The Origins Podcast with Lawrence Krauss - Saul Perlmutter: Third Millennium Thinking

Episode Date: September 18, 2024

Saul Permutter won the Nobel Prize for his eventual role in the discovery of dark energy. In 1996 when I was lecturing at LBL he bet me that he would show dark energy didn't exist. His group had been measuring supernova distances for years, in hopes of determining the deceleration rate of the universe. Instead, after recalibrating some of his earlier data, his group and an independent group discovered the universe was actually accelerating. That is the beauty of science, it supersedes any individual prejudices, and scientists actually change their minds if the data requires it. This is one of the many important characteristics of science that Saul and his collaborators discuss in their recent book, Third Millennium Thinking. It is a good read, full of useful examples about how scientific thinking is important in the world beyond just science. Saul and I had a lively conversation about science, the scientific method, and his own experiences as a scientists. It was an enriching and enlightening discussion, and I hope you enjoy it as much as I did. As always, an ad-free video version of this podcast is also available to paid Critical Mass subscribers. Your subscriptions support the non-profit Origins Project Foundation, which produces the podcast. The audio version is available free on the Critical Mass site and on all podcast sites, and the video version will also be available on the Origins Project YouTube. Get full access to Critical Mass at lawrencekrauss.substack.com/subscribe

Hi, and welcome to the Origins Podcast. I'm your host, Lawrence Krause. In this episode, I had the chance to talk to a longstanding colleague, Saul Prometar, who won the Nobel Prize in 2011 for his contributions to the discovery of something near and dear to me, the fact that the expansion of the universe is accelerating and the apparent fact that the dominant energy of the universe resides in empty space. This was a fun discussion because it wasn't just about Saul's, own scientific background in history, which we covered at the beginning. But in fact, I wanted to focus on this because of a new book that he wrote. And that new book is a collaborative effort with two

other colleagues, a psychologist and a philosopher called Third Millennium Thinking. And it's really about how scientific thinking can be important in affecting the way we think about the world. Obviously, a subject I've talked a lot about in this podcast and in my own work. But we had a great discussion of his own background, and the many tools that scientists that the people can use from science to make the world a better place, and also the limitations of those tools. And it was a really enjoyable discussion, including a frank discussion of some of his own work and some of my work and how that fit in. I think listeners who want to learn more about science and about salt science, but about the importance of science in the world, will really benefit from listening to this discussion,

I really enjoyed, and I hope you enjoyed it as much as I did. You can watch it ad-free on our Critical Mass Substack site, and subscriptions to that site, paid subscriptions, will go to supporting the Origins Project Foundation, which produces this podcast. Or you can watch it or listen to it on our YouTube channel or listen to it on any podcast listening site. And no matter how you listen to it or watch it,

I hope you'll enjoy it, and I hope you'll consider supporting the foundation. So with no further ado, Saul Pumuter. Well, Saul, thanks so much for being on the program. I really appreciate it. It's been a long time, so I've been with you in person, but we go back a long way, and so it's nice to see you again. It's nice to be here with you.

Yeah, well, and it's fun to not just talk about you, but this new book that you've co-written with fellow colleagues, which I feel obliged to show Third Millennium Thinking, and we'll get to that. And it's a wonderful, read and and most and not least because I agreed with almost everything in it. But we'll get there. But as you may or may not know, this is an origins podcast and I want to begin, therefore,

with your origins. I always like to know how people got to where the point that we want to begin, to the point in this case of the book or your work. So I want to begin. And it's always fun for me to learn about, especially for people I've known a long time like you where I get to now learn more about your origin. which I learned about, interestingly enough, so your father was a professor of engineering, is that correct?

That's right. And your mother was a professor of two, but I didn't quite, like administration. What does that mean? So it's a branch of social work, and it's the area where as you go from caseworking individually to actually administrating programs, they study what works, what doesn't work and what people learn about it. So you obviously grew up in an academic household. Also, were your parents, you know, I notice that your grandparents,

one of your grandparents taught Yiddish. Is that right? That's right. So all four grandparents, that was probably one of their first language. And my parents both knew Yiddish growing up, although, you know, I think they mostly used it for us as a secret language to tell when should the kids go to sleep, you know. Yeah, yeah, that's right. My parents spoke Yiddish when I learned German, hoping I, when I was a student, hoping I would understand when they were having their secret conversations. But needless to say, they didn't teach you Yiddish then, did they? Well, we got sent to a Sunday school, which was a, you know, there's a Yiddish culture movement that was very non-religious and probably fairly strongly, you know, non-religious, but very strongly cultural. And so we were taught Yiddish. We were not taught Hebrew, actually, in the Sunday school. Oh, that's interesting.

There was a short period in which I actually could carry on a reasonably conversation in Yiddish, and I can still read, but most of it I don't have. Oh, that's great. You can read it. I just know the standard words that we learn, a few of them, that are useful. Now, but Yiddish, as you just point out, speaking Yiddish and being religious are not necessarily the same thing. Was it a religious household, or your parents religious?

Were they? No, all four grandparents. were, you know, were, I think relatively strongly anti-religious. I think, I mean, mostly I think it was a, it was a period in which a lot of sort of the enlightenment ideals were coming through in that part of the world. And I think these were people who all were, you know, planning to move to the, to this new, new world, you know, new country. And I think they had, you know, this, this free-thinking style.

My sense is that it was actually relatively widespread. And at least there's a very big community that then showed up in places like Philadelphia and New York that probably shared that style. Oh, interesting. And obviously then your parents shared that. You did go to Sunday school. Were you bar mitzvah? I was. But in a secular ceremony that it was all seen as a cultural activity.

So we wrote a talk about a socially relevant topic. we, you know, we sang, you know, songs. And so it was, you know, definitely a whole different experience than those friends of mine who grew up in a more traditional, you know, Jewish synagogue environment. Yeah, well, I went and I was the latter, and I wish I'd have been the former, because maybe I wouldn't have reacted so negatively to the whole experience. But did you, did you, not that it matters, did you, were your parents, um, did you, were they athe? or Ignorcer, did that issue ever come up? It was not actually a major topic of conversation, but I would guess, and I would say that they would probably think that atheism was

the obvious default. The norm, yeah, yeah. It probably didn't even occur to them to discuss it because it seemed so clear to that culture. Yeah, no, you know, you make up an important point, which I've talked about otherwise. A lot of people seem to think this God thing is really important in science. I don't call myself an atheist anymore as much as an apotheist because people don't realize that the question, you know, I've both been scientists for over 40 years. And I've never been to a scientific meeting and ever heard the word God discussed at all.

So it just doesn't come in. It's just irrelevant to our understanding. So we don't even think about the question. I mean, one way that I sometimes describe this to people is I say, okay, so the problem is that if you were trying to figure out what, like in our case, you know, we both work in these areas of cosmology where the, you know, the origin of the universe, you know, the Big Bang, it obviously comes up a lot. And so, you know, people say, well, you know, doesn't that tell you something about, you know, about God? And I say, the problem is that if it told you anything about God, then you'd want to know more, and that it wouldn't, it wouldn't answer any of the questions if you said, well, this was all the creation of God.

It doesn't help because you then want to know, okay, well, did God have other options? Could he have done something different? Did he have to work within a, you know, rules of physics? Or could he make up the rules of physics? And if so, how does he make it up? You know, how does this stuff work? Right? And so it doesn't really address the questions that the scientists, and for that matter, most people in the end care about. It just puts them into a different context.

Really a good point, in fact. And Einstein, unfortunately, gets quoted because he didn't believe in a kind of God, that the canonical kind of be often said what most interest me. And this, by the way, is probably what most interest me, too, is the question of God had any choice in the creation of the universe, which for physicists like you would mean, the question you just raised, could there be other kinds of universes with different laws of physics, or that they have to be the kind we live in today? And of course, the work you've done is important

in that regard. And then I've spent a lot of time on it as well, and thinking about whether it means that our universe is just a cosmic accident, which I think is probably what most theorists think probably right now, which is quite different from when you and I were younger. When I mean, I became a scientist because I want to know why the universe had to be the way it was. and the answer may be, well, that's just an accident. But anyway, we will get there. Yeah, yeah. Of course, that's still one of the,

now that's become one of the interesting debates, you know, as to what exactly, exactly. You know, and so, but I think the key point is that, well, and I wasn't even to go here yet, but the key point in cosmology is that it hasn't, that there's no evidence of anything, the need for anything beyond the laws of physics to get where we are now. And it's kind of amazing that using the known laws of physics,

we can understand plausibly the history of the universe right back from well before the first second of the universe to now, that is an amazing, amazing result which we should celebrate. Yeah, no, absolutely. Now, at the same time, I always, you know, sometimes when I get asked some of these questions, I always feel like it's important to at the same time say that that doesn't at all address the human need for understanding meaning in the world, right? And so that all that could be true and all that could be things that we will almost take axiomatically for what we're doing as scientists. But it doesn't answer the question that people are in some sense really asking for.

And that people want to know, well, where do we find meaning in this world that we live in? And for many people, of course, the key element of religion is that it actually allows them to live in the world in a comfortable, way. And I always feel like, I don't want to take that away from anybody just because it hasn't happened to be, you know, the way I reach for meaning, you know. And so this is funny sense in which people sometimes feel like it's a, you know, possible conflict that I feel is, it almost misses the main point, which is that people want both things. They want to understand the, you know, the facts of the world, but they also want to understand, you know, where does our meaning come from? And they, and those in some sense, they were originally both done through some of these religions, not every religion, but many religions. And so I don't, you know, in some sense, I don't want to undercut that need that people have, which both those needs are there.

Absolutely. I mean, if religion didn't fulfill that kind of need, it wouldn't be so ubiquitous in human experience since every culture has them. And people have searched your meaning. And, you know, and for, you know, and for people like me, I'm happy that we, to think we make our own meaning. But exactly.

But, you know, it's actually extremely interesting you point this out because near the end of our conversation, we'll get to this because the book is about something that I've spent my life doing, which is trying to teach people out to think like scientists because I think it's important for our society. And obviously, that's what the book is large about. But at the end of it, of course, we talk about values versus science,

and we'll get there. We'll get there. And how many hours it takes us, we'll see. But we'll get there. And so we'll come back. back to this question of meaning at the end. So it's a nice circle to have. But I often say that, you know, if the sky, if I look up at the sky tonight and the stars have rearranged themselves to say either in English or Aramaic or Hebrew or whatever or Greek, I am here, well, then,

you know, then I might think cosmology was telling us something that it isn't right now. But even then, you'd immediately go to the question of, how did we do that? Yeah, exactly. Yeah, yeah. I'd ask the question. but I'd be willing to be more open to the fact that maybe we had to go beyond just the known four forces of nature. Okay, good. Well, I've gone far away, but let's come back to you again. So your mother was background in social work, your father, in engineering, an academic household, and you were a nice Jewish boy.

Your parents wanted to be, I assume a professional, but unlike my parents, they didn't care if you became a doctor or a lawyer. assume. I think that's true. I think they both grew up, of course, in immigrant families, which were relatively, well, very poor in some cases. And so for them, going, you know, getting educated was actually part of entering into a functioning economic world, you know, where they could actually make a living as well. So I think there was a little bit of the first generation need to know, are the children going to be okay? You know, you know, Are they going to be able to find jobs? So I remember that as a student, as an undergraduate, I was looking at physics.

I was also looking at philosophy, but I ended up being a physics major. And my parents were reading the articles at that time in the newspaper describing how physicists were having a hard time finding employment. And so they were asking, you know, have I considered like taking some engineering along the way? because, you know, engineers always eventually get employed. At least that was the feeling. And so I remember at the time saying to them, you know, at the end, I think that I will probably be able to make a living and I will probably make a better living doing something

that I'm really personally invested in, personally excited by. And so that was, and I think they understood that, you know, and they did. But you're right, that was the kind of thing where I think parents who were living in that slightly academic, well, in a very academic and professional world, they would be looking and be more open to different ways you could do that. Absolutely, yeah, they're much more, yeah, unlike my parents didn't graduate high school, so they didn't quite understand the issue.

For them, a professional, and education was doctor or lawyer. But interesting, though, interesting, I wonder whether your father might push you more towards engineering and physics. Because when I was chair of a physics department, I had that a lot when we were talking to students who were choosing. And we actually created an engineering physics degree, partly in response to that, because, you know, we would say that the best part of, you know, of engineering is learning about physics is learning how to solve problems. And interestingly enough, what I learned in the process of that was that most physicists

outside of academia who get employed are called engineers. So they don't have to have an engineering degree. You can be basically employed as an engineer with a physics degree. And I would always say that's a better route, but that was just obviously a prejudice. And of course, the equivalent of that nowadays is that almost any one of my students ends up being a highly trained data scientist in the end of the day. And of course, that's, you know, in some ways they can get jobs there more easily than almost any other training because people recognize that the physicists tend to come with this

extra ability to think about the problems as well as the computer programming. Yeah, as well as learning the numerical thing. I mean, one year when I taught at Yale, all of our, every one of our PhD graduates went to Wall Street and got a good living. But beyond, you know, caring about whether you did a profession, I want to wonder whether where your interested in science came from. Was it your father? Was it your mother? Was it reading? Was it an uncle?

