Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas - 108 | Carl Bergstrom on Information, Disinformation, and Bullshit

Episode Date: August 3, 2020

We are living, in case you haven't noticed, in a world full of bullshit. It's hard to say whether the amount is truly increasing, but it seems that everywhere you look someone is trying to convince you of something, regardless of whether that something is actually true. Where is this bullshit coming from, how is it disseminated, and what can we do about it? Carl Bergstrom studies information in the context of biology, which has led him to investigate the flow of information and disinformation in social networks, especially the use of data in misleading ways. In the time of Covid-19 he has become on of the best Twitter feeds for reliable information, and we discuss how the pandemic has been a bounteous new source of bullshit. Support Mindscape on Patreon. Carl Bergstrom received his Ph.D. in biology from Stanford University. He is currently a professor of biology at the University of Washington. In addition to his work on information and biology, he has worked on scientific practice and communication, proposing the eigenfactor method of ranking scientific journals. His new book (with Jevin West) is Calling Bullshit: The Art of Skepticism in a Data-Driven World, which grew out of a course taught at the University of Wisconsin. Web site University of Washington web page Google Scholar publications Wikipedia Twitter Amazon author page Calling Bullshit website

Hello everyone and welcome to the Mindscape Podcast. I'm your host, Sean Carroll. In fact, welcome to a special bullshit edition of the Mindscape podcast. Now, I know what you're thinking. There's probably plenty of other episodes of Mindscape, which qualify as bullshit. But this is not supposed to be a description of the episode as a description of what the episode is about. I apologize for anyone who finds the language a little bit colorful or spicy, but bullshit has become a technical term in philosophy ever since.

the publication in 2005 of On Bullshit by Harry Frankfurt. The idea was supposed to be that bullshit is different than lying. In lying, you know that there's some truth and you're telling the opposite of it. Whereas in bullshit, it's not that you're trying to tell untruth. It's just that you don't care what the truth is. You have something that you want people to believe, and that's what you're going to try to make them do. So today's guest, Karl Bergstrom, is a biologist at the University of Washington, who studies the role of information in biology.

Not especially information in some highbrow information theory sense, although there is that, but how organisms use information, how organisms share information with each other and sometimes information that is not true. Basically, even crustaceans and birds can bullshit each other in the correct circumstances. So this naturally led him to study the flow of information in social networks among human beings,

and now it's made him an expert in the COVID-19 era of the amount of bullshit that we're hearing over social media and even over respectable news organizations about what this pandemic is doing to us. He's perfectly situated to talk about this because Carl and his colleague Jevin West have been teaching a course that has now turned in to a published book called Calling Bullshit, the Art of Skepticism in a Data-Driven World. It's especially about how we can use charts and graphs and numbers and data, to make a case that is basically bullshit. One of the features of the modern internet, highly interconnected world, is that bullshit can both exist in new ways and travel much more frequently and much more effectively. So we need to understand both sides of the equations,

both to how to recognize bullshit when it's out there in the world, and also how to prevent ourselves from promulgating bullshit, even if that's not what we mean to do. So this is a fun episode. you will even see us do a little calculation in real time and we'll get to both big picture issues about the nature of truth but also directly relevant issues to the big crisis that we're all facing right now.

So remember, for those of you who don't already know, we have a Patreon for the Mindscape Podcast. You can find a link to it at the website, preposterousuniverse.com slash podcast or just go right to patreon.com slash Sean M. Carroll and you can find it there. We have enormous gratitude to all the patients, Patreon supporters, my endless thanks to you folks out there.

You help support the podcast, keep it going, keep it vibrant, and Patreon supporters get ad-free episodes, as well as the ability to ask questions for the monthly Ask Me Anything episodes. So with that, let's go. Carl Bergstrom, welcome to the Mindscape Podcast. Thanks a lot, Sean. So you have a book that has come out called Calling Bullshit. That's the title. Is that right?

That's right. Calling Bullshit, the art of skepticism in a data-driven world. Yeah, we're going to have to put one of the caveats on this episode saying that occasional bad language will be used if bullshit counts as bad language, I suppose. But just so everyone knows, I mean, it's an interesting journey. You're a biologist by training. You've studied how information comes into biology and the role it plays. So I can kind of see how that segues into bullshit. But why don't you fill us in on how exactly that happened?

Yeah, it has been kind of an interesting journey. I started out trying to understand animal communication and wrote about animal communication a great deal as a PhD student thinking about in particular what keeps it honest and why do animals tell each other the truth? Because if they lied all the time then they wouldn't listen to each other but they do listen to each other. So what is it that keeps communication honest?

And they're really elegant set of mathematical models that get at these kinds of questions and they're paralleled by really neat models in economic game theory. And so that was what I was really interested in as a graduate student. From there, I moved on and started studying epidemiology, actually. And did a postdoc in epidemiology, started trying to understand how new, this is kind of unfortunately prescient, but how new diseases emerge from animal hosts

into the human population. And what happens then? And what does that spread look like and what can we do about it? And at the time, I was really thinking about things like H5N1. avian flu and such. And as I started studying more and more about epidemiology, I was thinking a lot about how things spread on networks, how do diseases in particular spread through human contact networks because if you just sort of treated disease as this mass action situation where all of the individuals are just bumping into each other at random, you don't get a very good picture of what

disease spread actually looks like. And so I sort of took this detour and spent five years thinking about sort of the physics of networks and working on problems in network theory. And as I started to emerge from that, we had all of the kind of current cultural issues going on about spread of misinformation and disinformation on social networks. And so that brought together, you know, some of these things I'd been interested in for a very long time. And it sort of kept working on it on a sort of back burner way about animal communication and about honesty and dishonesty and the economics of information. And Net brought that together with really thinking, trying to think clearly about networks and how information travels or anything else travels along

networks. And with all the stress that was given on the role of social media with the spread of fake news and such around the 2016 elections, this became something I was really very interested in and started trying to work in that area. But it must be a slightly weird situation because you've written a book with your co-author, Jevin West, based on a course that you've been teaching at the U-W on Calling Bullshit. And presumably, if I know how publishing works, in between when you handed in the manuscript and when it came out, we got hit by a massive pandemic.

And you were sort of positioned to talk about this in a useful way. And now you're at least a minor Twitter celebrity as the go-to place to have bullshit called on various takes about COVID-19. Well, certainly has been something that I've been trying to contribute during the COVID pandemic. Yeah, I mean, you're right. We did hand in the manuscript before the pandemic broke out. And it sort of feels like in some ways the book was written in a different world, in a different time and place. On the other hand, it's amazingly relevant to everything that's going on right. now and you know i think you could rewrite the entire book uh swapping out every single example in

every chapter with something that's happening around covid um because it's it's just uh these these are the sorts of uh problems that we're dealing with whether it's the misuse of information of sort of numbers and and statistics to try to you know uh basically push people around and intimidate people with oh i got all these mathematical models and my models show this or my models show that or here are the figures and you know figures don't lie and which of course the they do, or whether it's, you know, sort of the dynamics of the way that social media leaves us vulnerable to disinformation being injected by foreign actors. I mean, all of these things have turned out to be major players during the current

pandemic. And so, yeah, it has been interesting to, you know, write this book and then find sort of immediate application for it, if you will. I mean, there's no shortage of applications, of course, but yes, this is a particularly sharp example of some of these things. I mean, but so let's, you know, let's be scholarly about this. What do you mean by the word bullshit? Because I'm sure that some people have slightly different ideas in mind. Yeah, so when I talk about bullshit, I'm really talking about the use of language or figures or, you know, statistical arguments that are intended to impress or,

persuade or intimidate a viewer or reader with blatant disregard for the actual truth of these arguments that are being made. So Harry Frankfurt, the philosopher who wrote the book on bullshit, which was this sort of originally a scholarly essay in an obscure journal and then became a surprise, best-selling little book through Princeton University Press. He defines bullshit as, as you know he distinguishes it from lying by saying you know the liar knows what she wants you to believe and tries to lead you there away from the truth and the bullshitter doesn't even care what you what about what the truth is the bullshitter is just simply trying to you know uh bullshit i mean trying to you often try to impress you or persuade you or something like this without even

necessarily having a target in mind That's why I think of an example. I think of like, you know, I think we all had this experience in high school of having to write an essay about, you know, something in one of our, you know, humanities or social sciences classes. We didn't really understand the reading that we had done. And we'd left it until too long. And we wanted to write something that would make it look like we'd understood it. We didn't really give a damn what the teacher believed about whatever we were writing about. But we did want to try to make ourselves look competent.

