, it's, there's a lot of narrative here, there's renormalization, there's mean field theories, there's ways that are basically they, they all fall under the same category which is just taking averages. OK. That's what they do. They, they, they treat the large phenomena as a statistical ensemble.
And they say, well, statistically on average, this is what's happening virtually every approach in the statistical arsenal of science does this in one way or another is taking averages and of course averages are not real. They don't really reflect what's happening. There are approximate methods. Now, those approximations might work, they do work in a lot of ways. If you're just trying to like understand, let's say the behavior at the top level of something, right.
So there's nothing wrong with using approximations and averages. We do this all the time. It's had a ton of success in science. But keep in mind what I'm talking about here, I'm talking about the bridge between the input and the output. Those approximations do not craft a bridge between input and output. They do not tell a story of what is happening. An average is not what's happening, right?
If I take the average of a big group of people and say the average person does this, that doesn't tell me why they do it. That doesn't even tell me how they do it. You're just giving me the average value at the top level.
So you, in other words, you can talk about the inputs, you can talk about the outputs, but science does not talk about the bridge between at least, not very well when they do talk about it so-called causal inference or statistical approaches that suggest that this is associated with this even then that's not really talking about the bridge. Is it, that's just saying the input seems to be associated to the output in some fashion, but it's a huge approximation.
It's a very loose correlation and that's assuming that the correlation is even being done rigorously or correctly to begin with. And of course, that is a whole another topic, right? Science is very much in a crisis right now because of so-called causal inference. Quite frankly, it's it's uh the the statistical techniques are ridiculously simplistic, they are abused. We have a replication crisis. Science is not going well in 2021 quite frankly, that's not what really what I'm here to talk about.
But that is a byproduct of some of the things I'm talking about. So let's go back to that great disconnect that I'm saying. There is this great disconnect between input and output the input is always a simple, simple system, right? Individual atoms, individual molecules, those could eventually get pretty big biomolecular modeling, but still relatively simple compared to, you know, an organism, right?
The way we actually experience and measure things in everyday life, science is stripping away, extracting and isolating in order to say more. OK, that's how science says more. That's how science quote unquote progresses. It's the only way it can quite frankly say more and progress is by strip away, strip away and isolating. Even if you're doing things at the output level where you're trying to explain the behavior. You're using these very high statistical approximate methods.
You still have to leave a ton of complexity out because you don't have access to the complexity. You don't have access to the true story between how inputs become outputs. You might get odd hints, statistical little correlations that suggest something is mapping but you cannot paint that picture. So why am I talking about this? Why? What I mean should we even care? Do we even need the bridge?
Why can't we just go study the components set up our particle accelerators and and learn more about the fundamental particles of the universe? That's interesting. It is interesting I mean, that is still nature. And why can't we just do you know, approximate statistical methods and entropy and all this kind of stuff at the high level? So we can say here's how thermal currents and fluid dynamics work and all this, you know, herd formations of buffalo and all that kind of stuff.
I mean, that's interesting. It is interesting and it is nature, we should still do those things. OK? But, but there's something wrong with this assumption that those two things are connected somehow or that we will ever have access to how the individual components become the things we actually experience and measure. OK. This gets called a number of things, causal opacity. It's opaque, we don't have access. It's an abrupt transition. I don't like the idea that there are levels of complexity.
Some people talk about that. I don't think there are levels of complexity. I think you're either simple or you're complex, you're in one regime or you're in the other and the transition is abrupt. And as soon as you surpass that complexity threshold, you do not have access to how something came about. Now. That doesn't sound very scientific, does it? I mean, we tend to assume that science is uncovering the mechanisms of how things come.
They might not know the why, but they're supposed to know the how today's current paradigm of science is doing an abysmal job at explaining the how. And I don't think it's ever going to happen. Because quite frankly, there's never really been progress in explaining the how now that is going to upset a lot of people, people are going to debate that they're going to say what are you talking about?
But again, go back to the great disconnect if you go to a particle accelerator and you start talking about all the particles, you're not just explaining the how because those things would have to come together in a complex fashion to, to, to, to produce phenomena that are nontrivial. Now, if the phenomena that are being produced are simple, that's a different story. But again, 98 99% of everything we experience is non-trivial. So what are you explaining with your particle accelerator?