Or was it good teachers? So I'm always interested in where people got that kind of thing. Yeah, I think for me it was a mix of a wide variety of those things you describe. I think I was just, partly I was just one of the kids who just was always curious, you know, about how things work. And I think to some extent I was always curious about all the different sort of languages in, I say languages, but I mean in any sense of the word languages of the world, including the languages of physics, the languages of, I mean, different people in different cultures, but also the languages of different fields. You know, how did people describe, you know, their areas? And I think in some sense, you know, physics is so the ultimate of getting down below all this. I mean, that you want to know, where does it all come from?

And I think that was, you know, in my mind, at least at the time, one of the reasons that it was exciting, you know, to be able to think that you could understand something that was beneath and underpinning so many other things. Did you read much sort of popular science when you were younger? that happened in fact, I did. I, you know, I was certainly reading, you know, bits of scientific American and the Martin, the Martin Gardner, you know, articles and, and, you know, occasionally, you know, some of the popular science writers, although they weren't as prevalent and visible then as they are now. But so I think that was, you know, a little bit in the mix. I had a excellent physics teacher, but I think that was, and, you know, which was fun. But that was, but that was,

It's just, you know, one year, you know, out of many in the... He probably already had the bug by then. You probably helped him more than he helped you, perhaps. But by having a good student who's interested often helps. Did you, what about general reading? Did you read while was reading a big part? Did you read, you know, addiction, comic books, you know? Absolutely, I was a big reader.

I think also there was a period when we were growing up where, in some sense, science and engineering was a little bit more of a bit more of the best. backwater culturally, right? That the dominant thread of being an intellectually interested person was not in the science side of the world. It was, you know, the humanities and a little bit of the social sciences. And so I thought that I, you know, I was somebody who I wanted to be part of the whole conversation.

So I was, you know, reading all sorts of things, you know, growing up. And I felt like it was very important to learn how to write and to think about fiction. and that fiction and learning how to understand one's emotions, that those were part of it, even though I was a bit more of a rationalist myself, and that it was important to learn not just to be rationalist because that was not where the cultural center was. Yeah, sure, certainly. And sometimes I used to moan that too,

that somehow being intellectual, you know, having taught at Yale for almost a decade, I could just see it where they didn't have to really take any science. But the idea was that, you know, to be an intellectual, you know, to be an intellectually. And we generally feel you have to be literate at some level in history and English. What's disappointing is somehow people think you can be literate and not have any scientific sense at all. And that's what books like this and other books are meant to counteract is that to be a literate

person without scientific literacy is he can't. And I think that's really something that we need to point out. It's not something just from column B. It's an important part of our culture, an important part of the solutions that are going to keep us going in one way or another in the 21st century. One of the odd things about that,

that there's been a cultural shift, I'd say, in the academic world, where the intellectual center has started moving much more towards the sciences in many universities. But what's unfortunate is that that didn't achieve what you're describing, which was to make everybody feel like you need this full picture of the world in order to be an educated, thoughtful person. Instead, it's just moved from, you know, being single-minded here to being single-minded there.

And I think we've, you know, and so now we've lost what you can learn from the humanities and the social sciences in an integrated sense. That's a good point. I think a lot of my own feeling having written about this lately and the influences, part of that is due to unfortunately the stifling of asking questions, the kind of questions. In science, you can ask questions and they're not politically charged often, depending on what science you do, certainly in our field, although even in our field, some people would argue. But in other areas, when you ask a question, it can create a lot of pushback. And I think, unfortunately, that's one of the reasons why people are less willing to have that kind of open-ended experience, partly for self-preservation. But anyway, that's a different

discussion. But you've probably seen it in Berkeley, and I'm sure you've seen it at Berkeley, because I know what's happening there as well as everywhere else. So since we're hoping that the, a bit of the approaches of the book that we'll get to, we're hoping our way of perhaps undercutting that and getting back to the ability to think together. It'd be great. It'd be great. That's an optimistic hope, and we'll see.

I'm skeptical of everything, and we'll get to skepticism, so that's important. The last thing before, want to get up to the beginning of your training really and to the to the issues of and you know we're not going to spend as much time here talking about your work as we might otherwise although it'll come in in the context of through examples in the context of the book um you one other question I'm interested about your background you went to public school first and then a private school uh right and and I wonder whether you could contrast that whether one or the other had a bigger impact on you

Well, to be fair, my public school experience ended up being only one third of first grade. Oh, okay. I didn't realize that. Okay, so it was limited. No, exactly. I mean, my parents chose to move to a neighborhood that had a strong public school that, you know, that they believed in public education. But at the end of the third of first grade, they realized I was bored. And so they ended up moving me back to one.

Well, to another very strong school system, which is the Quaker school system. Yeah, the Friends system, a lot of people. I know Quaker friends. I know people in the area who, yeah, in fact, Joe, what's his name? He won the Nobel Prize for Gravitation. An old friend of mine. Yeah, Joe Taylor, who I've known for a long time. He went to, he had a big background in the Quaker schools and probably more.

I did. He was too, yeah. Yeah, yeah, yeah, yeah. He definitely did. And I think continued to be involved with him. But anyway, okay. And so that experience, you know, was obviously. useful, a school where it was open for you and encouraged you to reach out beyond where you

might otherwise, which then gave you the kind of, not only just the kind of training, but I suppose the kind of imprimatur for better or worse to get into Harvard. And then at Harvard, you immediately decided to do physics. It wasn't a, it wasn't a choice. No, actually, for me, I was torn between the two very fundamental things I could think of at the time, which, with physics and philosophy. And I looked into the possibility of doing a double major of physics and philosophy. But it turned out that at that time, if you did that, that would take up all your courses. And I also want to take literature and economics and history and whatever else.

And so- You don't have a good bullet. I think in the end, I mean, I was same. I was fascinated by philosophy. In fact, as high school student read James Jean's book, Physics and Philosophy, it's one of the things that got me interested more in physics. And later on, I was going to go to Oxford with a road school. to do physics and philosophy. I'm just glad I dodged that bullet and decided to go to the U.S.

and do physics instead. But anyway, but yeah, you were an undergraduate when I was at the same time when I was a graduate student in the region, and then I moved to Harvard, I think, after you left to go to Berkeley. And, you know, I'm sure a bunch of my friends and colleagues who taught you and it had a good program. Howard George, I was really interested in teaching. I don't know if he taught you at all. Yeah, yeah. I actually took a really interesting, small, like five-person seminar from him

because it was the off year when he was not teaching group theory. And so five of us asked him, could he just teach us it as an independent study? And so we had a, it was actually very interesting because he is such a character as a strong, I had describe it. He's so warm and friendly

and when you're in a small room with him with a bunch of people, he's constantly smiling and nodding and you can't and you can't help but smile and nod too. And so it meant it was often very hard to break it to him as best we could that we weren't understanding something because he'd be explaining these to us

and we'd be smiling and nodding and then somehow at the end we all leave the class and say, you know, we have to go back and say and we just didn't get it, you know. You might, this may be giving you some consolation having been a colleague. You might imagine that perhaps it was going the other way as well.

Sometimes, you know, he may have been smiling and nodding sometimes and not understanding what you were saying. And, but anyway, yeah, he taught a course in physics for hot shops. When I was, there was a society fellows, I got to teach a week of it when he went away and it was kind of a neat, fun thing. And anyway, it's really, it was really useful. But then you, what made you choose Berkeley after you to get your PhD? So for me, it ended up being a pretty clear choice because I knew I wanted to do experimental physics, not theoretical physics.

But I didn't know which area of experimental physics. So I wanted there to be a place that was very strong in a broad range of areas. And Berkeley, at the time, probably Berkeley and MIT were the two that had the broadest range of experimental physics programs that were strong. and MIT to me still came across as too much of a science nerd world, and I wanted to meet people in all fields. And so I think that was probably the final thing. I do remember, though, taking Princeton very seriously, I mean, among others, because they had this very strong tradition in experimental physics, smaller, more boutique in that sense. But I do remember at the time talking to current grad students at that time. And they all said, you know, the great thing about this program is that it only takes four years.

They guarantee four years and you're out. And, you know, it's isolated. We're a little lonely. It's a little bit miserable. But it's only four years. It's great. I went to visit Berkeley. And for me, it was off the ends of the earth because it was, you know, California.

I've never been outside the East Coast, you know, for the United States before. although I've been Europe, you know, and so I asked the grad students here. I wasn't seriously considering it because it was so far away. And they said, oh, actually, they don't know how long it takes. They haven't actually even looked. They're having a great time and they don't even care. At that point I thought, well, you know, grad school is part of your life. You might as well, you know, feel like you're going to enjoy it.

Absolutely. I often say, yeah, when students ask me, you can get a good education in a lot of places, but go to a place you want to be. And I was thinking that for a nice young boy who'd been on the East Coast the whole time, going to Berkeley, not just in terms of other disciplines. You're right, MIT is more focused. I did my PhD at MIT. It's more focused.

Berkeley is obviously a much broader. But sociologically, the Berkeley environment would have opened you to vastly different experiences than if you'd stayed on the East Coast. Absolutely. I think that's right. And of course, you know, you don't know what the cultural differences are until you've lived these places. But it was very interesting to spend time then to have ways in which I felt like I missed the East Coast. And then you go to the East Coast and you go, actually now I miss the West Coast too. There are things that you actually start recognizing in yourself both places.

But you also mention something else, which is good as advice for students. And again, I've always tried to do it, which is if you don't know exactly what to want to do, go to a place where there are a lot of options. Because you might find, and even if you do know exactly what you want to do, you might find that what you wanted to do was not what you want to do. And if there's no other option, you're in a bad situation. So try, if you can, to go to a place where you can get exposure to a variety of different in physics, say, experiment a lot of different areas. And you might be surprised. And so it's nice to go to a place, you know, big place.

And, you know, I went to a place where I was the only student to graduate. I did a degree in physics and math. And then I went to MIT where they're like 300 students in physics. It was a big difference. But it's nice to have that experience of seeing all the different things. But you say you wanted to go into experiment. I meant to ask you another question, which I will answer now. I'm always interested in experimentalists asking them as a theorist.

So why did you want to be experimentalist? As a kid, did you tinker? A lot of the experimentalists I know had ham radios or other things. Did you tinker? What made you want to be an experimentalist? It's a good question. I'm trying to remember now, thinking back. I was a very minor tinkerer, so I would, I think when my pleasures was with some friends,

we would make little Rube Goldberg machines where, you know, this thing would trigger that thing, would trigger that thing. And I was always enjoying looking up, you know, and learning about new little mechanisms that you could, you know, that you could try. But I also really enjoyed learning about computer programming. And I think I, you know, because that was becoming available to us, you know, Sure. And every time I'd learned about a new algorithm out there in the world,

I would try to think, oh, is there a fun activity, a fun game I could make using that algorithm? So I was a bit of a computer tinkerer as well at that time. And I don't know whether those things were what led me. It may have been that I was perhaps. I remember one of the things that was most striking to me as a scientific American reader was when I first read the articles about the aspect experiments. And I remember, you know, the, for me, one of these. For listeners, those are experiments testing sort of quantum mechanics directly in the lab.

Sorry, go on. No, exactly. And they were showing things that the world worked in ways that you, that shouldn't, that shouldn't be allowed to work in. Right. And I think I realized that for me, one of the real excitements and pleasures of being a physicist is catching the world in the act of doing something that it shouldn't be allowed to do. That, you know, based on how our brains work, we don't. think it should, we don't think that should be a possibility. And yet, um, we find out that the world does that. And I think being close up to that sort of thing, being able to see it, you know,

directly as an experimentalist was something that I was attracted to. Okay. Well, that's great. But did you, was there, did any of the labs as an undergraduate? For me, the labs as an undergraduate turned me off. That's one of the reasons I did a math degree was to do fewer labs. Um, but, um, uh, did they, did they turn you on as an undergrad? In general, um, um, um, um, um, um, um, um, um, there was this problem that class labs were so canned, right, that you knew what you were supposed to see and you knew what you're supposed to do. And so it was rare that one would get, would really excite you. I'd say the closest was, I remember doing a lab with superfluid, with helium.

And, you know, seeing it do some of its surprising behaviors. And so that, but other than that, I mean, I'd say that you're, you're right, that I was still waiting to become a physicist. You know, I mean, I remember, I considered, for example, taking a year off and doing a, you know, traveling fellowship or something like that, and, and I did not get the traveling fellowship I was applying for. And I was thinking, well, should I, should I do something else? And I thought, you know, actually, the problem is I've not really dived into physics. I've, you know, I've taken my courses every year, every semester.

You know, there's one, one physical course or two and a math course. and then I would take my, you know, English and my whatever else, philosophy and courses. But I didn't really feel like I had dived in to physics yet. And also during that period, it was harder to do something substantial as a undergraduate in a research lab. I think over the years, all the universities have gotten much better at bringing the undergraduates in. But I worked a little bit in one or two labs like in the summer, and I never felt I really got to do much. So I was at that point, feeling like, well, why don't I go to grad school just so I can find out do I really like this physics stuff? You know, I was almost, you know, I wanted to give it a try.

Oh, that's good. It was an experiment, an experiment, an experiment, an experiment, an experimentist. No, no, I think that's great. I agree. I went to grad school, not expecting a lab, but I thought, you know, what the heck. But, and I think it's really important, again, to give advice of students. Too many people think about jobs. You don't know what the job market is going to be like.