And that's kind of the epitome of bullshit. in Harry Frankfurt's sense. So it's less, I mean, maybe tell me this is accurate or not. It's less about lying per se and more or less just not caring if what you say is the truth or not. You have some instrumental goal in mind you're going to try to get there by saying whatever it takes to get that reaction in your audience. Yeah, I think that's exactly right.

And very often that goal, I think, has something to do with, you know, trying to impress someone or persuade them of your own competence or your own likeability or whatever that is. And so is this something that hooks into the biology research that you mentioned? I mean, are there evolutionary reasons why we bullshit? Because clearly it's prevalent. It's all over the place. Yeah, that's right. I mean, we talk about that in the leading chapter of the book.

And we look at sort of where, you know, where did these things have their origins? and so it may be more along the lines of true straight-up deception, more along the lines of lying than along the lines of, you know, Harry Frankfurt's type of bullshit. But, you know, bullshit or at least deception goes way, way back in the animal kingdom. And we have some, we talk about some examples. They talk about stomatopods, these crustaceans that have these very, very powerful claws, and they threaten each other and they fight with these powerful claws,

when they wave the claws around. But they do something when they're molting. And when they're molting, their claws are, you know, just basically a, you know, a shelled lobster claw. They're defenseless and useless. But they wave them around anyway and try to intimidate each other. And it's often successful. This is this kind of deception. And we talk about ravens and the way that ravens actually have a theory of mind and some of the experiments that show that ravens can, you know, actually think about what other birds see and what they might be.

interpreting and the way that they use that to try to fool other cons specifics. And so, of course, you know, deception of that sort goes way, way back because, you know, information can be, you know, manipulating other organisms access to information is a very good way to manipulate their behavior and whether that's done by, you know, manipulating that access to information through a communication channel that sort of evolved for the purpose of communication or whether it's done, you know, through something like camouflage, it can be a very effective way to, you know, communication channels in general give you handles on other people's behavior.

It's like, I tell you, how do I get you to do something? Sean, I don't grab your arm and move it. I talk to you and convince you that that's the thing that you want to do. And so this goes way, way back. And then as we move toward humans, you know, what we have that's sort of unique in humans, are I believe unique in humans as we have this sort of infinitely expansible combinatorial language that allows us to create an infinite range of meanings and so forth.

And so we can talk about and we can construct all of these kinds of arguments and narratives and entire cognitive edifices in the world that we can use to influence what people do and think and so on. And so we have all of that. And then we have a quite well-developed theory of mind about one another. so we can kind of think about in advance how the stories that I'm telling you are going to manipulate your own belief states and how that might lead you to respond. So when you put all of this apparatus together with the fact that, you know, human incentives are rarely perfectly aligned between any two people, that creates the sort of, you know, setting in which we'd have a bunch of lying and a bunch of bullshitting as well.

I guess I'm going to guess that there is some game theoretic reason, why when it comes to communication between individuals, you usually want to be telling the truth. Otherwise, people won't believe anything you say, right? There's some sort of optimum mixture of bullshit. You should stick in there to maximize the chance that you'll be believed and also get what you want. Yeah, that's spot on. And, I mean, we see things like that. I mean, you see something like this even in a poker game, right?

I mean, it's... That's what I'm thinking of. That's my general paradigm for these issues. Okay, yeah, exactly, right? So you bluff some, but you can't bluff always. and so otherwise people will call your bluffs all the time. And it's the same when we interact in person. And then the frequency of the interaction varies.

I mean, if I do a single appearance on your podcast, I may bullshit a lot more than if you're a close colleague in mine that I deal with on a daily basis, that sort of thing. I wonder if the incentive to put false ideas about the world in the mind of other individuals is pretty obvious to me. what about putting false ideas about the world into our own minds? I know that people have sort of debated how much we trick ourselves, but clearly we do in some sense. Either we trick ourselves, or at least we're not very good at always getting to the right picture of the world.

Yeah, of course, there's a ton of self-deception that goes on. And I think a lot of it, in my personal view, is actually due to, you know, relatively ineffective application of puristics that we have to various, ways of interpreting the information we have about the world. You know, there are certainly people that argue that actually you have sort of, you know, adaptive or optimal self-deception where, you know, you can actually perform better at certain problems or tasks if you believe things that are false and so on. That, to me, clashes with an awful lot of what I think about, you know, are basic principles and decision theory. And so I've not found those arguments.

It's particularly persuasive, but what is completely clear is that we're all very good at convincing ourselves of things that are wrong, and though I just don't think it's necessarily optimal that we're doing so. Free is great, but only if it's useful. Free credit scores from some apps can differ by as much as 100 points from your actual FICO score that 90% of top lenders use when you apply for a credit card, personal loan, car loan, or mortgage. That can mean a higher interest rate, a bigger monthly payment, or worse.

Denied. My FICO gives you your actual FICO score. The score lenders use straight from the company that created it. For the moments that matter, get the score that matters, your FICO score. Visit myfico.com and get started for free today. Well, I mean, that's interesting to me. Is it an open question or consider an open question? Like, is it optimal to be as correct as possible?

Well, if you're, you know, in decision theory, you would certainly, you know, you certainly would, you'll do, you know, for whatever your objective function is, you'll do better if you have correct information than if you have incorrect information, making, you know, say, Bayesian decisions based on, based on that information. And, you know, similarly, in non-decision theoretic context, not in a game theoretic context, but if you're just trying to make a decision that doesn't impact anybody else, more information is always at least as good as less information and often, and usually better. Things get a little bit more complicated when you move. into a game theoretic context, you can definitely have situations where getting more information

can hurt you. It's less obvious to me that getting false information helps you in those kinds of situations. But, you know, there are people that argue, especially that there are, you know, constraints about what we can do is say, I'm not, you know, humans can't bluff well. So the only way to be effective at bluffing is to believe the thing you're bluffing about, for example. would be one argument. So we've evolved the ability to self-deceive in order to be better bluffers. I don't understand why we didn't just evolve to be good bluffers and believe the truth. But that's it.

I mean, I guess what it, this is off topic, but it's just fascinating to me. I mean, is there some sort of group selection argument we could put down to say that, you know, for any individual, they would be best off getting the most accurate view of the world possible. But within a species or within a community, if some people are wrong, there's some benefit to Like they act in crazy ways that benefit the group because of their adventurousness or something. Well, as an evolutionary biologist, I tend to be pretty skeptical of group selection arguments without really, really hard quantitative backing because they just typically don't go through. You know, and the basic problem with the group selection arguments are what George Williams brought up a great evolutionary biologist and said, well, look, sure, that might be true that a group that has a mix of people, you know, ones that have crazy ideas and ones that don't. does better than a group that all has sensible ideas.

But within that group of people with crazy ideas and reasonable ideas, which ones do better? Well, the ones with reasonable ideas do better. And so pretty soon you lose the genes for having crazy ideas go away to massively oversimplify things and pretend that their genes for having crazy ideas. But you see what I'm trying to say about this. I do. I do. And yet so many people have incorrect ideas about the world.

so we have to work to understand this. Good. So back to bullshit then. Is there like a categorization? Is there a grand Aristotelian theory of kinds of bullshit? You know, there have been, it's remarkable. There's been quite a detailed exploration of the nature of bullshit in the philosophy literature since Frankfurt's original paper.