What are you explaining with genetics? Explaining more about genes is circular? Because you're just talking about the genes. You're not talking about how they become anything but you are assuming that they become something. If you're just talking about the particles, you're just talking about the particles, but you don't know how they become anything, how things become something is not something that has made much of any progress in science.
We know this, the transition is abrupt between the simple and the complex. We can't break past the opacity. It's fundamental.
So that great disconnect between input and output, between simple and complex, between components and things we actually measure and experience makes the narrative that science is growing humanity's knowledge somewhat problematic because what is it that you're growing if you just keep talking about something like particles and components and you know, more and more about those particles, but they can never be connected to the things we actually measure and experience.
And what is it that you're growing? What is it that you're explaining? OK. Now, these narratives of course, exist as though the things are connected because people do this all the time, layman and scientists operate as though there's that, that that connection exists. Now again, the connection itself might exist, but we don't have access to it. We can't figure out the mechanism between how those inputs are become. We have statistical averaging, we have randomized controlled trials.
We can try to like little correlations at associations, but we can't make the connection. So explaining things more about the components is not explaining how the thing is actually arising. And because of that great disconnect, when we think about science getting into policy, science, getting into how we understand the world. Well, what do you mean by understand so much so that it's actually quite dangerous?
Remember, I use the calcium example, if you see a prevalence of a mineral like calcium in someone's body, you're going to assume that therefore, I should just eat a bunch of calcium, right? That is, that is a stupid idea. Now, I'm not saying we shouldn't be eating calcium and I'm not saying calcium is unhealthy, but you would eat a lot more than just I saw it at the output. Therefore, I'm gonna intake it on the input because of the great disconnect. We don't know why calcium is there.
It could be a byproduct of another process altogether. It might not even play a role. I'm not saying calcium doesn't play a role. But the point is is we do this all the time in science where we look at something at the output, we are assuming that therefore more of the input must be good or bad or must lead to the thing we do this in genetics. We think that genes play a role in disease. We see disease, we study people with disease.
We'll notice that there's a prevalence or, or a lack of a gene, let's say with people with disease. Therefore, we go back to the input and then we start doing things at the genetic level because we think we can now influence the disease. But you might not be able to, there's a good chance you won't be able to or you might do it by happenstance. It would be lucky. Even if you did, you don't actually have the control, you think you do because the input does not map to the output.
No matter how much you study about genes, it's not going to tell you more about the disease because the disease is not the same thing as the genes, even though it comes from the genes, OK. Inputs are interacting in such a complex convoluted manner that whatever we experience is emergent, it doesn't have the properties of the little inputs that you're studying. So this is hard for people to get their mind around because we just assume that this is the case, we reverse engineer the system.
We look at the individual components, we learn more about the genes, we learn more about the particles and the atoms and the molecules or whatever it is. You're studying the individual animals that go into her formation. We keep studying the little pieces and then we have lots more to say about those little pieces. But because of the great disconnect between input and output, what is it?
You're actually saying it's circular, it's circular and so progress in science under the current paradigm is very, very circular, which is problematic, right? It just folds in back on itself. In other words, if you say that you are making progress in genetics, really, what you're saying is you're developing new tools and techniques to study genes more. Yeah. OK. I get that. But is that progress? Well, sure. Well, why is that progress?
You have to eventually say because you think genes become the output but they don't they, or, or, or, or however they do, we don't have access to it. You understand how that's circular. You're, you're saying more about genes, you're saying more about genetics, but you don't have the path of how genes even become the output and any type of influence of genes to produce the output, which quite frankly has been an abysmal failure is not happening.
OK, I'll, I'll talk about the human genome project in a second. I'll talk about the last 30 years of particle physics. In a second. We know that these things aren't working. We know that these things are not actually connecting to the output. You might influence it, but it could be very dangerous. It might be positive by luck, but you don't have the control.
So every scientist is operating under this assumption that what they do at the component level, the input level, the reverse engineering side of things is connected to the output level, the stuff we experience and measure in our lives. But the great disconnect shows us that because there's this abrupt complexity threshold where we go from simple to complex that connection does not exist.
So to say that you're saying more about science that you're growing the body of knowledge of science, this great disconnect precludes that that makes it problematic. So why do we have narratives? Why do we talk all the time as though there is this connection between the input and the output? Well, we we we can't help but do this, right. That's what a narrative is. You know, we can't create the way we consume.