And as you said, you really wanted, if you're going to spend your life with something, it should be something you enjoy, but also, you know, you don't know. So try it and see if it works. And be governed by that more than the likelihood that it will lead you some specific place that it probably won't lead you anyway. But, okay, you knew you wanted to be experimentalist, but you didn't know what area? I mean, it wasn't as if astrophysics was something

that had been a central preoccupation like some people. No, in fact, I had almost no astronomy background. I mean, unlike, you know, many of my colleagues who had backyard telescopes, et cetera, I had almost zero contact with astronomy. And I was assuming that I was going to, I knew I wanted something that was getting at these fundamental questions that were surprising things about how the fundamentals work, you know, in the world. And so the most obvious thing at that time was that a huge amount was happening in particle physics. Of course.

I'm the big accelerator projects. And so I was assuming I probably would do a particle physics project. and I looked at some of the groups, but there was a bit of a tension going the other direction, which was that you tended to be in a huge, well, at that time they were big, but they're becoming bigger. Bigger, yeah. These collaborations, and I think I wasn't quite ready to sort of join into being part of a huge culture. I wanted something where I had a little bit more chance to be a bigger part of the project, myself. And so there were a few of the faculty who were doing what were particle physics-like projects,

but they were with very small groups. And so one was a professor of Beaufort Price, where they were doing experiments, you know, where they were looking at tracks of particles and plastics. And there was only a half dozen people, maybe even two or three, on a given project. And so you could feel like you were doing something substantial. I probably would have stayed in that group, except the fact that the timing of when the next big project was about to happen would have meant that I would have worked up to the point

of maybe taking it out to the field, but never had enough time to collect the data and analyze it. So I started looking around for another group, and I found this rather surprising group that Professor Richard Mueller was running at the time, where it was surprising in the sense that he was... fearless about taking on any technique, any scientific topic that was of interest where he thought

he had an angle that would get at something interesting. And so that meant that, you know, at the time I joined that particular group, there was a, oh, they were doing one project that was a tabletop cyclotron that could be used for mass spectrometry. There was another project that was doing Raman scattering to study carbon in the atmosphere that you picked up these samples by flying high flying airplanes over the ocean. And so it's looking at the carbon cycle. There's another project. They just finished, which was the first adaptive optics project where they made an adaptive

mirror that would take out the twinkling of the stars. We were still associated with a group that had done CMB measurements because that was one of his first projects was measuring the CNB dipole. And so all these things were going on, including some astrophysics projects. And that one of them was a robotic supernova search project, which could use my computer interests. And it was getting at saying that it could be kind of deep. It was getting at the age of the universe, which of course was your topics. and trying to measure the Hubble constant with nearby supernova was the idea at the time.

And so that was going on. And I remember thinking, you know, astrophysics could be a way into all this stuff because it does have high energy events happening. Maybe you could get at some of the things you get at with these particle accelerator experiments using astrophysics. And so I started getting interested in can I learn enough astrophysics to make that a alternative tool for this. And so that was sort of the starting point. Yeah, it was great. I mean, I've often thought about that.

And I was thinking about that when reading your book, that it was a very fortunate experience for you, particularly, I think, to be in Rich Muller's group who had that, you know, all over the place attitude and you're able to find the right thing. As you say, a lot of students get turned off from particle physics if you're an experimentalist because it's very large group. but as a theorist, I mean, that's why I got into particle physics because it was the fundamental stuff, and I didn't need, and it's theorist that didn't need to be in a big group. But interestingly, it also shows you never where, you know, where you're going to go either. I mean, you and I share that, unlike a lot of people who do astrophysics, I think I, I was a professor of astronomy as well as physics for almost 40 years, but I never, once, I never took a single course in astronomy or astrophysics, even up to it, including my PhD,

so you never know what's going to go. but I think it's also, so go on. I remember there was a point where three of us, grad students in physics, realized that all of us were using astrophysics as a tool, and none of us had taken a course. And so we sat down together every week, and we take turns teaching each other,

going through all the different textbooks that we could find, just to see whether we could catch up on some of this stuff. So basically almost all of my astrophysics, was taught from these two other faculty, these two other grad students. Well, that's a good way to learn. I mean, most of it, and lifelong learning, most of what, I don't know if it's same for you, but I always tell people I've learned a lot more physics after my PhD than before, and I think it's true.

And it's part of lifelong learning, the process of lifelong learning in some sense is what, is what the, the, the, the, the, the, the books about as well. I think, it's also, I think, poetic that Rich Muller was not only your mention, in that way. But if you think about this book, I mean, he wrote, he started writing books like thinking and teaching a course about, you know, physics for future presidents, which is, again, a very similarly motivated idea, which is to use the techniques of science, not just to become scientifically literate, but to think like a scientist and use that in, in the public arena, which is really what, what this is all about.

No, absolutely. And I, and I, you know, mentioned a couple of points in the book, but I, but I do feel like so much of that style of thinking that I was interested in when it came to developing the course and then this book comes very clearly from what I learned from him and from his relationship with his mentor, Louis Alvarez. And so there was a very interesting, certainly a thread of, and of course, when you talk to other physicists, they all feel like that's their culture too, that, you know, this is something that I think was the implicit background of so much of how the physicists thought about the world that just wasn't usually articulated.

Yeah, no, I think it's nice you're right. Alvarez, of course, influenced you as well, as well as Mueller. He added that style as well. And yeah, and you've, so it's nice to see everyone following in their mentors, footprints in one way or another. But let's get to the book. So it is written with two collaborators who are both, both them at Berkeley. Is that right? At the time when we started the course, they were both at Berkeley.

It's based on the course that you decided. I wanted to ask what caused you to start the course and how did you, how did the three of you come together? It doesn't not be a long story, but I'm interested. It's not, as far as I can see, I didn't get that out of the book. And I'm just wondering how you found you. Yeah. No, no. I mean, what really happened was I remember, oh, I've been long thinking that would be fun to try to teach some of this material that I had been learning as a

grad student and postdoc over the years, et cetera. But I remember there's one point where I was watching our society try to make just simple practical decisions like, you know, should we raise the debt ceiling? Surprisingly, that was a question back, you know, 10, 15 years ago, too. And I was thinking that it was a little weird to see that the discussions about it had, they almost seemed like religious discussions, where they had nothing to do with the content of, you know, why would you raise a debt ceiling? what does it matter, you know, et cetera. And when you heard scientists at the lunch table talking about topics like this,

they were using all sorts of different vocabulary and different of ideas of how you think about a problem that you just weren't hearing out in the world. And I found myself thinking, where do we learn all this stuff? Since it's not taught in any physics course or biology course or chemistry course I know of, and I realized it was all taught by apprenticeship, essentially. And so that was a starting. point. I thought, well, could I try to articulate these and start to teach it? And so I, at the time, I was developing a course in physics and music. And so I started saying, could we do a course where we have,

let's say, we'll teach three ideas from physics and music and three ideas from how scientific thinking works in every class. And so we started building around that concept. And then I started thinking, you know, this stuff really should be taught by itself even. And it should be a course on this. And it's not good enough just coming as a physicist to it because, you know, you go to a faculty meeting of a physicist and they don't necessarily act any more rationally than anybody else. And so I thought, you know, we needed some other parts of the story and I want to find a social psychologist. I want to find a philosopher. And so I went out and brought in and found these to other family members, one of whom was a social psychologist who was working in the public

policy school, which is even better, because he had this other, you know, legal. policy connection, and then the philosopher was somebody who had done, you know, philosophy of science and philosophy of mind. And I, and I realize that, you know, those were really important ingredients in the story. And so that's where we started. And then I, what did you do? Do you put out a notice or you just went to the school or you had to ask people who you might talk to? You know, I just, it's always interesting to me. So the philosopher I'd found in a group that was already meeting, that was, and still meet sometimes, of, of, a bunch of scientists and artists and and theater people who meet regularly and and give little

presentations to each other what they're working on. And so he happened to come into that group. And so I started meeting with him and discussing different ideas. And this was one of them. The social psychologist of Rob McCune and the philosopher is John Campbell. The philosopher of Rob McCune was I asked around from the, because I was interested, there was a new course concept at Berkeley was developing called Big Idea Courses, where you were supposed to bring together faculty from different fields. Like, you know, you could have a musician, historian,

and a physicist talking about time, you know, the concept of time. You know, it's not that in a course. And so I thought this would be a good one for that. And I found that the person who was organizing it actually knew faculty from all across campus. And she said, you know, you should go talk to the dean of the public policy school because he really is pretty sharp about these kinds of things. And he said to me, oh, this guy would be great for this. And so that's how it started. And I just made a cold call appointment, sat down his office, described the course, and he got all excited.

It was something that he really cared about. And so the whole thing just fell together nicely. We also ended up feeling like to develop a course like this, you needed to really think through what is it you want to teach? Because it wasn't obvious. So we put a sign up saying, are you embarrassed watching our society make decisions? come help invent a course, come help save the world. And about 30 people, a graduate student's postdoc, students started showing up every

week at the end of the week on Fridays. We would meet around 4 o'clock and we'd go on past dinner, just trying to think through what would make a good vocabulary of ideas that would help people think through problems. And went off about nine months. And then we, in the end, developed, I think, like 23 concepts. And then we started asking, how would you teach them in a way that felt experiential, where people would actually remember them afterwards and see it in the world when it came up.

And that led to us teaching the course with many of these people then got involved as the teaching staff. Oh, well, you know, actually, that's impressive. Saul. I figured it, like many things, just came together and tried it out and, you know, see what worked and see it didn't

and, you know, spend a week preparing the class. But it's nice to see that there was actually more a thought that went into it. And the thought, you know, clearly comes out in the book. What I'd like to do, really the purpose of the book is, as far as I can see, and tell me if I'm paraphrasing it incorrectly, is to how to use scientific ideas to help deal with the 21st century and beyond, really. And is that a reasonable?

I think that's right. And the only thing I would add would be both personally as individuals, but also as groups that we're in and as societies that we're in. Absolutely. And we'll get to that. They need to deal with groups well. I thought there was something interesting and overlapped well, actually, with a podcast I did with someone else that will talk to. But I'd like to work through it if you don't mind and in some detail, I hope,

because the ideas are interesting and they'll be familiar to some of our listeners in certain ways. But one of the first things that you talk about, which I think is important, you know, people say, why do you want to teach people how to think like a science? What does it matter? Why do you want to teach them, think like a musician or a, and the point is, it's not the sense that science is more valuable than music or art or literature, but the fact is science works, and it works in a specific way. And then one of the first chapters you have is about how science works.

And it applies to the real world. So since you begin the book with that, I thought I'd ask you about that issue. I mean, there was an interesting period in the academic world where people were so enamored with discovering how all of our perspectives, you know, come from different historical contexts, et cetera, that I think that they were losing touch of the fact that there were also commonalities. And one of the most important commonalities is the interest in understanding that world out there that's going to go on doing its thing, no matter whether we're good or bad at

getting it or whether or not our particular cultural framing is helping us or hurting us. And that feels like it's such an important starting point that people feel like they actually share a common world out there and that we don't necessarily have good access to it. We don't necessarily understand what's going on every time. But if our jobs, if our collective jobs are partly to be effective in it, we want collectively to understand what's there in the same way that you want to know. As sometimes I say to people, to some extent you want to think of this a little bit like, to what extent is there a table? in the room, you know, that you're in the middle of the room, the dining room table, let's say, that, you know, you know, you know, there are ways in which tables are collections of, you know, mostly empty space and with some atoms, et cetera, et cetera. But when it comes down to it,

you can't walk across the room through the table. You're going to, you, you, you want to know there's a table there enough so you won't try to walk through it. And that that sense of a, there being a reality out there that's going to do its thing, no matter what we would like it to do, is, I think, a really good starting place and that you want people to feel like they want together to understand enough of that reality so they don't just make stupid mistakes or missed opportunities that we can do things in the world.

Yeah, you know, it's a good point. You don't say it in the book, I think, and that's why, you know, people often say young children are scientists. And of course, that's the whole point. You're learning that. You're learning not to touch a hot stove. You know, the very early stages of your existence

are learning that there is a real world out there and objects are sharp or hot or cold and the cliffs and other things. And we learn that effectively. The good news is that those people who don't learn effectively, I don't know, it's not good news. But I mean, the people who don't learn effectively probably don't get them live long enough to reproduce, as Charles Darwin would say. And so effectively, evolutionist and evolutionary psychology has taught us, you know,

and every individual in the process of they're growing up, at least in the early stages, as a scientist. Unfortunately, we'd beat it out of a lot of people in the interim, but but and and I think the the point which even more strongly and I think you say it but it is that that we want to learn how the universe works but we've learned but through a long history certainly the last 500 years but even longer in certain ways we've learned a set of tools that allow us to do that it's not just that we want to do that for better or worse science is and it's unambiguous it's not some value judgment science is the effective way to learn

how the universe works and how not to make mistakes in general when it comes to the physical of physical reality. And to say even more strongly, if we learn that there is some other better way to do it than we're currently doing it, we would happily switch, right? That's the point. We'd happily throw out it like yesterday's newspaper. Right. And science in some sense is it's the hunt for ways to learn about the world effectively and not to fall ourselves. And it will go along with any way that does that. Exactly.