There is not a taxonomy that I'm completely happy with. There are some kind of fundamental debates that come out in that literature. one of the really important ones is is is is is is bullshit in the in the mind of the speaker or the mind of the beholder in other words does intent matter um and uh so they're you know there are arguments that go in both ways for harry frankfort intent very much matters um and for uh for some other authors you know it's like i mean it's a statement it's on paper it's just the text it's bullshit or it's not which is it you know um so these are the kinds of things people are carving up i don't know if there's sort of a grand taxonomy of bullshit in any other sense. I mean, I guess then the two strategic questions are, how do we avoid being bullshitted? And then maybe the slightly naughtier one is, how do we become better bullshitters when it would benefit us?

Yeah, I mean, both of these are interesting, valid questions. It's kind of been a challenge, too, as we've been thinking about it. So we've been teaching this course calling bullshit since 2017. And one of the things we've been kind of thinking about is, you know, how do we, you know, make the world a better place with this course instead of just simply training people to be better bullshit artists. So let's see, to get to the sort of first, the first question, how do we avoid being bullshit? I think we're actually quite good at that for what I'd call old school bullshit. And old school bullshit is the kind of weasel words that you'll hear from, you know, company spokespeople might be a sort of a political speech. It's sort of just taking rhetoric and words and maybe this notion of paltering that people use to kind of, you know, bend the truth or get around the truth enough to have, you know, plausible deniability or diffuse away the direct responsibility by using passive voice and all of this.

And I think we're pretty attuned to that. We're good at picking that kind of thing up. when people are bullshitting, we know it and, you know, we wince and people have a natural distaste for sort of weasel wordery and all of that. And what the book is really about is that there's this stuff that I call new school bullshit. And that comes clad in the trappings of science. And in particular in the trappings of numbers and statistics and kind of figures and, you know, machine learning algorithms and all of that. And I think that we are more susceptible to this because first of all, it's a relative. new thing. The world's so much more quantified than it was 20 years ago for reasons we could

talk about. Second of all, I don't think the education system is really doing as much to train us to be tuned to this kind of quantitative bullshit that's out there. And third, I think there's this feeling that we have that numbers are somehow, you know, more real or objective than words, words are opinions and they're kind of, they're fuzzy, and numbers are these hard things that come straight to from nature. And of course, you know, that's not true. And even when it is true, there's so much flexibility in the way that people present numbers and facts and figures that they can give you true numbers, but make you feel completely differently about them, depending on how they're presented. So that's really what we're

trying to do in the book is to say, look, you know, we're assuming that you know when the corporate spokesperson is just bullshitting their way out of taking responsibility for something. but it's a lot harder when somebody comes rolling in with a statistical analysis and waves it in your face, and you don't quite, you know, you've never learned that kind of analysis or you don't remember what it does if you have learned it. You know, then how can you challenge them? Yeah, I mean, I guess I want to get to the quantitative stuff because obviously that's, it's fascinating. But maybe I'm a little bit less optimistic than you are about the old school bullshit. I mean, for the examples that come to mind,

are just hugely grandiose claims from sketchy sources, which in my experience, people are really willing to buy. As a physicist, people on the street come out saying, oh, I've solved all of physics, and there's a remarkable number of people who are like, oh, yeah, okay, we should take that seriously. Or recently, just as we're recording this, the New York Times had a story about UFOs being captured by the Pentagon, and people are like, oh, we should definitely pay attention to this. It is really interesting. You know, I, at a just curiosity, I went to a heterodox science conference that was

held on the campus of the University of Washington, essentially because the university is a public institution and anybody cannot use our facilities. And I was, I was quite interested to see what, what, what, uh, was going on. Of course, every person there had either, you know, disproven general relativity or had, unified it with quantum theory. And so, I mean, it was very interesting.

They felt that there was this conspiracy by big science to sort of cover up the problems with modern astrophysics and quantum theory and so on, which was interesting in its own right. But the thing that was, I think the most interesting to me was that the rhetoric they used was very much the, many of the things that we've essentially tried to teach our students in classes to be skeptical, you know, to say, you don't want to just don't take this for granted, you know, ask, and somehow it was just this sort of misapplication of those principles, which are quite reasonable principles that had led people down the wrong path, you know, coupled with various delusions of grandeur and, and deep misunderstandings about how science works, that had led you to this place. So I see, I see what

you're saying, especially as a physicist, how you would feel vulnerable to this. You know, there's, there's, there's, there's, there's, there's nothing, you know, glorious about figuring out, uh, about, you know, reversing the, uh, the, you know, popular belief about the life cycle of the three-toed river salamander. And so, um, these guys don't come after that, but they sure come after, uh, you know, grand unified theories of physics and such. But I mean, can you, uh, for our audience members who might be, um, romantically tempted by these theories? Is there a, is it's, I know that it's difficult to make everything boil down to an algorithm,

but like are there rules of thumb when we see a scientific claim outside our own area of expertise? Like none of us has an area of expertise on all of science, right? But what are the warning signs that, okay, maybe this is a little bit less reliable than something else might be? I mean, you know, this is, this is, uh, this is, uh, this is, uh, this is, this is, uh, this is, this I mean, I'm going to give you an answer that people will immediately, you know, you know, people, contrarians will immediately say as an appeal to authority.

But, you know, my answer is if somebody makes a particularly, particularly someone makes a really shocking or extraordinary claim, you want to look at the venues in which that claim appears and the, and the credence that it's given by people who are well thought of in the field. And, you know, and I think the, so if some people, if, you know, if somebody actually was able to mathematically show that there were fundamental contradictions in general relativity and we should throw the whole thing out and replace it with their new framework, you know, we would expect this to appear in, you know, better be in physical

review letters and in science and better be in the newspapers and you'd have top physicists, you know, taking it very, very seriously. And to give you an example, I mean, you know, there are, some people say, oh, you know, there's a cover up. They'd never, they'd never do that. But of course, people do take these things very seriously. And when we had the whole cold fusion story, there were a number of really good groups that that investigated that in great detail, even though it seemed like a fairly implausible claim, because it's so damned important if it turns out to be right. And it wasn't, you know, it wasn't patently wrong on the surface, even though it did raise some major questions that no one, you know, had answers to at the time if it had been true. So, you know, I think,

you know, one thing to do is just basically look at the venues, look at who's supporting this, and, and think that, look, the world-changing ideas may not come from, you know, the most important, most prominent people, but they will be relatively quickly embraced by some reasonable fraction of serious scholars in that area. And here, I think it just helps enormously to try to explain to people a little bit more about how the scientific community works. It's not like we all, you know, go to a meeting and sit down and decide what we're going to tell the public. It's this very, Oh, my goodness, no. It's this very red and tooth and claw world, right, where everybody's trying to

scramble their way to the top and there's, you know, enormous prestige and so forth to be had by getting in early on something that's right and disproving something else. And, you know, so if somebody had really made this major discovery, you know, that's a, and you're one of the first people to pick that up. That's, that completely makes your career. And people would jump on that. So anyway, I think that's maybe might help with dealing, you know, thinking about some of these sort of contrary and scientific claims. No, actually, I think that's a wonderful point, probably the best advice you can give.

I mean, people talk about the establishment or whatever as if it's a monolithic thing. But the reality is there's a bunch of people who are competing with each other and they would love to find the new brilliant idea. So if some outsider scientist comes up with it, if it's at all plausible, someone's going to jump on that. I mean, I think it's a very good thing to point out. Yeah, absolutely. I mean, I think that's the, you know, and if no one does jump on it, then, then that tells you something, right? Let's tell you. Yeah, exactly. Actually, this heterodox conference was very interesting because the, one of the speakers there, one of the organizers actually said, you know, he was talking about what they needed to do to move forward. I mean, these are people that are passionately in love with science, by the way. These are not, you know, denialists or anything like that. I mean, they adore science and they want to be, they want to be part of it. but they're talking about what they need to do. And the organizer said, well, one of the problems is when we have our conference, everybody's selling and nobody's buying.

Everyone comes in and they've got their own personal theory. And it's almost as if everyone knows that no one else's theory is worth taking seriously. And that's so different from what science is, right? Because when we go to a meeting, you know, sure, we're selling, but we are buying. I mean, there just be no point to go to, you know, a major society meeting. if you weren't there to buy, so to speak. Oh, yeah, yeah, yeah. No, I mean, I dream about, you know,

small meetings with wonderful people at which there are no talks, and everyone just sort of talks to each other and hears what each other has to say. Pretty much the only ones I go to anymore. Well, I guess I used to go to. Now I don't go to anything. I just log on to snow in the morning.