I've talked about this in a number of different kind of facets, a number of different ways. Let's take zebras. Ok, let's take biology. I like the zebra example, people including, like layman as well as scientists always, always talk about biology just in an awful way. They talk about it as though it has a purpose. Right. This is a problem. Right. Um, why does zebra have stripes? Well, zebra has stripes in order to protect itself from it, from the predators, right?
Because the stripes will confuse, you know, the lion or whatever that's chasing the zebra because it's striped and it's moving. You can't tell which way it's moving. That's the answer. There, there's another answer. Might cool the skin of the zebra. You might get these little, uh, you know, air eddy currents that go from the black to the white or back and forth because you've got different densities of air based on what's heating up and what's not. And then it cools the skin of the zebra.
Ok. Whatever it, so it's confusing its predator and, or it's keeping it cool. No, that's, that's a stupid, that's a stupid explanation. That is not why a zebra has stripes. Ok. A zebra has stripes because the stripe zebra survived. That's it. Now, why did they survive? Well, they survived because it's probably more confusing to go after a zebra to go after an animal stripes because you can't tell which way it's moving. Now, it sounds like those are the same thing.
The first one I said was stupid and then I gave you an answer. That kind of sounds like the same thing, but they're not the same thing. Those are very different. In fact, they're completely opposite. They're diametrically opposed to each other saying that a zebra has some type of properties to it. Because as soon as you say that you're, you're going down the wrong path, you, you're facing the wrong direction. They don't have reasons.
The only reason we see the patterns that we have around us is because those are the patterns that have survived. They have adapted to the stresses of the environment, right? The zebra doesn't know it even has stripes or, or doesn't think in terms of that, he doesn't have a purpose to it. The Venus fly trap I I is, is around today because if you catch fly with a big mouth, whatever it's called on the plant, uh you're, you're gonna ingest the resources you need to survive.
But people would talk about the Venus fly trap as though it's been, you know, almost like it's been designed, right? Like it has, you know, this in order to, to capture the fly in order to ingest. No, it's not, it's not a purpose driven thing. And if the stressor in the environment changes, then those aren't going to be useful anyway, right?
It's important to understand, it sounds like a subtle difference, but it's critical that you understand the directionality of why things are the way they are Right. And how they are. So, so people talk and this is why a lot of people get confused, I think about evolution.
They don't really understand it as a process because they don't, because even the biologists aren't quite frankly talking about biology that well when it comes to teaching people the process of how things work and they do this because we're humans, we all do this. We, we talk as though things are purpose driven, right? That's the way we tell stories, narrative. The zebra looks like this because the plant looks like this because no, nothing is looking a certain way because of anything.
It's just it survived out of all the non striped zebras that definitely existed, right? Whether you considered it a zebra at the time or not, those ones got killed more easily than the stripe. That's, that's, that's how things evolved, that's not relegated to just biology, right? That's, that's any process that, that evolves. And then it goes, adaptation has to have a massive amount of variation 90 to, to, to, to 95% of which has to get killed off. Nobody's imm to that, right?
So there's this directionality problem where people, so let's go back to that kind of great disconnect where people are talking about inputs as though they've got this purpose to become the output, right? You know, the particles look like this because, and then they come up with an explanation. OK? Because because they're giving it a purpose, they're giving the, the atoms and the molecules a reason to do what they do.
And therefore, OK, so, so I'm explaining this difference because instead of things looking the way they do in order to produce a certain outcome, they look the way they do because out of all the other ones that existed, these ones survived the best, that's why things look the way they do. OK? Again, it sounds like a subtle difference, but it's actually a fundamentally important difference to understand.
And I call that directionality because they, they're basically, you know, it's not, it's not something existing a certain way in order to become a thing, it's a thing that exists because it survived and, and it's, it's directional because this gets into how we start to talk about science. Let, let's, let's think about the story that we're told.
You know, I've talked about the academic narrative before where, you know, we have all these textbooks and we learn from these teachers and we, we, we basically learn a foundation of concepts and methods and approaches and tools in science to then presumably go do a bunch of things that will be good for humanity, right? Uh and, and grow the knowledge, but that's the wrong direction.