And we'll change what we're doing if it doesn't work. That means the process of science is to find out what works and what doesn't and keep using the things that work and throw out the things that don't. It's not some ideology. It's not we have some secret handshakes. And in particular, and you say this later in the book, but it is a set of tools that's almost designed to overcome the natural tendencies of individual scientists and individual people to fool ourselves.

We all come equipped with a whole bunch of things to fool ourselves. That includes scientists. And again, as you pointed out in faculty meetings, you see that rampantly. You see it everywhere. But if you can use the tools of science, it can help filter out and avoid some of the red herrings that otherwise would be there. Exactly. And I also would like to emphasize that it's aspirational, right?

It's the scientists want to do this. It's not that they're always doing it. And they often are making mistakes of exactly the kinds that we're describing in the book. And I do it myself and the people who are teaching to do it all the time just because we're human. And we're bound to do these things. What we try is build a culture of people who at least understand these kinds of mistakes so that when you make one of these things, somebody will call you on it. Or they'll help you and say, wait, wait, wait, why, you know, don't fall into that trap. You're about to step over there.

you know, and that's what you really want. You want a group of people who help each other make the mistakes less. You know, not that we ever, we will ever be perfect at it, but we'll do it a little less. And that's allowed us to learn things about the world otherwise we never would have. A good point. And implicit in that, and you made the point, and people don't realize that science is a social activity. Because it is always the case that individual scientists get let astray. And that's okay.

And you talk about how it's possible, and we'll get to there, how it's possible. to be led astray if you're a scientist. But the great thing about science is it isn't done in an isolated way. It requires a community. It requires a community of people who are willing to openly challenge you. And we don't view that as a threat or something that shouldn't be allowed or that you're being hostile or offensive or somehow oppressive that you expect to be challenged. And it's a shame, you know, if we get an environment where people are afraid to challenge because it's really important. That challenge. means that when you're doing something, fooling yourself, someone else will help you.

It's not to attack you. It's to get to the truth. And that community is really important. Absolutely. And I think this other element of the fact that you need people who will be motivated to disagree with you. In fact, you need people who are in some sense, you know, on the other side of some team gaming. Because otherwise, it's just too easy for people to not catch things where they're falling into some, some mental trap together.

And so somebody has got to be giving you a hard time. And you actually want those people who are annoying to you in some ways because otherwise, you know, there's no way to get that rigorous an analysis of where you could have gone wrong. Exactly. You've said wrong. You really need the annoying people. And again, that's what worries me about our society right now and academic society. No, no.

I think the public, you know, I mean, this current style of. of saying, I don't want to hear anybody who is, I think is wrong, is giving, it's disarming yourself or it's giving up one of your best tools that you want those people because they'll help you. They won't be necessarily right about everything they're saying, but they will be really good at finding the places that you're wrong. And you don't want to be wrong. You want to actually work well in the world with, you know, with some understanding of it. Yeah. And I again, we said this before, but I think you can't emphasize too much that this notion of, I think, from David Hume originally, and I learned her from

Christians, but that freedom of speech isn't the freedom. The important part of freedom of speech is not the freedom to say something, but if you give it up, you've lost the freedom to learn that you're wrong. And that's really what's important. And Jonathan Roach, who's done we did a podcast with a wonderful writer and journalist and in his own right philosopher, I think really made that clear as to me first,

that science requires a dialectic. Science as an activity cannot progress without a dialectic, without that constant give and take, with the constant battering of every idea, because only by battering ideas, will the good ones survive? Anyway, well, okay, I think we've gone enough. Let's talk about some of the good ideas and some of the bad ones. The next thing you go into,

which is a really important understanding, but also a really important red herring, is to do with correlation versus. causation. And you have a chapter about that. And that's probably one of the biggest mistakes many of us make. You do something and, you know, you think it's easy, especially when you want there to be a cause, but it's easy to notice that something happens when something else happens and to ascribe causation. And one of the biggest challenge, one of the biggest red herrings, one of the biggest mistakes one can make is to assume a cause that isn't there and also to learn how to tell

the difference between correlation causation. So I turn it over. to you. No, exactly. And I think that probably maybe the classic example that really brings us home is that if somebody comes to you and says they can cure your disease, if you're not well, you're highly motivated to want to believe that what they're offering you, it will cure the disease. And they often are highly motivated because they happen to have taken whatever this thing was or they did whatever it was, and they got better. And they're, really excited that they have the possibility of helping you in that way. So it's the perfect meeting of two people who both want to believe that this thing is going to work and throw into

that the fact that sometimes placebo effect even means that if you believe it, it'll help. So all these are the, it's like the perfect storm for places where you will find, you will assume that the causal connection is established. Just because, you know, somebody took this thing and they got better, you should take it, you will get better. And so that's one of these places where I think little by little, people eventually realize that they have to start using other ways to tell which drugs are actually helpful and which ones are what snake oil salesmen or even well-meaning people who just have the wrong understanding of what was causing what. This led to the current practice of using, you know,

randomized controlled trials. And so we so begin there as describing it as becoming like a paradigm of if you can, that would be the thing you always would love to do. You'd love to be able to say, we'll take, you know, just this one variable and we'll change this one, and we'll just see whether that one variable, when we change it, leaving everything else constant, does it change the outcome? But of course, we then go on to point out that you can't really do randomized control trials for many things.

it turns out you can do it for drugs, but it's very slow process. And so it's not always the easiest way to get an answer if you're in a hurry, like if somebody's going to die. But for many things like our field, cosmology, we have very few chances to do randomized controlled trials with galaxies and the universe. The universe, yeah. We don't yet have an ability to try things with different universes to see what works and what doesn't. So we've had to just invent other ways to capture some of that same

ability to recognize what things are causal and what things are just happen to correlate with each other. And there are a lot of strange correlation. I'll tell you about one, which relates to actually an important physics thing. But you hit on something that resonated with me too, that one of my favorite books of Carl Sagan

was a demon-haunted world, which is a science has a brief candle in the dark. And any point, speaking of that wanting to cure you, he did point out about, I mean, an organized version, of this, which is the Catholic Church and the miracle at lords, where it's an immorical example. People go to lords to be cured, and 130 million pilgrims have gone to Lord, and the Catholic

Church keeps careful notes. And there was something like 27, you know, cures that they could not explain otherwise. And, of course, as he points out, no one regenerated an arm, but there were cures from cancer and other things. And then you look at the background dating, you find out that, sure, cancer is often going to spontaneous remission. And in fact, in the background population, the rate of spontaneous remission was greater than that of people who went to Lourdes. But if you went to Lourdes and you were cured of cancer, there's no way I could ever convince you that it wasn't that miracle.

Absolutely. Yeah. It's a wonderful example of what Fox Muldar said in the X-Files. We want to believe. And we have to realize, this is one of those human traits that you talk about in the book, too. But we have to realize we all want to believe. And it comes up again and again in the book. And so what science teaches us to say, okay, we just let's recognize we want to believe, and let's find some external tools that can convince ourselves that maybe we're wrong, which is the hardest thing to do. But the correlation that, you know, I remember when I worked on solar neutrinos for a long time because it was a big mystery, and Ray Davis, who was a wonderful experimentalist, an old friend,

and who won the Nobel Prize for discovering an amazing experiment that first detected neutrinos from the sun, which are the result of the processes inside the sun that power the sun. But he found lots of interesting correlations between that neutrino signal and other things, and I remember spending a fair amount of time looking at those statistics. And the question with something is correlated to something else, you have to ask yourself, is there a physical mechanism that might suggest that that correlation is reasonable? One of the most amusing correlations that you could find, and he didn't describe any significance to it, but it turns out for many years, his, the number of neuterner,

in that experiment was correlated to the stock market. And there's an example where it's clearly you can't see a physical correlation, but sometimes it's not so obvious. So one of the ways out of that trap is to say, if there's a correlation, can I think of a plausible physical mechanism that could provide that correlation? If there isn't, it's probably spurious. That's right. And I think that one of the things that we also discussed in the book, Alscore probably,

that as scientists, you often start to learn is how often you see things that are just random that look to us as if they're significant. And that, you know, I don't think most of us have at all a sense for, you know, you flip the coin, how often would you see long runs of heads or tails? It's just not saying that our brains are well calibrated at. And so we're constantly seeing things that we think, oh, that looks interesting. and it turns out that, you know, lots of it is just random. If you flip the coin, that's what you would see.

You know, and that is probably one of the biggest takeaways for, you know, young scientists is little by little learning that you always have to compare anything you get excited by to what you would have seen if you just did a random version of it. Yeah, in fact, it's one of your chapters. We're going to get there. We're going to get there. And there's a lot of some examples that I'd be like. And one I want to question you by it from your own experience.

not the one you think I'm going to actually perhaps maybe. You mentioned one regarding pulsars and we'll talk about that. That's also other things. But the next chapter is about, it deals with, I think, one of the most important unsung values of science. And it doesn't sound like it. It sounds like a constraint or a failing of science. And that is to do with uncertainty.

I think I want to read a paragraph, if you don't mind from your book. Science offers us a right. radically different way to think about our connections to reality, this reality that we know something, but not everything about. It allows us to shift from a stance that says we can only work with things that we're absolutely certain of, to a stance that says we're actually more successful if we can work with things that we have a varying degrees of confidence in. Furthermore, just understanding the concept that confidence comes in degrees can be much more powerful than holding out for definitive answers in a world where available evidence usually fails to

give us an absolute certainty we'd like. And I think that's the point that the recognizing that everything is uncertain, even in science, every single measure you make is uncertain, is not a failing. It's not a bug. It's a feature of the world. And it also, as you point out, involves another key aspect that Richard Feynman, I'll quote him a number of times in our discussion, talked about, which is scientific honesty, the willingness to say, you know, I don't know for sure.

an honesty that you wish politicians would have, and sometimes parents and sometimes teachers. So anyway, uncertainty, your turn. Yeah, no, I think you've captured the starting point really well. And I think that, you know, in some sense, we disarm ourselves. If, you know, and we were just working with, you know, if you just worked with black and white, and that was all you had available to you, you know,

the world would just be a lot less interesting place and you know a lot less about the world because not everything is black or white. And in fact, almost everything is not. And so you really want to be able to work with a full palette of what the experience of the world is like, where some things you know very strongly and other things you know rather weakly. And that is real information. That's actually allowing you to do things in the world. And otherwise you're just bumbling along, you know, only seeing things when they happen to be those two colors.

Absolutely. I think it's the realization of the real world, it's not just science. I mean, we all realize the real world is uncertain. But I don't think many people realize that scientists realize that everything they're doing is uncertain. And to be able to say, to be able to say, not only I don't know, but I'm not certain, is essential. And one of the things that science does that maybe the rest of society doesn't is that is that, is that, being able to say how confident you are quantitatively. Yeah. It's incredibly important. And so we don't we don't do that. But one of the things science does is let's quantify our uncertainty to say, okay, I'm 90% certain and that really means something. That's just not something I invent, oh, I'm 90% certain that you're, you know,

mean, you know, or something. But, but we actually work hard to find a way. So we know if we did this experiment 100 times, you know, I expect that 90% of the time it would come up with what I'm thinking, and sometimes are wrong, of course, but quantifying uncertainty, not just recognizing that exists is incredibly important. No, and it also allows for a few different things. For example, you can now be a very rigorous and proud person about your ability to recognize roughly what the certainty is, not about being right or wrong. And you don't want to be married to being right or wrong because

then you don't have the opportunity to change, to be open to learning new things. You want to be married to having a pretty healthy guess as to, you know, where your confidence is. And so that you will be proud when you discover that, you know, one out of ten of your papers where you said you're 90% sure about something, that those one out of ten better be wrong or you're not doing your job well. And that's good. I mean, to be able to be proud that some fraction that what you did was wrong is actually allows you a, whole different kind of freedom and ego investment in a much better place than being right. Absolutely.

And that's a really good point. And actually it'll lead to where we're going to go next in a sense. But having that confidence also, one of the words, I don't know how you feel about this, but people always say, do you believe this or do you believe that? I say in science, I don't think, and I actually don't think anywhere should we use the word believe. The way science works is you say, this is likely or unlikely. And this is very likely or very unlikely. But believing is irrelevant.

What matters is, is it likely be true or is it unlikely to be true? And if it's so likely be true, you might use the word believe. And you and I probably use it even though I try not to. But that's the key point. It's likely or unlikely. That's all science can never really say. It can never say it's impossible. Well, maybe that's too philosophical question to go into here.

But in general, it's something. think is either very, like the sun is going to rise tomorrow is very, very likely. I mean, it's possible that it might not, but it's so likely that we say, I believe the sun's going to rise tomorrow. So that level. Right. And I say to people that, you know, that just because you're using probabilistic language doesn't mean that you can't be pretty sure about some things. I mean, the some things, you know, you give it 99.99% and you will bet your life on them, right, which is unusual. It's literally true, right? You know, we will get on an airplane and, we bet our life on the idea that these, you know, these many tons of metal are going to fly.