So has, so you indicate the idea that the quantitative era that we're in now has sort of changed the nature of bullshit. I imagine that's both in the kinds of bullshit that we can get but also in how we can spread it. I mean, what are the new kinds of

bullshit that have arrived since we've started to quantify the universe, so precisely? Yeah, so I think I think there's just been a fundamental change in the last 20 years about how data and numerical arguments are

introduced into the public discourse. So, you know, if you look at at newspaper articles from the 1980s say, you know, when I was in high school, you would not see a whole lot of data visualization. You wouldn't see, you wouldn't see, you know, just a whole lot of discussion of statistical arguments. If you did, you'd just, it would just say, you know, oh, the Fed did statistics and here's the qualitative, you know, outcome or something like this. But you wouldn't go into these kinds of details. And you wouldn't have, there's no such thing

as data journalism. And, And we just simply didn't use numbers for persuasive purposes the way we do now. And I think so much has changed in the last 20 years. If we think about the availability of data compared to what was there even in the year 2000, I mean, first of all, we're all tracking ourselves physically in space with the cell phones we have. Then we're, you know, through the sort of data exhaust we produced, you know, with companies. You know, Google knows what we want to know. And Amazon knows what we want to read.

read and, you know, Uber knows where we want to go and Tinder knows who we want to go there with and all of this, right? So all of this information is out there. We're creating, you know, just unbelievable data sets. Then the sort of A, B, testing that any of these companies can do in real time on their user interfaces means that every single one of these companies knows more about psychology than the, you know, the collective discoveries of the entire site community,

at least about these, you know, very specific things about what color of buttons do people click on and what kind of wording works well in headlines. And in fact, not only do they know that, but they know that, like tailored to each of us individually because of, so there's all of that data, and then there's all of the environmental sensing that's out there. You know, and then we have the Internet of Things,

And so, you know, my refrigerator is telling Google a tremendous amount about, you know, how often I go to the grocery store because once a week I leave it open for 20 minutes when I loaded in. And my car down. So just all of this data that we live in is a fundamentally new thing. And we're taking that on, I think, in the way that we that we make persuasive arguments about how the world works and about what we should do in the world. And so when you look at, you know, even on a news broadcast, you see a. a ton of data visualization. The New York Times has a large and extremely talented data visualization team a couple of dozen people that does these amazing things.

And I see all of that as really being a massive change in the last 20 years. I feel like our education system hasn't really adequately caught up to that. And is the biggest problem that people misuse it or people don't know how to use it when it when it comes to making persuasive cases on the basis of some data sense? I think that there's some of each. You know, I think there's, I mean, you know, there are, you know, there are most, I think, you know, most of the people that, that are putting material out there in, say, traditional media are, you know, good actors at heart.

However, we typically do have perspectives and we try to convey those perspectives as powerfully as we can. And I think that's completely fine, by the way. I mean, this is the same thing in science, right? It's like, you know, part of why science actually works well is, as we've talked about, it's a somewhat adversarial system. It's, you know, I would love to prove that you were wrong. Oh, yeah. And so that sets up these nice arguments where we each try to make our case as well as possible,

and then the universe can adjudicate between us. And so that's very. So we do the, anyways, we do the same thing in the media. And even if we don't necessarily know, you know, all of the black arts of, of manipulating data and the stories that come out and telling misleading stories out of data. We certainly kind of, you know, get this, get an inclination as we're playing around with ways to display things. Oh, well, you know, it sort of seems more persuasive. You know, I want these numbers to look really big. So I'm not going to write them down as percentages. I'm going to write them

down as absolute numbers because there's 330 million people in the U.S. if I do that, you know, the numbers get big, you know. So you see these sorts of, you know, arguments where someone says, you know, you know, a thousand, a thousand, a thousand, uh, deco recipients have been accused of crimes against Americans, right? And it sounds like this terrible number. And, and, oh, my gosh, a thousand crimes. And then, uh, you know, what you're not, you know, what you, what you're not pointing out is that that rate of, uh, of being accused of crimes is, you know, more than an order of magnitude lower than American citizens, for example. And, uh, you know, as we talk about those kinds of examples in the book. Um, and, and we see this, you know, uh, uh,

Some of this may be malicious and deliberate, and a lot of it, I think, is just people that are trying to make their case as well as possible and kind of, you know, doing that however, you know, kind of stumbling into some of these tricks, if you will. From the writers of parenthood and life as we know it comes, it's not like that. A new family drama about starting over and second chances. Scott Foley stars as Malcolm, a recently. widowed pastor and dad of three. And Aaron Hayes is Lori, newly divorced with two teens. Their families used to do everything together. Now they're navigating single parenthood and maybe something more. Watch, it's not like that. All episodes streaming May 15th on Prime Video. Well, the example that you just mentioned, I think, I mean, that is an example of a more general

tendency, whatever you want to call it, to people to not take either rates or fractions or proportionalities into account. I mean, I read a, the thing that really bugged me is

I read an article about buffets and restaurants and, you know, how they're going away. And it would just say like, you know, oh, in this town, three people got sick at a buffet. But there was no comparison to how many people got sick

from other ways or at non-buffets or anything like that. I mean, is this an example of a general principle that we should be keeping in mind when we look at other people's data? Yeah, I mean, we essentially have a chapter on this in the book. And so some of the things that you described, you know, there are these, you know, people choosing whether to use percentages. You know, you can use percentages to, so if you have a really big number, but you have a really big denominator and you want to make that big number look small, you can report it as a percentage or vice versa. And you brought up another very important thing, which is that numbers need to be presented in a way that allow you to make useful comparisons.

And when numbers are presented in ways that don't allow you to make useful comparisons, that makes them almost by their very nature bullshit. Because if I just say, you know, hey, look, Sean, you know, 123 people, you know, had a heart of time. well listening to a podcast last year. I really need to stop doing this. You might think like, oh, shit, you know, I need to rethink my whole life. But you, but, uh, your whole life, but one little element of your life. But in any case, you know, but of course, the right thing to do would be to say, well, you know, how many hours are people, how many hours did Americans listen to podcasts and how many, you know,

how many heart attacks do people have an hour and so on? and cash that out because there's just you know if i just give you a number you can't you can't possibly make meaningful comparisons and and so i mean that actually we're that's i mean this is really something we're talking about a lot in the book is this this sort of use of sort of quantitative shock and awe where you just throw these numbers out there um and because you've got them it's really hard for somebody to come back and i you know if i make a claim like that by the way i have no idea whether it's you know i'm off by two orders of magnitude in either direction but uh but in any case, if I make a claim like that, it's really hard for you to respond right away because,

you know, unless you're super good at Fermi estimation, which you may be, you know, you're going to, you're going to struggle to be able to come back and say, you know, oh, yeah, well, here is the right denominator and when you do this division and here's the comparator. And so, you know, one of the things that we're really encouraging people to do is when they're, when they feel like they're getting pushed around by numbers like that, is to, you know, take a step back. You know, we talk a little bit about Fermi estimation and how you could sit. down and do that calculation and and figure out whether a number like that should be, you know, something that's frightening or whether that's entirely to be expected, et cetera, or just, or, you know,

alternatively just run a quick Google search and work something like that out that way. Maybe for the listeners, you can fill them in on what a Fermi calculation actually is. Oh, yeah. So Fermi calculations are, it's just this notion of doing a sort of, you know, back of the envelope, a quick estimation of the rough order of magnitude, sort of what power of 10 is a particular quantity, and named after Enrico Fermi, who was very, very successful, very skilled at this. And I find it a very, very useful technique whenever I'm confronted with these various numerical claims to just see whether, you know, is this even, in the ballpark of plausible.