Information, that is the wrong direction because those things don't come together to produce the thing, the thing is, is discovered largely by happenstance. And then we create the textbooks out of the discovery. OK. It's the same thing. We humans can't help think that we create foundations and then we use the foundations to build things because that's how we use that. That that's how it goes for very simple things, right?
When you build like office towers and, and rocket engines and train tracks, which might not sound simple, but they are by definition, simple things because they're fully deterministic. You can always debug them, you can always see how the inputs become the outputs. They haven't passed the complexity threshold.
So we tend to think that everything operates in that fashion, but something like scientific discovery and creativity and you know, things that aren't simplistic aren't industrial revolution style. They are not like that. We know that they don't operate like that the inputs don't just map determinist deterministic to output, you know, or even if they did, we would never have access to how exactly that happened. And so it's the same thing, it just, it doesn't, it emerges, right?
That's not a hand wavy thing. That is a definite thing, that complexity threshold. So that's why people are, are, are talking if you go talk to a geneticist or you go talk to a particle physicist, you go talk to any scientist, they talk as though there's this narrative between the input and the output as though they, that the individual unit pieces come together to produce the thing. And we just kind of assume that.
And so the more you talk about the individual pieces you must by default or, or at least by proxy be talking about how the thing becomes. Yelp. But you're not doing that because there's a fundamental great disconnect. That's the wrong direction. Instead, what it is is things get revealed that we experience and that we can measure and then we go back and then we kind of pick it apart and say, and therefore here's all the things that went into it, right?
You notice the disease, then you go back and say, I'm gonna now map this disease genetically. And now here's all the genes, right? You notice, you know the the the the influence of black hole, right, the the mass of gravity and the densities that are occurring in the universe, right?
Uh maybe the rotation uh you know velocities or whatever of the spiraling Galaxies, you notice that they change and therefore there's black holes and then you go back to the particle accelerator and say blah blah blah blah blah symmetry, right there something about the particles, you know, assuming that people are making the connection between particles and black holes, whatever, right?
You you you understand the direction you experience the thing, you measure the thing, the emergent complex pattern. And then we go back as scientists and we reverse engineer things and we take a look at the components. And then the story is that those components are somehow connected back to the thing that we just talked about, but we don't have that connection. So the more you say about the components, you're not really saying more about the phenomenon.
That's really what I'm trying to say here, the more you reverse engineer and pick apart the components still interesting, it is still an input, it does exist in nature. But no matter how much you say about those components, you cannot be saying anything about the output that you're actually measuring and experience in because of that great disconnect because of that complexity threshold that exists.
So we can go back and reverse engineer the zebra and say, oh look, it's got stripes and there's these little eddy currents that keep the school, the the the, the skin cool, sorry. And, and I can come up with a story about how, yeah, it's probably confusing to look at stripes and we reverse engineer and we pick apart and, and that's, and, and then we tend to think that therefore the directionality is right?
The reason, the reason that that the zebra does what it does is because right, it's the same thing as the academic narrative. The reason we have computers is because of computer science. No, no, no, no, no, no computer science. It was in no way required for computers to exist. OK? That, that's, that's an academic narrative that suggests that you need a foundation to produce the thing.
You don't need a foundation to produce the thing you need randomness, you need happenstance, you need tinkering, you need creativity. And then the thing that is interesting will get revealed and then you go produce the textbooks and you reverse engineer and you talk about the components that go into that thing. Are you understanding the difference is the same thing as the zebra and the Venus fly trap and modern day computers. It doesn't matter what it is, disease. It's the same thing.
Things are revealed to us that we're interested in either because we want to eliminate them or improve them, just get to know more about them, whatever it is. And then we kind of reverse engineer the inputs and we talk more about the components that go into the thing. We talk about the mechanisms behind the stripes. We talk about the mechanisms behind the Venus fly trap mouth or whatever it is. Closing, clo uh closing. We talk about the individual genetics that went into the disease.
We talk about the individual particles and their symmetries and their and their forces that presumably make up the black hole influence that we're looking at in the Galaxies, right? We see the thing it's revealed to us, we're interested and then we go talk about the components, but talking about the components is wholeheartedly disconnected from the thing we're experiencing.