That, you know, isn't a obvious thing that you should be able to do. And yet, you know, we, we will cheerfully do it almost every day, you know, but that's very different than saying that it is guaranteed, you know, that this is absolutely true. And the next chapter you talk about is when scientists do get, get, because they're human, one of the secrets, that scientists are human, one of the hidden secrets about science is that is overconfidence. And, you know, we all tend to be overconfident in the things we believe in. And scientists can be too, and you often see that. And so when scientists are wrong, they're not penalized so much if they're wrong,

and they say they weren't certain. When they're penalized, they say, we know this happens. And it turns out to me not the case. And science, and the whole point in some sense, I think of what you're getting at is that science gives us tools to try and overconfidence. our natural tendency to be overconfident. Yes, yes. And in fact, actually, it's interesting that we tend to be overconfident at the top end

and a little bit underconfident at the bottom end. So, you know, sometimes our gut feelings, when we really feel like we don't know, sometimes our gut feelings are not bad so that, you know, really we should have 65% confidence, not 50% could be right, could be wrong, you know, on something. But these are all things that apparently people can be calibrated. They can be better trained at understanding that little better. And then, of course, we try to develop all sorts of statistical techniques to make it easier to just come up with numbers with just algorithms without having to make.

We'll talk about orders magnitude or understanding, which of course is, I wrote a whole book about it on my physics book in some sense. But yeah, we've developed tools to try and get some confidence, even we don't know very much, but at least a set of tools that anyone can use to at least get from saying, I have no idea, to saying, well, maybe it's around here. And I think that's really, and we'll get there. I think you do a great job in the book talking about that. But the next chapter is about signal versus noise.

And it leads us to the question of looking, of the examples of seeing patterns that weren't there. And let me. And so the, and I think it's important to realize that, as you say, that signal is something you're looking for, and noise is something that gets in the way. But that's sometimes a very subjective thing. One person's signal could be another person's noise.

And the best example I know, I can give two examples, both of which involved the Nobel Prize, one of which is yours. You know, my friend Frank Wilczek won the Nobel Prize for developing a theory of the strong interaction with colleagues. And it's called quantum chromodynamics. And then you talk about the discovery of the Higgs later on, And he always is amused by the fact that the predictions that led them to win the Nobel Prize are now the noise of what you, because you can use their theory to calculate, so that the noise you want to get rid of if you're looking for the Higgs particle.

Similarly, the discovery that the universe is accelerating, the supernova is important. But now in some sense, we use that as a background to try and look for deviations from that to try and find something new. And so signal in noise is very subjective. But nevertheless, the ability to distinguish signal from noise and know when one is, when you're, actually seeing a signal that exists instead of one that you want to exist is important. And in the book, you give an example of your own from pulsar. So why don't you just talk about that a little bit? And then I want to talk about another example.

Okay. Talk about pulsars. Your experience as a young person. Yeah, so the pulsar one is supposed to show you a case in which, and it was, you know, I was reviewing a story in which it's so clear that random noise can mimic a, what looks like a very clear signal. And in this particular case, we were hunting in the remnants of an exploding star, a supernova,

that was one of the few that was close enough that you could actually go and see them by eye, in fact. This was in the southern... Since Kepler, maybe, or somewhere like that, yes. Yeah, yeah, no, it's like the first in 300 years, right? And so we rushed down to the southern hemisphere where it was visible and started to see whether we could hunt using infrared light into the cloud of debris and find we thought there could be the

birth of a new pulsating star. They're called pulsars. A neutron star that spins and produces basically a beam. Exactly. Everything goes past you, you see a bright light, you know. But it could be thousands of times per second. These things are very fast. So you do this with electronics to try find it. And we, surprisingly enough, found something that looked like a really interesting example of a pulsar. And it was doing a kind of odd drifting in that frequency, that thousand times per second, it was like almost 2000. And it was so slowing down in a sort of randomish-looking way, which could happen. You know, these things can be slowing. But we really, that actually if you're going to make that measurement, you have to take into account the fact that

things will look like they're slowing more or less, depending on whether you're moving towards it or away from it. This is the famous Doppler effect when you hear a car horn that sounds high when it's coming towards you and low when it goes past you away from you. And so we had to make sure that we weren't having a Doppler effect that was confusing this particular drifting frequency. And in particular, when we move, when the earth is spinning, you know, in its, in its axis, you'll be moving towards it when, you know, sometime and away from it the other time of day. And as we go around the sun, there's also a change in whether you're moving towards it or away from it. And so we

had to correct for those, and we took those out. And shockingly, when we took those two effects out, what had been sort of a random looking drift turned into a perfect sine wave. I mean, it looked almost too good to be true. And that shocked us and it made us think, wow, you know, that's what you would get if there was a planet orbiting around this pulsar and it was slightly tugging it to and fro. Years later, that's how people actually found exoplanets. But that was the first time that we thought maybe we saw a planet. So we rushed to publish a paper in nature about this. And then we went back down the next time that next season when the, that particular spot in the sky was visible as it came out from behind the sun, you know, over the course of the

year. And we started looking for it again to see whether it had slowed down more and because pulsars do slow down and what was going on. And we couldn't find it at first. And then we found it. And then it went away. And we started getting a little suspicious about what was going on. And then we realized that we found it every time an instrument across the other side of the telescope dome was on. And apparently it was no pulsar. It was just a radio interference from this other instrument that was leaking radio waves. Weirdly enough, in this absolutely perfect form where if you happen to correct that random drift that it happened to have for the Earth's motion, this is completely random, you end up getting a perfect sine wave.

Now, that seems really unfair, I mean, you know, that the world would trick you that way. But it definitely reminds us that we will see perfect sign waves if they, if something is going to appear, and they can be formed randomly, just like getting a whole bunch of heads in a row, when you flip coins can happen randomly. And we learned our lesson. We had to retract the paper. And we sent a note to nature and retracted it. And for all my years afterwards,

I was that much more aware that just because you see something that has got to be real, doesn't mean it's necessary real. That you have to do. It was a great experience for you. Yeah. And it was a good experience of Hassan's done because it's all so honest. Hey, we thought this was the case. you know what we checked we checked and we'll get to that the whole point of checking and why you

check and it wasn't the case and the fact you were tracked it that's good science not bad science that's good science and and by the way i won't spend time now but when i was reading the book i think i figured out why your instrument would give you the sine wave why you think it did when you extract that we have so i'll give me my i think i worked it out so that's a different thing as a theorist but i now want to turn to another case which i'll say frank by the way, I want to ask you his question, but if you want to later on remove it from our discussion, we can. But I was intrigued by this. And, you know, the result that you were, obviously, when you then discovered the universe was accelerating, that was another thing that's too good to be true.

And you had to worry about a lot about whether it was wrong or not. But, you know, that's something, that's an area where, actually, you and I have had experience together. I remember, I vividly remember my three weeks or four weeks at Berkeley Labs when I was going in 1995 or six when I was going around saying, you know what, I think there's a cosmological and I remember you coming up to me after my seminar and saying, we are going to prove you wrong. We're doing the experiment and we're going to prove you wrong. And of course, later you proved this right. But the more important thing is, and I want to ask you about this, if you don't mind me asking

you about this, is first you did prove me wrong. In 1997, your group came up with a result, which said that it wasn't there. And then later on came up with a result that said it was. And I think if you don't mind going through that, I want to know what was the change and why were you willing to make it? And if you don't mind chatting about it. Do you mind? No, of course.

The very first time that we had just enough data to begin to actually make a measurement of what we thought was slowing of the expansion of the universe, was in 97. And we had, I'm trying to remember now it was five particularly good supernova, maybe seven altogether. If I remember, we were something like that. And we said, okay, already at that point, we were getting to the point where we developed a technique that was finding batches

and batches of supernova. So we were already up to like 25 or 30. and we were, you know, and then by the time we actually did the final analysis, we had 42 supernova altogether. But with those first, you know, five or seven, you know, at a starting point, we thought, let's test all the steps. Let's make sure that we can go all the way from this measurement of an exploding stars, brightening and fading away, all the way to a measurement of how that tells you about the expansion history of the universe from those few supernova. And we went through the entire paper to show how the techniques work. What we found was a big arrow bar as we knew we would.

We knew we were going to need at least like 30 supernova to make a real useful measurement. But already the first one looked suggested because it was centered around the, what you would expect if the universe had no cosmological constant, if it was actually, you know, slowing it appropriately. And it had big error bars and it had even bigger systematic error bars. But we said, you know, this sure looks like we're going to come in with a result that would prove you wrong in terms of the possibility of the cosmogical constant. We, I'm glad that at the end of the paper, we did say something like, you know, however, the arrow bars are still big. we still have another 25 or 30, whatever it was in the can at the point, we will be able to give you a more definitive answer coming up.

So that was the starting point. And then, of course, when we did the full analysis, not only did the error bars dramatically shrink, as they should when you have a lot more. But they also shifted over to where it was. And that was why we were really struck at the beginning of the, just the following year when we had this new plot and we thought, well, something must be wrong. It's, you know, we're getting an answer that doesn't make much sense unless you happen to be, you know, Lawrence Krause. And, and so, you know, we knew that we were going to have to do a lot of work checking every one of the steps and figuring out, you know, because we just put the whole thing

together and we need to make sure that, you know, everyone was calibrated well and every, and the more we did, the more it didn't go away. And the more actually things sharpened, and that was when we started to believe it over the course of probably seven or eight months of analysis before we started to believe that you were right, you know, at the end of the day. So that was the, that's the fast version. There's one extra element of the story, which I like to say sometimes, which is that going back afterwards, years later, I went back to look at, you know, could I at all understand, you know, whether it was just random that the center happened to be,

you know, where it was, or was there anything else going on? And I've come to the conclusion since then that one of the supernova would have pulled things a little bit the other way, but we analyzed it and analyzed it until it didn't. Now, it didn't, you know, I don't think by itself, it wouldn't have changed the whole story. Yeah, it would just broaden the uncertainty to some extent. Yeah, it would have broadened the uncertainties a little bit,

and it would have probably moved the center a little bit away. But it would still have overlapped with zero, which would have been your preference. Exactly. And so I don't think it would have changed how we would have said things much. But in retrospect, I looked at it. And this was right after I had done an analysis of one of our rival teams data. And I had discovered that they had done some serious mistakes.

This was some Hubble Space Telescope data. And I found that they had done some serious mistake in their analysis that actually should have shifted the result dramatically. And I thought, well, why didn't it? And it turned out that it didn't because it was exactly canceled by a change in the Hubble Space Telescope's calibration over that, that came out around that time. And it made me suspicious. And I thought, well, you know what? I bet that they analyzed things up to the point that they got the answer they expected, and then they stopped analyzing. And I was feeling very smug about this until I went back and I looked at this other one and I realized, you know what?

I think we did the same thing on that one supernova out of that small set of, you know, five, seven that we had. And from then on, I don't believe any results that we do unless we do them in this technique where you blind your analysis, that you don't let yourself know what the answer is until you've done all the checks and you agree that you're going to publish with what you've got. And then you allow yourself to see the answer. Because I don't trust myself to not do that, to get that right. When have we decided that we've done enough analysis, let's publish, and not to be affected by what I expect or would like the answer to be. Well, actually, I'm glad to ask the question.

That's again another great example of how science is done, not just individual scientists and our tendency to stop asking questions when we get what we want, but also the fact that, hey, you know, you look at data and uncertainty and uncertainties really do mean something. It may be centered on the value you want, but there's a wide uncertainty. And, you know, when you continue to the experiment and you reduce the air bars, it could become different. That's what uncertainty means.

Yeah, sure, I'd like you to be zero, but it could be not zero. And you know what? It's not zero. And, you know, election is going to go a certain way, and it goes a different way you thought, no matter what this. And we all had a dramatic example that occurred to us, you know, in the elections in which we thought the polls were saying one thing. But what they really were saying is that there's a one-third chance that it could go the other way.

Yeah, absolutely. And we got the one-third chance. Yeah, people don't realize it's 70% is, 67%. That's how it was going in exactly the same direction. It means one out of three times you have an election. You're going to get wrong. Before we leave, I just want to mention, because I think I hadn't occurred to me until since we brought up Joe Taylor, a wonderful scientist who did win the Nobel Prize for discovering gravitational waves as a result of Powell SARS. And it's worthwhile pointing out that the techniques he did

to discover the gravitational waves were exactly the ones you tried to do to remove the noise. Very, I mean, an amazing analysis, not just remove that because he was trying to look at things at one part and 10 to the 15th or so. But, you know, removing the motion of the earth around the sun and the sun around the galaxy and every single thing that could get in the way of the actual signal. And it was only by doing that that he was able to uncover this incredibly small signal. When you move on from that to searching, again, for patterns are not there, which in some sense we talked about, but the fact that if you saw random, and you give a great example of the book,

which I like of two random patterns, one of which most people will say is random. random and it's not the one that really is. Because no one thinks a random pattern is going to have seven heads in a row, of course. But of course, it's guaranteed to if you keep, if you keep, you know, hitting the dice enough times, you'll get, or a point enough times, you'll get seven heads in a row. And that's going to happen. It won't, it's not going to happen a lot. And most, unfortunately, most people who gamble think when they've gotten six that they're more

likely to get the seventh and then that's where they lose all their money. But it does hit at one of these evolutionary psychology things that we all want to believe for, we all want to look for patterns. And I think one of the arguments is that we do it because it's helped save our lives. That in the early times, in the savannah, when our ancient ancestors were hominids were developing, you know, the trees could rustle and you can assume that's nothing, or it's a lion. and the ones who maybe assumed there was something there, even if there wasn't, were the ones who survived.