Right. And then also, if it is in the ballpark of plausible, is it anything I should be surprised by? Yeah. You could sit down and you could say, well, people live for roughly 100 years. There's a certain number of people in the United States. They must be dying at a certain rate. Should I be surprised that people die while listening to podcasts? Exactly.

Yeah, yeah, that's right. And you try to do it. And, you know, what we encourage for Fermi estimation is so you don't get sort of hung up, you know, just to get it roughly right, you could, you could do it, you know, just use powers of 10. And so, so, I mean, we can even do it. We can even do that one live, and then you can cut it out if it doesn't work. But, you know, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, so, of these, of these podcast listeners.

You know, 10 hours a week. Do you think they listen 10 hours a week? Okay, so. Order of magnitude, yeah. Yeah, order magnitude 10 hours a week. Let's let's have them, let's have them listen. Let's see, yeah, okay.

So then they've got, you know, then you've got 500 hours of listening a year. And I shouldn't be trying to do this live. We could make it 100 hours a year if the numbers would be easier. Yeah, so you can see, so you listen to 100 hours a year. And, of course, off the top of my head, how many hours in a year? That's what it gets hard. Right, right, right, right, right, exactly.

Because we don't live in a sensible universe for years or a thousand days long. It's a very metric. This would all be so much damn easier, you know. So, yeah. Let's leave this in, but not completely. 10,000, okay, yes, you can cut this one out, but, you know, 10,000 hours in a year, they're listening for 100 hours. So, you know, there's a one in a hundred chance if somebody dies in a year that they're listening to a podcast while they do it. You know, and now, now you've got 300 million Americans, 330 million, 330 million Americans.

So we're going to have, you know, well, here, here, here, here, here your Americans live, you know, lives exactly a half a log between. between your powers of 10, which is always unfortunate in doing these calculations. But you know, so let's say you've got, let's say you've got three million Americans dying a year. One in a hundred of them is dying while listening to a podcast. If they're a podcast listener, it's only one in ten of those. So one in a thousand of the three million Americans that die a year, die listening to a podcast. So one in three thousand, uh, uh, uh,

So you have about 3,000 podcast deaths due to heart attack a year. Or 3,000 podcast deaths a year, not all due to heart attack. And then we can say, well, you know, one in 10 Americans dies to heart attack. And we get one in 300. And, you know, so it's the same order of magnitude as the 123 that I threw at you at the start. Yeah, I know. That's pretty good.

Yeah. I hope you'll cut that all out. But that's a, but you should feel free. Because I'm going to keep it in there. Because I think that this is. Absolutely keep it. It's fine. Yeah.

I mean, it's a wonderful, you know, I just did, I'm doing these videos. these days on some of the big ideas in science. And believe it or not, I got the periodic table dramatically wrong. I said the beryllium was the third element rather than lithium. And, you know, I got some comments saying like, oh, that was my favorite part because you made a mistake. I love it. Yeah, yeah, that's totally true.

Seeing how the sausage is made is very, very useful, I think. It is useful, actually. And it's kind of fun to do, right? I mean, it's just. Yeah, and it's a demonstration that it's doable. And it's also, you know, there is the fact that your IQ gets cut in half when you stand in front of a blackboard or get on a live podcast. This is certainly true. People should cut slack for that.

But, you know, so part of this is how we get the data and what we do within our individual brains. But I know that you're interested in the sort of ecosystem questions as well. And there are a lot of journalists or more broadly people sharing information who are not trained, who have not taken your course. Is there some realistic aspiration that we can just train up all the citizens in the country or in the world to think a little bit more carefully about statistics and rates and things like that? I think if we're going to keep using social media, I don't think we have an alternative. The thing about social media, right, is that, I mean, we had this, and we talked about this as well in the second chapter of the book about the way that sort of the way that information production and, distribution has changed, you know, not only sort of the volume of information in its rate, but also what kind of information is out there. And so there's been this, there's this, you know,

fundamental change, essentially for most people in the 1990s, where, where all of a sudden you go from having, you know, the, you know, basically you get, you get, you know, the internet plus digital type setting. And now everyone can create professional level content. And, you know, add the web on top of that, plus the acceptance of World Wide Web, you don't even have to know law tech. And then you add, you know, so then you're starting to get all this, you know, new content, and we were all very excited about this in the 1990s because it was going to bring all these voices to the table that maybe hadn't had the political and social and economic capital to be there. And to some degree, that promise has been realized. But what we also got, of course,

is this enormous glut of information. And we came to all these problems about how do we sort through it? And one of the solutions that we've come to, especially given the sort of acceleration, at which, you know, about the rate at which this information is produced is through social media where we all take on the role of becoming editors for one another. And so all of a sudden, I have this really important role in determining what my friends read or, you know, given the amount of time I'm spending on Twitter these days, a lot of people I've never met and what, you know, and what they read. But we're all doing this. And so we're all filtering information in this way. And we're all doing the things that professional editors used to do. And one of the big

problems is, you know, we, you know, not only don't we have the training, but we also don't have the same incentives that professional editors do in terms of, you know, protecting our reputation. And, you know, we may be trying to signal something about our group identity by sharing a meme instead of, like, you know, trying to signal something about the truth. And, but in any case, because we're using a information exchange system where we are all playing this role of editing. editors, people have to, I think, if we're going to have, you know, reasonable information hygiene out there in the world, people have to, you know, what we suggest in the class is they have to learn to think more, share less, right? And how to think carefully about what they are sharing

before they share it. And this sort of, as I see it, there's kind of three approaches you can take to this problem in the social media universe. I mean, you can try to throw technology at it. and, oh, we're going to have AI that can detect fake news. And while that might get you to deal with a VC, I don't think there's any chance you're going to be able to actually make that happen. Because to the power of adversarial AI and everything else, you know, even if someone were able to do it for the current, you know, as things stood now, it would be easy to overcome.

So on. And then you could try regulation and you could try doing things like many places have of sort of criminalizing misinformation and so on. And as a pretty strong advocate of very, broad interpretation of First Amendment rights, I don't like this solution. I would like to see some regulation

of the tech industry to make sure that we have some control of individual users of what we see. But other than that, I don't want to go down that road. And the third leg of the stool that I see there is education. And that's the only one that I think we can really stand on, is to help people understand better how to parse the media environment that we all live in now. And a big part of that is sort of media literacy and that kind of education.

But at the same time, as we move toward this increasing quantification of our world, I think this quantitative literacy becomes very important as well. But I guess what I worry about with that, I mean, I agree with everything you said, but the pessimist in me says, well, the biggest problem is not people who want to get the truth but don't have the quantitative education to do it, the people whose incentives are to do something other than get the truth, you know, to identify with their tribe or whatever. And media, social media, make that so much easier in some ways than it ever was before.

I don't have a rebuttal to that. I mean, we've slammed very, very hard into that during the COVID crisis. And that's something that actually we never really anticipated or saw coming. I spent the 20 aughts, you know, doing pandemic, planning work. And while there were a lot of, you know, debates, you know, in the scientific community and in the political setting about, you know, what role should the government versus the free market have in planning for rare, you know, health disasters and so on, we all assumed that if something like this ever broke out, everyone would be on the same page. And we'd just

try to get this thing done. And instead, you know, of course, we find ourselves in a world where the very existence of the virus is a politicized issue, whether masks work politicized, does hydroxychloroquine work politicized? And this is really bonkers, because if you sort of think about it, I mean, there are a lot of things where if I said, oh, hey, you know, Sean, there's going to be this crisis in two years. Here's what the crisis is going to be. Some people are going to want to do this response. Are they in the left or the right? You'll say, oh, military intervention, they're on this side, right? If I said, you know, hey, Sean, there's going to be a pandemic and some people are going to want hydroxychloroquine, you've got no way to tell me whether that's going to fall on the left or right, right?