At least in terms of what we can access information because we can't get to the path between how those inputs become the output. So the more you're saying about the components is not really accumulating knowledge because you're not really seeing anything. As long as that is fundamentally disconnected from how the thing arises, there is a fundamental disconnect between the two. So do you see how fundamental this is?
This is a problem, the current paradigm of science, this narrative that we are increasing the body of knowledge that we're increasing our understanding of the universe. What do you mean by understanding? Is it really understanding to look at the components of something? If those components are disconnected from what is actually experienced and and revealed and measured in everyday life, what exactly are you explaining more of something?
But because of its disconnect it, it's it's almost like a moot point. It it doesn't have any weight to it. It might still be interesting. I think it's still interesting to know what particles go into something. You know, we can, we can surmise like why might nature do that? What kind of symmetries might exist there? You know what kind of properties seem to hold at the low level of something? What are the fundamentals quantum mechanics? Right. Relativity, right.
Genetics, informational units that nature is somehow using as a supposed blueprint to create its life that is all interesting and of course real. But the assumption that those blueprint type components map to the output in a way that will ever be able to describe is untrue untrue in the sense that we'll ever have access to it. It might, again, presumably the inputs do become the outputs, but we'll never have access to the path.
So the more that you say about the inputs is circular, you're just saying, you're only ever going to say more about the inputs. You're never going to connect it to what we actually experience and measure and what we experience and measure was the thing that got us excited in the first place. It's not really particles we're interested and we're interested in the black holes. We're interested in disease, curing disease, improving the quality of life. We're interested in the dispersion of light.
We're interested in the things that actually come about. We're not actually interested in the components. We're just reverse engineering because we think it explains the things we experience, but it doesn't, it just explains the components because of the great disconnect. So I hope that makes sense. OK. I hope that all made sense. So, so yeah, the narratives will exist and this is why it's confusing and this is why people don't really understand evolution.
And this is why a lot of people uh you know, unless they spend the time to appreciate complexity, the non trivialities of, of the universe, of the world that you're getting a lot of that story wrong, you're getting that directionality wrong. And I think this is problematic because it's a wrong. It's there's something fundamentally wrong about the current paradigm of science, you know, and, and again that the consequences of that are things like the replication crisis.
Uh you know, the, the fact that a large number of, of studies just cannot be replicated, obviously, the worse, the more um complex the field is. But even on the simpler fields like physics, again, simple in terms of, right, they're much more component based as opposed to how things aggregate. Um Well, actually let's talk about that.
Now, let's, let's talk about, you know, two kind of failure is a strong word, but I'm just gonna use it to make the point failures in the last 30 years of science, you could argue is particle physics and genetics. Now, that's probably gonna surprise a lot of people because that doesn't, I mean, we hear about these things all the time. We've got large Hadron Colliders, we've got, I mean, genetics is just constantly being spoken about.
But let, let, let's, I'm gonna start with uh well, let's start with the large Hadron Collider in particle physics just really quickly, you know, uh it's been written about before the last 30 years of physics. Theoretical specifically has been largely a failure. It hasn't really produced much. Now, that's maybe OK, I mean, who's to say there aren't dips in progress, right? Who's to say that everything is supposed to, uh you know, every year there has to be some great discovery.
I don't think that's the case. Any, any kind of creative endeavor is going to have dry spells. But the last 30 years, that's quite a run. And, you know, a lot of that is around large Hadron Colliders, these particle accelerators that smash fundamental particles together to try to understand, you know, the, the the fundamentals again, those components right of nature. Uh It hasn't shown some of these super symmetries that they thought were, were going to be present.
Uh you know, they did discover the Higgs boson, which is presumably a good discovery, but not a lot has come out of this multi, multi multibillion dollar tool that was put together right now. Maybe that'll still change. But I argue it won't for the reasons I'm talking about in this episode, it's stuck in the wrong paradigm. Maybe there's another way to use the LHC, but the way it's being used right now is stuck in the wrong paradigm.
It's, it's running under this assumption that the more you find out about the particles that's gonna somehow lead to the things that we experience. And that's just not the case. It's never been the case. Genetics, the Human Genome project. Perfect example. Uh you can go look online, you can study it. This has not been a success. This has not been a success.
OK. Um You know, back in the day, uh Obama gave a big speech, the human genome project would be able to, you know, if you don't know, they're basically mapping all the genes of of the human genome, right?