So we tend to have this teleology. We always look for purpose and something there when it may not be there. And we find significance in something that may not be significant. And I think we all have this experience of anything that happens to us is suddenly significant, even if it's just an accident. And it's really hard to convince someone. That example of the Lord's case is another example. But if something's an accident, if it seems weird, you think it should be significant.

and scientists, and I know many examples, I've seen it in my own work, where experimentalists find something so interesting, they're not willing to realize it's an accident, and then sometimes publish something without doing the adequate checks. Feynman was a big, was big in terms of often finding out their errors. And he often gave the example, you know, you have a, you have a dream one night that your friend is going to break their leg, and then they wake up and you find broke their arm and you think, wow, but you don't remember the 10,000 dreams you had, you know, all these other times that were nonsense. So we have gotten a technique, and you talked about it with the Higgs example, to overcome that, besides the blind search, which we'll talk

about a second, to overcome that effort to find patterns that aren't there or look for things. And I thought your discussion, the Higgs was good. Do you want it, do you want to, do you mind giving it? Oh, just that we were pointing out that when you ask a question of data, if you have a very specific question you ask, it's reasonably easy to then try to figure out, well, how often would the answer come out to be what you see if it was just random noids. But what you don't always recognize is the fact that you may be asking lots and lots of different versions of the same question. and each one, there was a chance that you could have gotten random noise creating it. So the physicists learned over time that they had to basically divide down the probability that the sign was right based on how many questions they asked. Because each one could be another source of a random hit, you know, a random pattern.

So that's called the look elsewhere effect by the physicists. It's called multiple comparisons and multiple questions by other fields who come across it. But it's a key thing that we, in our current world, you have to be aware of because we're getting so many opportunities to be asked questions of many, many questions of the same data because so many people are on the internet looking at the same results. And so you will end up getting many, many times a random, what looks like a pattern just because so many people

have asked something of the same data and this wasn't used to be the case that it's been harder to do that so that's become even more of an issue in our current world and okay and then another issue that's big in our current world and relevant perhaps more obviously

well relevant in physics but in other areas that you talk about and I like the fact that of course the book eventually moves into because the point is to talk about not just science but how to apply it in society, and you begin to see it more and more in the book. But the question of false positives versus false negatives, you can be wrong a number of different ways. You can be wrong in saying there's something that isn't there, or you can be wrong by saying there isn't there something when there is, and they both can have consequences, and sometimes

one is more important than the other. I like that discussion. You want to elaborate on that? Yeah, no, and of course, in some sense, we always have to choose our thresholds of, you know, for this particular purpose, are we happier of being wrong, some of the, you know, a higher probability of being wrong by missing a signal? Or are we happy, is it more important to us that we catch a signal, even if that means we'll get some false alarms?

And, you know, we've, we all have a lot of that experience of having to make that decision in the recent pandemic, where, you know, you had to make, all the time, decide, you know, am I going to be absolutely sure before? I go to something that nobody has COVID, you know, 19, or, or is it more important that I, you know, that I, in this period of case, don't get triggered by a false alarm? And, you know, and then, and I think it's, you know, it depends on case by case. And, and, and, and, and I think, you know, the interesting thing, I guess as an experimentalist, I'll ask you is, because, you know, it's, I think it's far worse, the,

sense I get as a scientist that it's you feel worse when people have a false positive when they claim something is there that isn't there than when they don't see something that is there. But if you're the scientist, what you're always worrying about is missing something that is there and having someone else scoop you. So I think there's these keep, you know, if you're actually to work, you're more worried about the false negatives. But actually if you're publishing, you've got to worry more about the false positives, right? Yeah, this is very natural tension, you know, and, you know, and you can see it sort of like in a, in a, in a, job talks, you know, when somebody is going to give a talk at a university, and if they don't

have an exciting result, then they may not get a job. But if they have an exact result that turns out to be wrong, they could lose the job here. Yeah. So it's, but there is that tendency to want to do it, you know, and I think, again, learning how to balance those things as part of the science and learning, not just learning psychologically, but using tools that have been discovered by others, by the tools of science for how to avoid those things is the key point. It's not as if we internally can cure ourselves of our natural tendencies. We just say, well, there's a methodology, and this methodology works. So let's use that methodology.

Absolutely. And also it comes along with a natural way to communicate the degree of certainty and uncertainty so that you can be, if your job is to be completely as honest as possible and to point out all the things that could be wrong, then everybody can then decide, do they accept this? or not. And if it turns out to be wrong, it's fair game that you know, that they've, you've been honest. And I think that's the thing that people really look for. Yeah, absolutely. Now, yeah, there's a next thing you talk about, which I really enjoyed and thought is,

is scientific optimism, which I think is really important to, scientists are innately optimistic in a way. And one reason for that is simple. And it's true of anyone, I think, in a difference that's, if you're going to spend 20 years of your life on something, you've got to have a a reason to think it's going to work. And so scientists are innately optimistic that what they're going to work on is going to work, even when there may be no evidence of it. And so I was amused that you point out, well, we just talked to you about how you can fool yourself, but you kind of need to fool yourself a little bit if you're going to be motivated.

I found that fascinating. You want to expand on the little bit? We'll talk a little bit more. I think one of the things that is a danger of teaching a course like this is that you can get everybody to be complete cynics, you know, and skeptics of everything, you know, to the point that it's just you say, well, yeah, how do I believe this? You know, that could be wrong, that could be wrong, you know, et cetera. But that's not, in some sense, it's like driving a car, I think we give the metaphor, driving a car with just brakes to make sure you don't make a mistake. But there's,

but you can't drive a car with just brakes. You need an accelerator pedal. And the accelerator pedal, I think one of the accelerator pedals for science is this, it's a culture. of willingness to believe that you can solve a problem, long enough to solve a problem. And generally, you know, I think we tend to, we can make error of sticking to a problem too long that's just not ready to be solved. But it's much more common that humans make the other error of they get frustrated after, you know, the first couple hours on a problem, let alone the first couple months on a problem. And a really good, interesting problem, you know, worth it salt, could take years.

And science has... For decades. Culture allows people to stick to something for years. Absolutely. And being willing to do that, that's right, because the hard problems are not easier to solve. One of my biggest, I think I said in my physics and Star Trek book that the biggest mistake they make is, you know,

every time there's a problem on the enterprise that's solved within two hours, the engineers can solve it. And that's one of the people get this. And it was, I think, frankly, a big problem with the pandemic, that people thought, well, science is there. Why can't we solve the problem in a month when, in fact, you know, if you don't know what the disease is, you don't know what the, you have to, the fact that you could even do it within a year is frankly remarkable. I mean, that was amazing.

A tune from TV to thinking, well, the scientists should be able to solve this and know the answer. And part of the problem was unfortunate. I think the scientists who got up and said we know the answer when we don't. It takes time and hard problems are difficult to solve. And the pandemic is still, we still don't understand a lot about it. And we're going to, I thought it was a wonderful chance for the public to learn how science worked. and instead it turned out to be often quite the opposite, a sense that science fails and not really being the good teaching lesson it could have been.

I agree. And I feel sympathy for the people who are in the position of having to communicate this, that I think they were really scared that they would do it wrong and that people would die. And so easy, you know, in second, you know, for me to sit back afterwards and say, oh, what they were what they said was this or that. But now that we've done this once, I hope that next time around that we really do try to take advantage of this whole style of thinking that we're able to explain to people that this isn't like, oh, you know, a situation where we know the answer and we just do it and we're done. You know, this is much more like playing a sports game, you know, a football game against the virus. Yeah.

And it's, you know, you don't come up with a play and then you just use that play for the rest of the game, for the rest of the game. you know, it's hours or three hours of the game, right? You are going to have to watch what the virus is doing. You're going to have to try and come up with another play. You're going to see what is it doing. Is it changing? Is it coming up with a different strategy? And then we'll change our strategy.

And we don't know what strategies are and we're going to have to try to figure them out because then we're going to have to come up with that. And people, I think, would understand that metaphor. And they would recognize the fact that they should stay tuned, that, you know, they want to be there when the coach tells them our next play this week is going to be this. You know, and they want to be in the game. They don't want to be, you know, just, you know, basing it on whatever.

The first, the first strategy was from the very first huddle, you know. So I think that could have been done, but I understand why it wasn't. And I'm hoping next time around we'll be able to take advantage of that knowledge and do better. I hope so. One always hopes that we can learn. And on the other hand, as Mark Twain said, history may not repeat itself, but it sure rhymes a lot. Anyway, but there's two other. reasons for optimism, which I don't want to leave for a moment, because you bring out one,

which is the fact that science often enlarges the pie, as you point out. And there are lots of examples. But I remember, for example, when I was a kid, I was strongly influenced by a book called Limits to Growth, which, you know, basically said, you know, there are fundamental limits on population and food and everything else. And based on extrapolating what was there, it was a perfectly good analysis. But often, science changes the rules or changes what's possible. And therefore, you know, extrapolating into the future is sometimes a little difficult in that regard, because by being optimistic that you might be able to change the rules and might be able to do better at producing food or better at curing diseases or better at generating

energy, you can change. So it's very dangerous to always extrapolate for the present. So that sort of motivates that kind of optimism. And you pointed that out. No, and I think that's, that is a key thing that, that science offers that, you know, the people have not internalized often, you know, that, that you, we have learned that things can change and things can be solved, that you may never have thought they could be. And that it's important to have that in mind so that you don't limit your options too early and that you, and that you're open to the possibility. and in particular in places where you have different people's interests pitted against each other. And it often feels like everything's a zero-sum game and that if you win, I lose and if that group is doing better, this we're doing poor. I mean, you know, you even see it in the economics discussions of what's going on when China is doing well, that somehow that's bad for the United States. Whereas in fact, it's probably good for the United States, you know, if China is doing well.

And just we seem to have lost sight of the fact that that's one of the biggest lessons of history and of science, you know, that often we end up being able to make something possible for everybody to do much, much better. And that's not a question of you when I lose. Yeah, and that's great. And by changing the pie, if it's always, if it's a zero-sum game, then it's a zero-sum game. But with science, it isn't always a zero-sum game. you change, you know, add variables. And I think an important example is climate.

You know, I read a book called The Physics of Climate Change. And I tried just to talk about the physics, not about policy, because I wanted to reach people who were naturally skeptical of some policies that people would suggest. But one of the things that I tried to end with, and I was influenced to some extent by a friend of mine, Dan Shrague, who's a climate scientist at Harvard, but is that, you know, it's natural to be doom in. gloom because if you make projections at present time, it is pretty worrisome.

But we need to realize that the real problem of climate change is probably not technological, that there's going to be technologies that are going to be developed to do things that we can't do now. The real problem is going to be political or sociological. But I have great faith, you know, while I view climate problems as severe and what can result as severe, and it's just basic physics to realize that those things can be severe. There's no politics involved as fundamental science, but we'll be able to develop technologies that are going to address some of the things that now seem really problematic. And I think that that's important

to remember. Otherwise, it just seems like such a hopeless problem. We might as well just give up now. I mean, I think one of the things that I've been so concerned about is that people are not recognizing how much capability they have, right? That we actually, I think, have learned, even in just our own lifetimes, how much we are able to change the game of feeding the planet and for that matter, changing the climate.

You know, the fact that things that we do can have enough of a scale effect that they can change actual climate, it's clearly been a problem that we're all facing, but it also comes along with the fact that we actually have the capability. We have tools that are much more powerful than we would have thought. And the biggest problem we have, I think is exactly what you're saying, is that we aren't getting people into that thoughtful discussion with each other about how do we,

what do we want to do together and how do we want to make things, you know, what do we want to solve and how do we want to make things work? If even we were a little bit more on that same page, and of course, that's one of the whole points of writing this book I was thinking, just being a little more on the same page gives us a i think we would not be worried about many of these things um they would be tough and we'd have to work hard at them but i think we would have the we we have the capabilities now in a way that we never knew we did and that would give us the will maybe to do it and the real will to realize that you know it's not a zero-sum game and it's not it's not you when i lose in when it comes to climate and addressing it there's another i i'm i'm continuing on this because i think there's

I think these are valuable lessons, so I hope you don't mind if I dwell on some of the things you brought up. But there's another aspect, which is that there's another reason to be optimistic because, and I don't think you stressed it in the book, but I think it's important, is that not solving problems is also a good thing in science. But it's a good thing in business, too. I created when I was chair of a department, a program in physics entrepreneurship, which the head of our business school called an oxymoron, but it's not. because often if you're optimistic and you work hard, we used to tell our students,

I remember in that program that one of the things about physics is you learn how to solve problems when you don't know what the problem is. And namely, if you use it, it's like the techniques that you use an experimentalist, you learn them and you may plug all the way to try and do an automated study of X and it leads nowhere.

But that tool that you've used is going to help you solve another problem. So the optimism often causes you to work hard on something that later on helps you solve a problem you never even knew you were going to solve. And it's true in the business world as well. Now, it's a very good point, which we did not actually get in the book. You're right. And I think this idea that so much of what we're doing is training ourselves so that we'll be able to do something else. Yeah, exactly.