This is just no reason to politicize that. It doesn't make any sense, but it is this tribal allegiance that you're talking about. And it is facilitated by social media. And it's been an enormous problem in our national response to COVID because you get things like, you know, people refusing to wear masks simply to signal their tribal affiliations. Yeah, I mean, it seems as if I've never really. thought about it in these terms, but it seems as if there's some stew of interactions that spiral you down between number one, the ability to bullshit and quantitatively

in this data-driven world we're in. Number two, the ability to spread the bullshit through the social media, and number three, the partisan or identity affiliations that make your allegiance to a certain statement stronger than your allegiance to the truth. And somehow we got to change the incentive so that somehow people are punished for giving into that. I think that's, you know, I think that is the sort of thing that that is happening. You know, my colleague Joe Buck Coleman and I have sort of, you know, not completely jokingly said that the answer to the Fermi paradox, you know, the fact there are no, you know, the fact that we don't see evidence of intelligent life anywhere out there is not that people invent nuclear weapons.

It's they invent social media. And it launches you exactly into that, not people, aliens, but it launches you exactly into that spiral that you're that you're sort of describing. And that could be an existential threat. We have a somewhat more serious paper that we're working on about what we call human collective decision making as a crisis discipline. You know, the idea is, again, this same sort of thing. It's like once you network people and you have all these tribal affiliations, you know, what happens, what happens to the way that information flows, what happens to the ability to have an informed electorate? and these become things that, you know, we were working on this before COVID and thinking this was going to be a real problem. And, you know, it's one of those papers where now everyone doesn't seem nearly as prescient when you publish it after it already happened.

Well, we shouldn't put too much blame on Facebook and Twitter at all. I mean, you also mentioned things like TED Talks or self-help books or there's pre-existing set of ways that we bullshit ourselves and each other. that's for sure you know there's you know it's it's by no means the you know sole domain of of social media i think that's you know i keep coming back to this partly because of this interest i talked to you about at the start about the way that information spreads through networks and i find that so fascinating it's really you know the the patterns of information flow are so fundamentally different from these highly centralized patterns where you know you have the the ted foundation or whatever it is that you know handpicking

some, you know, people to groom into media stars and then puts them up and, you know, teaches them how to give a 10-minute talk that starts with a problem and, you know, ends up with this techno-optimist solution and it has three jokes along the way at minute, two, seven, and nine, and so on. And then, and then, you know, broadcast that in a central broadcast fashion. And then it's just absolutely so different from the dynamics of what we see playing out on the internet, which can be at really enormous and powerful scale as we see things like the dreadful information around the pandemic video and so on taking off. So understanding those dynamics are so fascinating to me. That's why I keep coming back to it. But absolutely, I mean,

the seeds of all of this predate social media. There have always been, you know, there have always been charlatans and hucksters and all of this. And a lot of that takes the form, I think, of of bullshit rather than of just outright lying, because a lot of them just want to impress you and make you think, wow, that person's really smart. That person's a thought leader, that, you know, whatever. Well, yeah, the thought leader terminology is a good one because it once again comes back to the incentive structures. I mean, I know that there has been a lot of discussion in recent years on the role of public intellectuals. And one claim is that we don't have public intellectuals anymore, all we have are thought leaders. And the difference being that the thought leaders are a little

bit more beholden to, you know, certain patrons who will pay them to say optimistic things about technology or whatever. And it's a little bit less rigorous and scholarly than the public intellectual model. Yeah, that's very interesting. I think that, that, I know, I, I, I've just heard it now, but I buy it, at least on a first, on a first thought. Also, I guess there's not much intellectual about a thought leader as the other. Well, yeah, I mean, I think I don't know what the origin of the phrase thought leader was. It might be something that was originally very sincere and now has been given so much irony layered on top. But it might be Dan Dresner.

It was, who made that argument. Who was it? Sorry. Daniel Dresner, maybe. Okay. The place I know it was, you know, in the 90s, this was the term for that, so basically it was what the pharma companies used. to influence what doctors do. Inference of prescribing practices,

it's too expensive to get things, to get on-label uses approved through FDA, but what you can do is you can have people give talks at meetings about off-label uses, which then doctors have the right to do. And thought leaders are the people that people look up to who would be able to give a compelling talk in front of an audience of, say, 5,000 or 10,000 at a major medical conference. And so a thought leader was precisely the person that at Farmer would seek out to give a talk about off-wable use of their product.

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I mean, that was the, you know, and I don't know if that's the original origin. That's just my first place I encountered it. Maybe I do want you to have a chance to say a little bit more. about solutions to the technology social media problem. I mean, you mentioned that there could be intrinsically technological solutions or regulatory solutions, and neither one of these were things you're really optimistic about. I mean, maybe say a bit more about that. I know that that's also very much in the news these days, especially with Facebook,

Twitter, maybe a little bit less, but Facebook, YouTube, there are algorithms that drive people to places where maybe we don't want them to go, and maybe that is fixable. Yeah, I mean, that, so this is a really important. important issue. And maybe we'll just start with YouTube, right? Is this well-known effect that YouTube sort of radicalizes. And so, you know, YouTube pushes you toward more and more extreme content. We talk about this in the book. There's a little story where Jevin and his son are watching the live feed from the International Space Station on YouTube and then on the sidebar are all these flat earth videos. And so, you know, so I mean, this comes back to the constant A-B test and

the psychological experiments that, you know, these algorithms are figuring out that people move toward more extreme content over time. And there, of course, all of these algorithms are being, you know, are optimized to maximize engagement, not education or accuracy or anything else like that. So, you know, I think definitely that kind of algorithmic direction is really important and really problematic. I mean, we see something similar if you try to use Facebook or Twitter to get a good sense of what the general zeitgeist in the country is,

not just the zeitgeist among the friends of an academic in Seattle, say. So I follow people all over the country with different opinions and things like that, but Twitter quickly learns that when some of my right wing follows who I don't necessarily

tend to agree with post blinks, I don't click on them. So it stops showing them to me. And so then I get pushed back into my own filter bubble, if you will. So, I mean, this is where I think regulation could be somewhat useful is I'd like to see, I'd like to see ideally it's sort of opt out, but even opt in would be okay. I'd like to see people have a lot more control over the content that they actually see on social media and things like this. I'd like to see these algorithms, you know, the sort of recognition that these algorithms are not designed.

to move us toward this extremely important, you know, feature of having an informed electorate. They're designed for something else. And given that, and given the role these are playing essentially as, as major media companies, we need to have a little bit more, you know, we need to downplay the influence that these algorithms are having. So, you know, I'd like to see if you opt in, on Twitter, you should be able to just see what your follow, the people that you're following

have posted in order. You know, no bullshit. And so these kinds of things, I think, would be little steps that would move us in the right direction. I think they're, they don't, they don't trouble me in terms of sort of First Amendment arguments. I mean, if you look back to the origins of, of the fair and balanced doctrine from the FCC prior, you know, until it was killed under, under Reagan, it was.

it was not something about bandwidth limitation per se. It was basically that it was a synchon for democracy, was that people, you have to have an informed electorate, and so we need fair and balanced coverage of the issues that matter. And so that was one of the other criteria there, was that the news stations had to talk about relevant things. And so I think that there's quite reasonable precedent in the way we think about media to say that, you know,

another synchon for democracy is to have people able to get access to the information they want instead of being manipulated by these algorithms that are able to run nonstop experiments on them 24 hours a day to maximize their own engagement. So that would be kind of my ideal. There are these little things you could do that would chip away at the problem, but would only chip at the way at the problem. You know, it's like, why do we allow targeted political advertising on social media? This is so dangerous. I can, you know, target all of the, you know, I can target all of the unemployed men between 35 and 45 and 45 and Taquilla who have indicated racist sympathies. That's something we've never been able to do before, more or less trivial to do on, you know,

with social media advertising. And then once I do it, the ad that I've put out there is dark in the sense that if I run a national racist broadcast ad, everyone sees that I did it. But if I just push a message to these 400 people, No one even knows that I did that. And I think that's a really, really dangerous thing. We've got very strong restrictions on political advertising overall. And so why we continue to allow this?