So this is because DNA and genes are supposedly making up the, you know, the blueprint of the human body, right, the blueprint of health thing, you know how proteins come together and, and, and and and how ultimately the the you know the properties of life come to exist in the human body is is because of this, you know, the deoxyribonucleic acid, the DNA, the genes, the informational blueprints that exist in the body.
So the more you know about those informational blueprints, presumably you have, the more you know about the human body, right? And of course, that could maybe lead to the curing of diseases and more control over things that would uh you know, hopefully improve the quality of life. But that has not been the case.
And this should not be surprising anybody because of the great disconnect, saying more about genes, which is what the human genome project is, is not saying more about disease because the properties of disease are not going to be reflected in the properties of genes. Sometimes Hutchinson's disease might be one example where there seems to be more of a direct mapping. But by and large, you're not going to have that mapping between input and output.
There is this great disconnect, it is fundamental. And even in even in the case where you think you have a, you know, I can turn on and off this gene and then we see this outcome. OK. So let me I'll get to that in a second where there's studies and experiments that supposedly show we can do these things, right? But, but before we get into that again, the particle physics example, it it it's been failing. OK. The, the genetics example has been failing.
Now you talk to a geneticist, they'll say no, no, no. Like even the human genome project, we're coming up with new tools, new research methods. Yeah, I know you are. But it's circular. Those new tools and research methods are still operating under the same paradigm that you think. Saying more about genes is saying more about the thing. But it's not saying more about the thing. It's just saying more about genes.
Your tools are just saying more about genes, your, your, your methods and your approaches, the new things that you come up with aren't, they aren't saying new things about how to actually cure disease even though the narrative probably exists, right? But it's not actually curing more disease. It's not actually making any uh net positive result is it, you don't have evidence of that. You don't, we know this.
OK. So it's circular particle physics can keep saying things about, they can keep smashing things together. They'll always say more and more about the particles. But if the fundamental assumption that the inputs are connected to the outputs is not there, then what are you saying more about? So that that's the circularity, it, it, it, it, it progressing a field. You have to understand what that means. We've made lots of progress in genetics.
Yeah. Well, maybe you shouldn't because, because the current paradigm of genetics, quite frankly is extremely problematic. We're making more progress in particle physics. Yeah, depending on how you define progress. OK. And that's what I was alluding to about a minute ago.
I wanna talk about this definition of progress, this definition of truth because we tend to think that you know, whatever field of science you're doing and growing tools and growing techniques and you're saying more and more you, you tend to assume that you're, you're kind of proving things or that you're saying true things uh or that the definition of truth is just kind of agreed upon.
In other words, if science says it, it found something, we, we all know what found something means, but we don't really understand that because, well, well, OK. So let's use an example here, right? So let's take something like neuroscience. OK. Um Which kind of takes this very physics envy approach, right? We're gonna try to understand what's going on in the brain and we're gonna have these kind of, you know, functional MRI images and we're going to highlight different pieces.
And I I'm not saying all neuroscience is like this, but there's this kind of physics envy approach to it for sure. Um That, that takes a very components based approach that here's are the regions of the brain, right? These maybe neurons can be turned on and off and then that lead, you know, so it's the same thing. It's just a particle based reverse engineering approach to supposedly commenting on.
In this case, the most complex phenomenon we know of which is the human brain, which is sorry, downright ridiculous. It's ridiculous if you talk about the complexity threshold, this is just the worst example, any part of neuroscience that suggests that the isolated regions of the brain are producing.
The thing is, is a perfect example of being stuck in the absolute wrong paradigm of science, particularly because they're, they're, you know, the more complex phenomena that you're measuring, it becomes more and more problematic that you would be taking this approach. But if you ask someone from neuroscience, now again, I'm not saying all of neuroscience is like some bad field, but it's definitely like many of the other fields. Technically, all fields they're stuck in this wrong paradigm.
So they're seeing a lot of things that are true in one sense, but again, are operating under this disconnected problematic narrative. So let's say, look, we've shown that you can uh highlight different regions of the brain and maybe it was a functional MRI and maybe it was when someone was going through some kind of emotional response to something and these parts highlighted and they keep highlighting again and again. OK. Yeah, I agree. That's probably true. So there you go. So there you go.