So it's rarely a waste of time to work hard on something. I mean, rarely. There's almost always a benefit, even if you have no idea what the benefit is going to be. And, of course, that's a great serendipity of science, the great discovery. And it's one of the reasons why I think it's so powerful to have people motivated by the excitement of learning and something about the world, that you bring people in to do something that, you know, it's never going to be, I don't think it'll ever be practical for us to really have a deep understanding of the Big Bang. You know, it's, I mean, maybe it'll tell us something deep about the physics that we can then use for something else. But to first order, it's a reasonable bet that most of it won't ever show up in our world. And yet, it ends up being one of the best ways to focus people's minds and invent all sorts of tools that then really are practically useful in the world.

Tools both for analysis, but also tools of sensors and techniques. And so it's just something that will focus the brains of a lot of smart people because they're excited by it is as valuable as necessary. the practical problem that they might be focused on. Well, and often, and often the great solution to practical problems comes from people who were just interested in the puzzle. And, you know, the history of, from antibiotics to even transistors. I mean, if we had, if someone gave Pell Labs a thing and said, you know, in 1940s, make better computers, they'd have wheels and pulleys and instead of, you know, transistor.

There's so many examples of it. I love also thinking about how completely impractical general relativity or, you know, you know, what about, you know, the clock's traveling near the speed of light, you know, it just doesn't seem like, you know, that ever was something that we're going to have to worry about. And yet, you know, GPS is exactly. And I remember writing about that. Yeah, GPS, I try and give an example of how esoteric things could be. And I remember, I don't know whether you ever took a course at Harvard.

He was a colleague in mine when I said Harvard, one of my favorite ones from Ed Purcell, one of the most lovely scientists I've ever known, won the Nobel Prize for developing what is now nuclear magnetic resonance. And he was wonderful. We had a, we had for two courses, absolutely. He was just as a colleague. He inspired me. Every time I went to work, I would say, I want to, and I talked to him, I'd say, I want to grow up and be a physicist, even though I already was.

But one of the things he said at NMR, when they developed that, he said, it's fascinating, but it'll never be useful for anything. But the difference is, and I think, I'm glad you're honest about it. I tell people that one of my great joys is that nothing I've ever worked on has any practical significance whatsoever. And people say, well, you don't know that. And it's true, but I'm pretty darn sure that nothing, you know, except for our understanding of ourselves and how we got here, which I happen to think is incredibly important, is never going to make a better toast or a car or anything like that. But although experimental techniques are different, they can have good side effects.

But once again, I think their scientists fall in the trap. I remember when the SSC, when the supercalotter was being built, they fell in the trap of trying to justify it by its spinoffs. And I don't think you should ever do that. If it's worth doing, it's worth doing. And yeah, anytime you spend $10 billion on any technology, it's going to have great spinoffs. But that should never be the chief justification for doing it. Well, the next thing you talk about, and maybe we'll dwell on and quickly do some of the others before we get to values, which I want to get to, is probably for me, personally, the most important aspect of understanding the world, which is as orders of understanding and Fermi problems. It's a central piece for me as a scientist.

And as I say, my book, Fear of Physics is really about that. It's that science has given us this amazing set of tools to take what seem to be insurmountable problems. And not only solve them, but understand them in ways that you couldn't before. You know, how many piano tuners are there in Chicago? If I go and ask someone on the street, they're going to say, how the hell should I know? But Fermi gave us a way and we gave away. And you don't just to discuss that, which I think is interesting and we can talk about it. but also orders of understanding to first, well, why do you talk about orders of understanding?

Why should I? You talk about it. Yeah, I think that one of the things that makes us feel much more capable of taking on really complex, difficult problems as scientists is that we've learned that most problems that we've come across, or at least many problems we've come across, end up having many, many contributing factors, but the factors aren't all equally important. and that if you can learn which ones are really the operative factors, it's often greatly simplifying the problem and it allows you to take on what otherwise would just be a mess

and really hard to do. So one of the first things that physicists will very typically do is try to identify what are the main contributing factors and then really do quick analyses, as you say, these back of the envelope, how many piano tuners in Chicago style analyses of how big are each of these in their effect? How important are they? And, you know, in some sense, you would like people who are doing national budgets to do that as well. You want them to be saying, well, let's deal with the big

parts first before we start doing, you know, with big arguments over these little tiny things that will make no difference to the overall effect. And that's, I think, one of the trainings that help scientists feel a little bit more powerful, a little more capable. And when we've taught it to the students and let them play around with doing quick estimates of things, we've had people come up afterwards and say, you know, that really made you feel like you can do things in the world. You feel superhuman. It's a magic.

It's a superhuman power. It seems like magic when I do it to people. And it really is. But that's one of the areas where in physics, we're fortunate, because you can often isolate the important things. One of the problems of, say, a science like biology and medicine. I mean, what doctors are trying to do is exactly that, but it's often much harder to do, to find out what are the key factors. Sometimes you have to work for years and years and years and years. It's not obvious. And similarly often in society, it's the case, too.

Absolutely. And climate change is a very complex thing. But it does, as another example, But it usually pays to at least ask this question early on because some problems that otherwise look completely hopeless are amenable. You know, even ones that, you know, that are social issues in public life, that, you know, some of them are a complete mess where there's many, many, many contributing factors that you can't get behind. But sometimes surprisingly, just one or two factors, if you can get at them, they provide you'd leaveers to help fix things.

You know, I was going to say, interesting enough, while this is fundamental to our ability to do things, I found it to be perhaps the hardest thing to teach students, especially graduate students. And the reason is the mistake we make in teaching, we give students problems and problem sets and undergraduates that they're guaranteed to be, well, in principle, they're guaranteed to be able to solve. Yeah. And to some extent, we give them PhD problems that they're more or less guaranteed to solve.

But in the real world, it isn't that way. And in order to solve any problem in the real world, usually in physics as well as everyone else, you have to know what to throw out. And throwing out information is one of the, I found, one of the hardest things that, you know, when I watch students go from being undergraduate students to fully functioning graduate students, the transition is sometimes being willing to know, okay, I want to be able to solve the problem. It may not be the problem, you know, that I thought I could solve because there's so many things. But being willing to throw out the stuff that is going to get in my way.

And I found that's probably the hardest thing to get people. No one wants to throw out information. And even though when it's, even though it's irrelevant, it's really interesting. I don't know if you found that in training students. I think that that's right. And I think that, you know, the terminology of, you know, teaching people to be looking for what things are negligible is, is a, you know, ends up being a big deal, right? Trying to say, you know, this thing here is just not going to be important. and don't waste your time on it when there are other things that are important.

And often, and I will get to this in a second we come to values, but it's also, in the real world, it's also worth realizing that what you'd call first order problems, the ones that you really have to take an account, for one person may not be the same as for another. What really matters, even if they're both, people are looking at the same, what you might think is the same underlying problem, what really matters for one person may be really not matter for another. And it's not as if one is right and one is wrong.

Always. And you have to realize that. And I think it's something that scientists too often don't appreciate when they work with government. We'll get there in a bit. But the Fermi thing, the fact that you can, you know, and I challenge the listeners to determine how many piano tuners are in Chicago. But it does something else, which I've always been amazed of. You know, when I grew up before calculators, I can, when I go to a grocery store and buy something, I know roughly what my bill is going to be. And I'm always amazed, and I remember the cashiers are always amazed when, you know, you can estimate something better than they can. But more importantly, it helps you from making ridiculous errors that a cashier, you know, you have 50 items and someone typed in something wrong in the cash register.

And they go, okay, it's $1.50. And you go, you want to think about that again? Because people trust what they see so much. Whereas if they just didn't estimate, well, we had 20 items. The average, the Azure cost per item is $2 or $4.4. or $6, it'll be, you know, 80, $120, you know, $160. And it's interesting how once again, that is a wonderful technique that people can use to overcome often finding ridiculous things, but just by thinking about order of magnitude. And it's not trivial. I hope he won't mind

me saying this. One of your esteemed colleagues was a student of mine, Martin White, who you may know. And Martin is a remarkable scientist. I have a great student. and he's amazing, amazingly fast at computing and doing numerical computations. And I was, you know, most of my students who were really good, and I've been fortunate to have a bunch of good students. You know, I see them. I've just been there to, you know, to guide them a little bit, maybe provide one useful thing, but they're going to do fine regardless. And the only thing I think I taught Martin was that, you know, he would do these amazingly intense computer calculations, come back the next day with the result.

And I'd say, it doesn't seem right. And I'd say, why? And I'd say, well, let's think about what you think the answer might be. And I saw over the course of his career, and he was a very successful scientist, just simply being able to anticipate whether the results of a really detailed calculation are right by stepping back and doing the simple things. If I like to do the simple things, he liked to do the complicated things. But it's very important. Absolutely.

And I also think this is another example of saying that's becoming even more important, in a web-based, you know, internet world where you will be given all sorts of claimed numbers all the time and you'll just look them up and you'll find them. But you need some way to do a quick sanity check to say, you know, this doesn't make sense. Because otherwise, it's just too easy for people to produce numbers and never check them themselves or to be, you know, intentionally making something up. And you need to be armed with the ability to say, that doesn't make sense. You know, there just aren't, there couldn't be that many cars on the road, you know, let

alone, you know, this much oil being used or whatever it would be. Or it is. I remember once getting trouble pointing out, you know, the average American is less likely to be killed by a terrorist than many other things. And the other people get mad at me, but, you know, if you work it out, it, and I think it's a training. It's a useful training and it's a fun training. I think people don't realize because it's numbers that it's actually fun.

and one of my mantras came from the former publisher in New York Times is I like to keep an open mind, but not so open to my brains fall out. But I generally find that when I read scientific papers too. It's not just going on the internet. If you read something and you think this doesn't jive with everything I know, it's quite likely that it isn't true. Now, it doesn't mean it isn't. You have to check it.

But if your experience and especially your quantitative experience says, you know, hey, you know, someone says, this was the best four years that ever happened, say they're running for president again. And you say, well, let me think about that. Was that really the best four years? Anyway, it's always worth asking yourself some simple questions to see if you're being deceived. And also, as we will skip, you know, we'll zoom through the last bit, which is really about how you can get things wrong, you know, how you can deceive yourself. and particular the notion of biases, that the bias that you had, that we all have,

but the bias that the cosmological answer is zero or not zero, the bias that, you know, you give lots of examples in the book, the bias that people had, you know, Feynman gives a variety of examples of this, of measurements of the electron mass or the electron church. And you can see what happens is the first experiments here and all the other experiments are around there, and then it slowly changes.

and every next experiment is somehow biased by the experiment before because they don't want to be too far away from it. So it never made that big jump, but slowly asymptotically approached the right value. And that bias is very, very important. And you spend a significant amount of time talking about debiasing. And so I want to turn it over to you again. Well, yeah, no. And I think that one of the things I like about that particular story is the fact that it was a great example where we are

still discovering new ways that we go wrong. And this isn't like, you know, we've finished developing science that we're done. It's that we will constantly be hunting and discovering, ah, there's another subtle way. And for example, some new ways are more likely to happen in a, as technology changes. So, you know, the fact that we no longer tend to do our analyses by, you know, adding 12 numbers together and then dividing by 12 and getting an average, typically, you know, we're putting things into this complex computer program and letting it run for a while. and that it leads to different kinds of errors that we have to watch out for. Like, you know, that now we depend on debugging our computer programs.

When do you stop debugging? Maybe you stop debugging when you get an answer that you're not embarrassed to have your graduate student go and report at the next conference. That's not a good reason to stop debugging. And we've started to see it when we reanalyze that data that you're describing of these masses of fundamental particles, and you realize that you saw that weird pattern appear. and it led to the particle physicists beginning,

and then us cosmologists starting to do that technique. I was talking about where you blind yourself to the result when you're doing the analysis so that you can't decide to stop debugging your computer program just because you got an answer you were hoping to get or that you thought wasn't embarrassing. And so I think that I love the fact that it's only been the last, what, 15, 20 years, that that's becoming a standard,

but it's becoming enough of a standard that in the fields that I've taken this on seriously, you just don't publish a result unless you've done some effective blinding. It hasn't caught on to the rest of the science world yet, and I'd like it too. But these things are developments, and we have to constantly be looking for them and trying to figure out how do we do better. And this is one of the ones I really love to see in action. It is very important. And it's in action of the, again, I'll quote Feynman, because I love quoting for him, but his point was it's really important and probably the hardest part. And it's implicit, what you're talking about here is that, as he says, every time you come up with the result,

you work really hard to see if it's correct, but you should work equally hard to see it's wrong. And that's the thing. We should work just as hard to prove ourselves wrong. And we never do that. It's the hardest thing to do. Exactly. And maybe, you know, you should be even working harder to prove that you're wrong than to prove that you're right because it's because you'll be you'll already do the first to you know the second one is the one that you're going to have to really push yourself you know you give us to in terms of confirmation bias and blind analysis you know this confirmation bias being we tend to try and confirm the things we like and how blind analysis can get around it you talk about you know a recent result which is really

important and one that is relevant to my own interest But I like the story about LIGO. So why don't you talk about what LIGO did? Because I think it's really important. And explain what LIGO is, I guess. So LIGO was this amazing project, but it took like 40 years to the techniques to work well enough to look for these tiny, tiny changes of distances as gravitational wave goes past us. And so, you know, these huge tunnels with, with, with, where they're bouncing light back and forth, you know, between mirrors. And what they were afraid of is that, uh, this false positive and false negative thing was going to, to hit them that they would be so careful not to, um, to take false alarms that they wouldn't see real, uh, signals when they happened.

or they would be just too accepting of coincidences of what looked like a gravitational wave signal and then they would be fooled into the thing they've seen one that wasn't there. So they did something which is very much in the spirit of blind analysis, which is they deputized a small team to make fake datasets that they would inject into the stream that they were analyzing. and they were designed to make sure that people knew that there was some of the results that would be very difficult to find that actually was a real signal. And if they can't find it, you know, which was because it was intentionally fake to be a real signal,

if they can't find it, then they know that they're not actually being sensitive enough. They're not actually doing the job correctly. And then similarly, you know, they can be a, check the other way that they're not just finding things when they didn't have a signal in. So they did this and then they set it up so that the teams would be go through the entire analysis chain ideally and maybe even write the paper before they would go and check to see whether or not it was a fake signal or real. And I gather that it didn't quite get used the way they were originally intended just by the way things happened in terms of what was turned on and what

was turned off at the right moment. But they did have the experience of going through a lot of the analysis and then something turning out to be a fake. Yeah, they had that happen at least once. Yeah. So that was a good exercise. And then the time when they actually finally found the first signal, it was so powerful, a signal was so strong that I think everybody's reaction in the team was, I'm sure this has got to be the fake. I mean, it just was, you know, it was just too obvious. Yeah, they all thought it was the fake. And, you know, but they'd gone through it before and when it wasn't a fake.