I have no idea. I mean, again, a tiny piece of the puzzle. But these are the sorts of places where I think regulatory solutions could be useful. Has there anything that you specifically learned or changed your views about since the COVID-19 pandemic hit? I mean, you've been out there on the Twitter front lines trying to set people straight. Is it more or less, given that you're an expert in both biology and bullshit, what you would have expected, or has your world been rocked by how crazy things have been? It has somewhat been rocked because I really did think we were all going to be on the same, you know, all going to be on the same team when this thing hit. And I thought that, I mean, you remember what things were like after 9-11, you know, like everybody just felt like we're all Americans and we're going to put these political divides behind and we're going to unite and we're going to solve.

this problem. And yeah, I mean, it wasn't, you know, it wasn't blissful. There was a lot of, you know, latent and not so latent racism that came along with that. And it wasn't all good. But you didn't see the sort of massive hyper politicization of just every aspect of, you know, the crisis that we're dealing with. And we did have this feeling as sort of a common goal. And when you deal with the pandemic, of course, you know, you may not want to admit it, but we really are all in this together because of the infectious nature and of the disease and the fact that these things spread exponentially through communities early on and so on. And yet we've completely failed to address this as a unified country. We're unable to, you know, not only are we unable to sort of mount, you know,

widely supported consensus policies about what to do. We can't even agree on things like whether this is killing one person in 100 or one person in 1,000. We can't agree on whether or not, you know, school children who get it are risk to their parents. We can't agree on whether masks help. I definitely did not expect that level of politicization of the science. And, and, you know, I think some of the ways in which that has happened have become quite interesting as you look at what's changing about the science. We've gone to, you know, in an effort to solve this problem, We've gone to this most massively open science that we've ever tried, where everyone is posting raw data and preprints. You know, immediately people are sharing all the code.

All the bottles are up there for the most part. There's some exceptions that are sort of notable. But, you know, it's been much more transparent than it ever has before at much earlier stages, waiting until everything goes through peer review. The discussion is taking place on open boards, on Twitter, on pub peer. places like that instead of at conferences and by private email. So all of that discussion is out there for the public to see. And the piece that I kind of hadn't really done an adequate job of anticipating was that as soon as that happens and you have this thing super politicized, now every single paper

that goes out there lands on one end or the other of a political spectrum, unless you exactly split the middle and sit on the rail. And as soon as that happens, you know, that paper gets picked up by one side and used as a cudgel to bash the other side with. And so, you know, as soon as I release a paper, like if my paper is just trying to estimate R not for this disease, you know, the intrinsic rate of increase, that paper is going to, you know, benefit the arguments of one side and harm the arguments or the other. And all of a sudden it just gets launched into this, into this partisan debate that we as scientists aren't used to. I mean, we're completely used to the fact that, you know, you think R not's two, and I think it's three, and I think you're an idiot,

and I'm going to prove it, but there's no partisan tie to this. And so this new, and similarly, people will, you know, when your evidence overweighs mine, people will just take your side. And now that this has become polarized like this, you know, even if your evidence overweighs mine, no one's going to, you know, no one in my tribe is going to take your side anyway because they don't want to risk anyone thinking that they're in your tribe. So this is the part that surprised me and has been, you know,

I guess in April I just found it enormously discouraging, and I think I kind of accepted it at this point and just trying to think about how to work in that environment now. But it was, yeah, that was a real shock. I mean, in addition to the sort of political or identity aspects of it, I mean, there's some sort of purely scientific aspects that are curious to me. I mean, the idea of how to model the spread of an epidemic like this. And there's even been, I mean, maybe this counts as politics, but there's been little squads. like economists versus epidemiologists and who has the higher GRE scores, right? Yes, right, right, right. Have we learned, have you learned anything about that, let's say?

Has that surprised you any? I guess I already knew it, but I've learned you shouldn't hold George Mason against economics. I mean, that's one thing I've learned. But, you know, I think that's actually been more common. You know, I've done, you know, I've always loved doing interdisciplinary work, and it's been tremendous fun. And it's a learned, it's a learned skill, right? And one of the key things to doing good interdisciplinary work is, is not assuming the other person's idiot even when they sound like one, because you're probably not understanding something. But it is something you have to learn and

you have to learn by doing. So I think you have a lot of people that are all of a sudden coming from different communities working on the same problem where maybe they haven't done as much interdisciplinary work in the past. And so you do see more of these of these kind of clashes, whereas, you know, my own experience, you know, so much fun, you know, sitting down with, you know, computer scientists and having them tell me about game theory from their perspective. And you just think, gosh, like everything this brilliant person has said for the last hour is wrong. How can they be so dumb? And then you finally eventually figure out what the, what the difference in assumption is, you know, you're thinking about expected case and they're thinking about worst case. And that clicks. And

And then you know, then it all, and if you, if you poison the well before that clicks, things, you know, you don't move forward. So I think, I think that's been the, you know, I think that's been some of the struggle there. And, you know, there, you know, I don't want to, to some degree, I think there are different disciplines that have, you know, different cultures and different perspectives of their own superiority. That may be, you know, we may be bumping into a little bit of that, too. but I don't know.

And you already mentioned the fact that some of the science is being done out in the open. But this is, there are issues, so not only is that being out done out of the open, but then it's instantly politicized, like you said, but these are not new things because of the pandemic. We're learning things, we're seeing things that we're always there, right? It's this more of a reminder that science is kind of messy and politicized, and we should keep that in mind when we evaluate any bit of science? I would I would disagree.

I mean, I guess the, of course, one thing I just want to note before I do that is that I'm acting as if this is a surprise. And all my friends in climate science are just thinking like, you know, you poor innocent, like naive child. Like what the hell did you expect? And because they've been dealing with this for decades. And the rest of us just kind of missed that. And I missed it even though I was not, you know, even though I was actually involved in all this sort of evolution wars stuff. in the early 2000s around intelligence design creationism and all that. And I still didn't anticipate this.

Yeah, but I think science is really not politicized in the same way that we're seeing right now. I mean, I think that, you know, there are areas where as you move toward applications, you start to see more and more of that. But I think what we talk about is being political in science is really, really a quite different thing. You know, I take, for example, in my own home discipline of evolutionary biology, mathematical population genetics, when we talk about sort of political fights, it often essentially come down to battles between the, you know, great-grandchildren of Sewell Wright and the great

grandchildren of R.A. Fisher, right? And it's, and it's absolutely remarkable, actually, if you, you know, Mark, Phelman gives a great lecture where he sort of traces back these ideas. And you actually see that we really are the great-grandchildren, you know, having the fights, continuing the fight between these two men. And so that's really politicized, you know, maybe it's like, you know, it's like, God damn guy as a selectionist just through and through. I just don't understand how he could not see the way that epistasis works. And as we talk about that as political, but it's a really different kind of political than, you know, Trump thinks hydroxychloroquine will work. and damn the conspiracy that's trying to cover it up. No, that's true.

Just to clarify, I was using politicized in the former sense, more, you know, nothing to do with right versus left, Republican versus Democrat, red versus blue. But there are human attachments, whether they're, you know, or even just ways that you were brought up as a scientist to pay attention to some things versus another that are not completely rational that have huge effects over what we think is important and how we go about doing

even the most removed science from political cares in the real world. Yeah, absolutely. I think that's a really good observation. A friend of mine, Jacob Foster, who's in sociology at UCLA and I spent a couple of weeks trying to, and not making a ton of progress on this, but trying to understand whether an adversarial model of science, you know, we have an adversarial model of justice in this country where you've got a prosecutor and a defendant. You don't have sort of consensus where everyone sits down and decides whether the

and skills you're innocent. And to some degree, science is more adversarial than it's usually portrayed in the public because you do have these different schools and these different, you know, political camps in the sense you're talking about. And so we were thinking about whether that could possibly actually be a good thing. And I think it probably is. And, you know, because it really sharpens the the conflict among the data that we

that we have. You rarely have direct observation of what you want to know. You have these various indirect ways of trying to get at this question and where there are conflicts there, if everyone kind of just agreed to ignore them, we might not make the same headway as we do when you have

you know, this school is determined to prove that that school is wrong and and so forth. I think that's all good and well. I think what's been, you know, so discouraging with the pandemic is the way that you just see a whole different level of disinformation getting put out there. I mean, things that scientists would never do. I mean, you know, I may really dislike the, the, you know, selectionists in population genetics, but I'm not going to make a video where I

tell lies about them. Nobody does that. I'm just going to try to write a paper that shows that, you know, their interpretation of the data is stupid. And so, so like, like this is just a whole different level. And I'm certainly not going to be, you know, calling their universities and trying to get them fired and sending threats and all the other things that public health officials in the United States are dealing with right now. So that's, that's just sort of a different. scale. It's like, I don't, I mean, maybe, may, and this may just be, you know, sort of, uh, appeal to the conventions that I'm comfortable with, but, uh, at some ways I'm kind of saying, you know, boy, people aren't fighting by the rules this time. Well, emotions are running high and stakes are

high, right? So even if I deplore it, I get it. I'm not completely surprised. And even, you know, far removed from those kinds of stakes, the examples that are close to my mind are like loop quantum gravity versus string theory, right? And I do worry about the, even though I'm pretty establishmentarian on many physics issues, I do worry about a lack of diversity, viewpoint diversity here, because from a game theory perspective, if you think there's a 90% chance string theory is right and a 10% chance that loop quantum gravity is right, every department is going to want to maximize the chance that its people are right.