What? Well, we, we've made this connection right between local regions of the brain and then what somebody is doing. That's definitely what it sounds like, doesn't it? But how did you get to that? How did you get to tell that story? And this is not, I'm not picking on neuroscience here. OK. This is this, everybody is stuck in this scientific paradigm. The only way to really tell that story is to create an extremely contrived artificial, not realistic scenario.
Now, why does science get away with that? Let's go back to what I said in the beginning, the way science operates is by extraction isolation, creating sterile, very unrealistic situations because that's what you do. When you reverse engineer something, you're trying to look at the individual components, you're trying to look at the localized region of something happening. The only way to do that is to is to is to remove the overwhelming amount of complexity that exists in the system.
Because if it's complex, it has opacity and if it has opacity, then you can't see what's going on. So in order to see what's going on, you got to remove the complexity, but that's your problem. That's your problem. Complexity is the thing that we experience complexity is what leads to the emergent patterns. Complexity is the science. It is nature. As soon as you remove it, you, you strip away the complexity threshold that actually leads to the emergent things we experience.
You're now studying something in isolation that doesn't map to the outputs. OK. So science, in order to call something successful has to create extremely artificial situations, someone stuck in an MRI machine that supposedly is feeling something is not a realistic situation. OK? And, and, and to know that the, that the feeling or the emotion that someone was experiencing, you know, to, to, to, to reproduce that, I mean, what does that even mean? Right. You're gonna, what, what are you doing?
Prodding someone telling somebody something then using statistics to try to see bubble. Yeah. Statistics is being grossly abused right now to try to showcase reproducibility and it ends up not being reproducible which again should surprise nobody. OK? I'm not trying to pick on neuroscience. We're all stuck in this paradigm. We have to strip away reverse engineer, we have to strip away complexity. But complexity is what it's all about.
Complexity is what leads to the thing not in the way we're ever going to know in terms of a path, but it's what produces the properties. OK. So the severe circularity that is fundamental to the current paradigm of science is, is just grossly problematic. OK. Progress growing bodies of knowledge. It's, it's all, it's all very, very circular because of that great disconnect that exists.
And you have to create in order to, to call something true in order to call it a success you have to create these extremely artificial environments that quite frankly don't really reflect nature. And, and we get away with that because we, that's, that's the scientific approach that is very much the scientific method under the current paradigm is we have to isolate, we have to extract, we have to make it sterile.
We have to actually make it quite unrealistic because we're trying to get at the components right, freeze all of this uh isolate all of that, you know, uh remove them from the environment in order to study this, that is in every field of science, nobody is immune to that. We that's what we do to reverse engineer. But because of that great disconnect to call that a success is only successful within its own echo chamber, right?
It's like it's like the same thing as the concept of an echo chamber, right?
You create an isolated environment where everything looks good to you because you can't get bad feedback on it because you created such an artificial environment disconnected from the rest of the complexity of real world life that things are being told to you that, that you know that, that, that that the way you set that up nature will say, yeah, that's great, but it's not really nature saying that it's your experiment saying that there's a difference between nature, saying something is working and an experiment saying something is working and the more contrived your experiment, the less you can really say that is true.
But that is the circular paradigm that we're stuck in in science right now, very artificial contrived situations, very much disconnected from what we experience and measure in the real world life, which is is complex, non-trivial and emergent. And we keep growing tools, growing techniques and growing bodies of knowledge that are disconnected. So in other words, a growing body of knowledge that is disconnected to what actually matters.
The thing we actually experience is not really a growing body of knowledge. It's really just a growing echo chamber, right? It's just a it's just a growing ball of circularity that pats itself on the back but isn't actually leading to progress. At least not the way progress is being described by science.
It does lead to progress in the sense that you're still interacting with nature and you're taking actions and that regular ongoing activity is going to just by default as a byproduct lead to progress. But again, remember the directionality tinkering and messing around will lead to progress. And if science makes you do that, that's great. But it's the narrative that you are explaining more with that activity that the growing body of knowledge is getting better.
That type of progress iau is not really occurring under the current paradigm of science because of the great disconnect. So what do we do? OK, I just want to reiterate, I don't think science is bad. I'm a big fan of science, I think it's done a lot of great things. Uh but not the way that the story is normally told.
And I think that as we continue to enter into, into more, you know, information driven and complex economies that this problem of circularity is becoming more and more problematic, right?
The industrial revolution, we could benefit a lot of that from, from that kind of simplistic component based uh thinking because that's very much what the utility was of the day we made steam engines and, and rockets, rockets and, and uh you know, trains and whatever things that deterministically simplistically added up. And so the component based knowledge was at, at least, even if it was still disconnected from the things that really, really mattered, at least they had a good to it.
We could probably start to make some improvements to, you know, train tracks and rocket engines. We're not in that economy anymore, right? We're not in that world anymore. So much of that physical deterministic stuff has been figured out and either it's going to be flushed away and not used or the improvements are going to be extremely incremental, right? And, and, and, and improvements are going to be more complex related. But that's, that's a topic for another episode.
But the point is is that the, the current paradigm of science cannot continue the way it is. We can't keep saying things about things that are disconnected, we can't just keep talking more about genes and talking more about particles and talking more about intermolecular interactions as though they are leading to the things we experience. They, they, they and and again, I wanna be clear, presumably they do somehow lead to the thing we experience, right?
It's not magic in between the input and the output. But we never have access to that story. The true story because of that complexity threshold that exists between that which is simple and component based and that which is complex and emergent. So the current paradigm, science is deeply flawed is deeply, deeply circular.
And I think this is what leads to so many of the problems that we see in science today, replication crisis and the P hacking the the gross misuse of statistics and and and if that was just confined to science, that would be bad enough. But as I said in the beginning, science leaks its way into public policy. It does affect the quality of lives.
It does have something to say about whether or not we can actually improve the quality of life, you know, whether that's fighting poverty or fighting disease. So we should be doing that the best way possible. A simple way of kind of thinking about this simple is properties over reasons. And I think I've said this in a few episodes before I'm a big fan of this. You know what, what I what I call properties over reasons.
You can understand the properties of things that are very nontrivial and complex. I mean, that's what I talk about all the time, but the reasons is something you're not gonna have access to. You're not going to know the reason. At least not scientifically.
Now, maybe you're a spiritual person, maybe you're religious, maybe that's another path, but you are not going to know the reasons for something because there is nothing in the scientific arsenal that can give you that there never has been, there has never been progress in the gap. We've had all the inputs, we've had all kinds of the outputs. There has not been progress at the gap between the two. It is nothing that has ever even budged an inch.
So the current paradigm is utterly incapable of, of giving you the reasons behind what is something that the truly emergent, non trivial things that we experience, which is gonna be at least 95% of everything we experience. OK. So science in nature, we can understand the properties which is great, which is wonderful. I think that's the way we should approach things, but we're not gonna understand those reasons that connect.
And so what I'm saying is that the next paradigm of science, we know it's on the right track if it has a lot less to say than the current paradigm. Remember I talked about a few episodes back how things that operate under complexity. Uh It's actually simpler to do which sounds kind of ironic but, and, and that's because there's this dramatic simplify, dramatic simplification of decision making under complexity, right?
We use heuristics, we uh you know, general rules of thumb approximations to, to make complex problems tractable, right?
And the reason is because of that causal opacity, the reason is because we cannot get access to information that would allow you to tally things up, that would allow you to, to, to, to weigh the pros and cons that would allow you to design some type of approach that takes all the, you know, the pieces and how they come together and see that path and then try to engineer something as though it were a steam engine or something. We don't have access to that information that we never will.
So decision making under complexity ends up being a lot simpler as long as you know how to accept the complexity, as long as you know, how to accept the epidemic uncertainty. OK. So that's why the next paradigm of science will have to actually have a lot less to say it will be looking at the properties, the high level properties. But it will admit that it does not have access to the path in between inputs and outputs.
It won't be a science of reverse engineering and saying more about particles and saying more about components because we will have to accept the great disconnect between what goes into a system and what we experience at the output of a system. The next paradigm of science, if it's done, right, assuming it's paradigmatic or whatever process you, you think science evolves, the next paradigm will have to have a lot less to say than the current one. And that would be a very good thing.
All right, that's all I got for this episode. I'm Sean mcclure. This is nontrivial. Thank you so much for listening. If you like what you heard, please consider giving nontrivial a five star rating on your platform of choice. Stick around because we got a lot more episodes coming up. Lots of good stuff. I hope you stay interested. Thanks again. Until next time. Take care.