So they will only hire a string theorists, right? And it's hard to build in that kind of multiplicity of efforts. Yeah, a couple of my friends in the philosophy of science have written quite nice things about this, in particular, Kaelin O'Connor and Kevin Zulman. And they've written about the value of epistemic diversity in scientific research. and they've studied these kinds of things where you have, say, epistemic networks where people share their beliefs with one another. And in fact, if you have this too tight of a community where people quickly adopt one another's

beliefs, you know, basically everybody can get off on the wrong track and science doesn't proceed as fast. And so I think that's, you know, there's a very important observation. And this has really been something I've been very interested in the last five years and then sort of got derailed by the whole COVID crisis. but basically like, you know, taking a hard look at the way that science operates and looking at what are our institutions and our norms that we use and thinking about the way that those norms and institutions create the incentives that shape our behavior and the questions we ask. And, of course, that determines the outcome we get. And so there's this direct tie from the structure of these norms and institutions, which evolved more or less haphazardly, you know, out of, you know, essentially enlightenment ideas and Western Europe to.

to the beliefs that we have about the world is emerging from science right now. And so one thing we can do is we can go back and look at the structure of some of these institutions, whether it's things like publishing or priority rule for credit or the tenure system or whatever it is and ask, how are these things affecting epistemic questions

about what we believe to be true that is true, what we believe to be true that isn't, what we don't know is true even though it is, et cetera. And thinking about, you know, are there ways to nudge the system to make it function better? We see a lot of this going on as people are trying to tackle the reproducibility crisis right now. And, you know, you can ask very similar questions around issues such as viewpoint diversity. Yeah, I mean, and the mentioning of the reproducibility crisis brings up the previous thing you mentioned about how science is now being done in the open more. And I know there's also two sides to that.

You know, science is being done in the open a lot more, including the fact that, especially because of the COVID crisis, people are noticing that there are pre-print servers, right? You can get scientific papers that haven't yet been peer reviewed. And on the other hand, something I, even though I'm a believer in peer review, I do try to tell people that just because a paper has passed peer review does not mean it's correct. You still need to be a little bit skeptical of it. I mean, do you think that this greater openness is improving science or is it a danger? I know there's a lot of people who say it's terrible to let the Hoy-Polloy in on all the speculations and preliminary ideas that scientists bat around all the time. That's geekkeeping bullshit. I completely disagree with that.

I agree, but I'm not. It would be, you know, I mean, you're a physicist. You know what it does to science when you open up pre-print servers. It blew my mind the first time I wrote a physics paper. It's a paper on the, it's a paper. on the paper on network theory, wrote it with postdoc of mine, Martin Rosfall. I was really excited about it. I thought it was neat work and I really hoped I could get it into a good journal so that people would read it and give me some feedback on it. Martin wasn't nearly as concerned

about this. We posted it to the archive. And within a week, every single person that I wanted to read it had written me back, you know, unsolicited and had read the paper and had the comments that I was hoping to hear or in some cases critiques I was hoping they wouldn't, you know, that wouldn't exist and so on. But that was like, that was a revelation to me. So, you know, having preprint servers and, and, you know, having the time lag between putting an idea out there and it being adopted by the rest of the community being a few days instead of a year is, is enormous, obviously. And I also think that, you know, this, this notion of gatekeeping is, you know, and we shouldn't let the public see what's going on behind the walls of the ivory tower. This is also absolute rubbish.

What we could do a better job of is explaining how the whole process works. And if we do that, then we have to hit on the thing that you hit on as well, which is that, you know, peer review is no guaranteeer of correctness either. What I think of peer review as, and this we write about, we have all chapter about science and how it works and all this stuff in the book. But, you know, what I think of peer review as is I think of it as, you know, kind of like burn in engineering. It is enriching for things that are correct and interesting.

It's not guaranteeing that anything is correct and interesting. It's just taking a bunch of the stuff that isn't correct and a bunch of the stuff that isn't interesting and throwing that out. And you end up throwing out a little bit of the stuff that is correct and interesting. But that's the cost of enriching the pool of stuff that you see. And then, of course, you've got this tier of journals from the highly enriched to the barely enriched. And you can sort through all of that. And the highly enriched may be enriching for not the things you want. And people complain about most of the stuff in science and nature is sensationalized.

And so on. And that may be the case, whereas most of the stuff in cell is maybe enriched for being correct, but not being very interesting or whatever, depending on your perspective. But fundamentally, that's what peer review is doing. It's helping improve the papers as well. And it's enriching the pools. But it's not, you know, it's it's not this like seal of approval that it's been presented to the public as, unfortunately. All right. Final question. Going to be completely unfair here. I mean, I know you begin your book by saying that, what is the sentence you used? Do you remember it? There's so much bullshit, probably. We are a washing bullshit, something like that.

Yeah, the world's a wash and bullshit, and we're drowning in it. The world is a wash and bullshit. So, and we've mentioned a lot of techniques that people can use offhandedly. But, like, is there the best piece of advice you have for, you know, we people on the street who are a wash and bullshit to separate it out? I mean, is there like one or two pithy little things we can say, or is it more that we just have to generally keep our wits about us and try our best? No, there are a few pithy things that you can do. You know, one of them is to just recognize that if something, if some claim seems too good or

too bad to be true, it probably is. And so these are the things that we're most likely to share, especially on social media, and yet they're the most likely to be wrong. I learned today, only today, that there's a law about this, which 20, The common law for data analysis is that any figure that looks interesting or different is usually wrong. More unusual or interesting the data, the more likely they are to have been the result of an error of one kind or another. And this seems quite a reasonable law of data analysis. And it's a law of media analysis as well, I think, when you think about it.

So that would be one. You know, another thing I really push people on is, you know, especially in those cases. And if you care, track things back to the source. So, you know, one of the things that are. emerges through the social media environment, but we had even beforehand, it's just exacerbated, is that you have this sort of game of telephone where you'll have a, you know, maybe you'll have a set of, you know, research studies that are distilled into a scientific paper that is written up in a medium post that's picked up by the New York Times that gets tweeted about.

And then, and then you see the tweet. And so, you know, if the tweet seems too good or bad to be true, start tracking back and finding the you know finding this you know figure out for yourself what the story is so i think you know the most important and the most important and that's what is this is what we teach in the class uh and the students get so excited you know i'll have the students come in and they'll say you know oh i saw this tweet from NBC News and then i tracked it back and it was it was actually about this story and the story came from this research paper and you know what professor berg for me was bullshit and uh which is so cool and and and You've done good.

Well, what it gets at for us is a fear with this class is that we create a community of Nillist and cynics. Whereas what we want to try to do is to show people that despite the bullshit-prone world that we live in, there is truth out there and you can get to it. Yeah, that's a very good motto. I cannot possibly think of a better place to finish the podcast on. So Carl Bergstrom, thanks very much for being on the Mindscape podcast. Thanks. It was great to talk to you, Sean.