And one of the things you don't, I love the story because it's amazing. It was such a gold-plated signal. If you had to imagine the pint of signal they were looking for, that's it. That's enough to make you, you know, think it's not true. Yeah, yeah. Remind me, I'll tell you in a second. I'll tell you another time that happened. But, but what is, what you don't mention is,

It's the other reason that it's the serendipity of that is that not only did that signal look so beautiful that they were convinced. Many people thought, well, it's the team coming in. But the other thing that was remark about it was it occurred three hours after they turned on the apparatus. So they were going to wait six months. And the first signal comes in three hours. So there's all these reasons to believe it's not true. The fact that it came in three hours after the minute. And it's, and I like to say, it's amazing because it came from something.

coming 1.2 billion light years away, I think. And to think that signal traveled for 1.2 billion years, if the experiment had been turned on three hours later, they never would have seen it. Yeah, yeah. No, no. It's amazing. So sometimes randomness, you know, hurts you like when we thought we'd seen a pulsar.

Sometimes it's on your side, giving you the nicest signal in the first three hours of your, you know, running. And I remember, by the way, after you guys discovered, you know, observed that the universe was indeed expanding, there was a one. it's an energy of empty space. I must have gotten interviewed at some point. And I said, the first time I didn't believe it is when they saw it. Because it's not too good to be true.

I had been arguing it was there. And then when you saw it, I thought, nah, it can't be true. Anyway, the last part of the book is an important question, because it involves groups as we're talking politics. And I want to spend a few minutes on it. And it involves the wisdom of crowds and the madness of crowds. One of my favorite books is Charles McKay's book, The Extraordinary Popular, delusions of the madness of crowds from I think 1850 or something. It's a it's a telling book to read because it's worthwhile reading now because it's it's worth realizing that in many ways we haven't

gone forward. But you point out that there however is a wisdom of crowds and there's two aspects of wisdom. And one of them is just, but one of them which seems magical is not magical at all. It's just statistics. So why don't you talk about it? So this was also a, a, early paper where somebody's pointing out that, I forget, who was the, was 1916 or something you referred to it. I forget. Yeah, but it wasn't McKay was 1850, but there was someone else who pointed out the effect that you're talking about to you, and I didn't know of that person until I read it in the book.

I don't know where it is now. I forgot now. Anyway, they were pointing out that if you ask a bunch of people, let's say, to estimate the weight of a bull in a affair, you know, that, You know, people don't get it right. You know, they're wrong. But on average, they average in on something that's remarkably close to the right answer. And this doesn't always happen.

You need it to be something where there's a healthy estimate that people might have had, but they will be way off in all directions, and that cancels. Now, this is what we call the wisdom of craft. and you can see it in other situations where you need lots of partial information. And if you put that partial information together, you actually get some, you know, a better result than if you just asked a couple people, you know, something. And the question is, how do you get that effect and not get the other effect, which is heard thinking?

That if, you know, you ask the first person in a room, you know, a question and then go around the table, people tend to kind of not want to disagree with either the most charismatic person in the room or the person who went before them. And so people sort of lock in on something. And you'll get, you know, people, you know, believing nonsense just because they're all just trying to match each other. And so how do you get one, not the other? And so that was sort of the beginning of this discussion. And, you know, what is it that you're trying to achieve when you get a group people together. Of course, what you want is the best of their collective wisdoms, not the worst of their collective madness, you know. And what we start seeing is that there are a number of different

techniques that people have been using over the years and trying out over the years that give you more of one than the other. And it was worth everybody being aware of them and having them available to them. So that was what the, but the chapter. Yeah. The first thing is, I mean, it, you know, the first thing is just a law of large numbers that if you get a lot of people, people, you know, without independently and without a dog in the fight, estimating something, it'll converge and you'll get something big and some small. But when they do communicate, then you have to worry about these things. And I found, you know, you talk about how we can get the best from groups and I won't go into it.

But I found it very interesting because I did a podcast with someone named Charles Duhigg, who is a best-selling book, basically about how to communicate. And it's examples of people who can effectively lead a group to a good decision. And interestingly enough, many of the things you talk about here are exactly the same things that he talks about from experience that have worked. And I think it's really interesting. So, you know, you really aren't. And people will follow the most charismatic person. And, you know, one of your colleagues, I was surprised I didn't see it here.

Maybe I missed it. The famous psychological experiment that's been performed for a few where, you know, they have a group of people in a room and a plant. And the plant says, oh, I see that. you know, the person who plan, oh, I see that. And then all these other people will start to see it. And it happens all the time because people, you know, don't want to deviate from the herd. And that takes us to really the real world, perhaps the most, which is how can we communicate, how can we work effectively?

And how can the scientific tools be integrated into things like politics? And the last part talks about facts versus values. And it's really important to know the difference. There are, you know, I think what's his name? Senator from New York, Moornehan used to say, you're allowed to have your own opinions but not your own facts. But it's not just that their opinions and facts. Values matter and to discount them is to discount human experience.

So why don't you talk about a little bit about it? I mean, I think early on we started realizing that, you know, there's a tendency to think, let's just get everybody to think rationally and then everything will be fine. But what's missing in that statement is that it's most decision-making that you have to make and policies and things like that actually are driven a huge amount by the fears and the goals and the ambitions and the values that are in play. And that's the real reason that you're making a decision because the saying you're trying to reach or the same you're trying to avoid. it's not that you got there because you want to exercise your rationality. You know, that's not why you're there to make a decision.

And so in the end of the day, if you don't come up with any principled systematic ways to weave the values and the goals and the fears in with the rationality, the part that you'll end up leaving behind is the rationality, not the fears and the values and the goals. And as we see, you know, often in the world. And so we thought that if you take seriously the rationality, you have to take seriously the rationality, you have to take seriously how do you combine it with the values and the fears and the goals. And so that was why we became very interested in what techniques have people developed that try to do that.

Now, to begin with, you know, first of all, you might just want to help people train themselves a little bit on recognizing which parts of the decision are the values and which parts are the facts. Because not that you won't need to discuss both of them. It's just you need to discuss them in a slightly different ways. is the facts you really want to discuss using all these techniques of evidence and rationality that we're describing and probabilistic thinking, you know, et cetera. The values, I mean, you want people to, you know, they often, sometimes people think that if it's a values question, there's no point in having a discussion because that's your value, this is my value.

But in fact, that's not really the way we work. I mean, we tend to have multiple values, each of us, that we're constantly trying to prioritize among each other in a given decision-making process. And if somebody points out to us that actually we share the same values, it's just that in this particular case, I prioritize this one. In another case, I've actually produced the other one that that person's prioritizing. That's actually helpful because it gives you a chance to stop and think, okay, I shouldn't be dogmatic about this value because actually I gave it a lower ranking

for another problem. And I should be listening to what the other person is saying, and we should try to think together about why do we want to prioritize one or another in this particular case. So there is an interesting conversation to have there. And you can learn a lot in talking about values. It's just that it's a different conversation than the conversation you have when you are doing facts. And you need to do both in these problems.

Very well said. I think that's great. And there's one aspect of it that you didn't discuss a little bit that I think is complementary. And I learned it from my wife who worked for the Australian government helping interface between politicians and scientists. and what she learned was that, and what she taught me, was that the real problems is neither side understands what's important to the other side.

The politicians don't understand what the scientists are interested in, but what she taught me, and she's just walking in here, so she's going to hear me talking about her, but what is equally important was that the scientists seem to think that the scientific issues are the only ones that matter,

where it's not, and it's not as if it's an illusion that economics is important. You know, for example, in climate change, it may be clear that doing X will, you know, if you really want to reduce climate change in some ways, do X, but for a politician and for people, it might require a change, which to them is more worrisome. And you can't discount that and say that's not important. The scientists themselves just didn't realize where the politicians were coming from. They're saying, this is important. It's going to affect our economic policies and it's going to determine.

and whether our government is re-elected or not. And you can't just say, well, I'm going to discount that. Because the minute you do, you're not going to have an impact on influencing the politicians. If you say, well, what you think is important isn't important, you know the politicians are going to stop listening to you. So she'd help train the scientists to make them realize what the politicians were interested in and vice versa. Realizing that people's values, when it comes to the real world, even on the same set of facts, can disagree on what,

action should be because of different values. And it's not as if those values can all be discounted. And unless you recognize what they are, you're going to have no useful dialogue. Absolutely. And that's the sense in which, you know, if you aren't engaging with the values and the goals and the fears, you're not really taking seriously the decision-making process. You're just, you know, doing this isolated, you know, rational calculation. But that's not. part of the decision process that ends up being effective. And I thought that one of the things that was perhaps most optimistic and inspiring to realize is that people often feel like in our current world, there's not really much interesting

discussion and debate and that people aren't able to make progress together. We happen to be living in a particular polarized moment, you know, clearly. On the other hand, what I thought very inspiring is that if you take a random sample of the population, and this is this technique called deliberative polling and people call it sometimes civic assemblies, if you use a truly random sample of population who are really representative of all the demographics and all the different interests that are there, that group is much better at having a conversation and thinking through problems than their leaders today, the orphan art. And that was really interesting to read about and to hear about.

And that those groups, now, if you leave them alone and don't give them any information, of course, you end up with a slightly useless conversation. But if you empower them by having them have panels of experts available to them while they're trying to deliberate and while they're trying to figure out problems, so they can keep whenever they get stuck, they can ask a panel who may disagree with each other, but they can ask them, okay, what's the evidence? and what the stories, what the issues here, they can make much more progress than I think people are aware.

And so that was, for me, a very encouraging part of the story that it's probably important for people to recognize that if they can get that effect, which people sometimes describe, you know, in juries. They can see that juries do much better than you would expect from just a random group of 12 people who you'll think of as being that smart about this legal issue. And so the more we can get that effect, and it has a lot of authority because it is the true representative sample of the public, even more so than the particular elected officials are at any given moment.

Well, that's nice to have that little bit of optimism. And it's important because a book like this, or the things we talk about are useful if they are useful of society in general, but they're also only useful if we realize how societies work. And that, you know, that's not everything is just pure science in the sense of pure facts. And that's why society, you know, people say, why aren't scientists, leaders of government? Because it wouldn't do any better than anyone else, I think. Societies are not scientific in that sense, although the tools of science can make life much, much better, which I think is the point of your book. And I want to come close to the end now here with a challenge that you give out in your penultimate chapter,

And I want to give you that challenge. The proposition is we may be the first generations in human history that could reasonably aim to build a lasting world in which every person can thrive. So you say that the proposition is debatable, but I'm now going to give you that challenge and say, argue for or against the motion. So for me, just the idea that this is even a problem. possibility is remarkable. I mean, I don't think when I was a child, I don't think I could have, even with a straight face, had had that conversation because I think we were all aware of the,

you know, the limits of growth and the, and, you know, all these, these mislusian, you know, problems that, you know, we were going to just outstrip our planet and not have the resources, et cetera. But we've learned that we actually have, you know, grown dramatically since we were, we were kids. in a population, and yet the fraction of the population that's starving, you know, goes to bed hungry every night, has dropped dramatically, you know. And so things that we never thought would be possible, I think are possible. And we've even now come to the point that, you know, the next major extinction caused by a asteroid hitting the Earth may not happen because we would actually be able to nudge it out of the course before it hit us. You know, there are things

you just couldn't imagine that we would have any effect on, including climate, that we actually could, you know, just like now we're doing damage to our climate. We could actually control our climate so that we didn't have to go through these ice ages and, you know, in the future, that would have otherwise been impossible to have our civilization survive through. There's so much more that we have available to us now than ever before. And the part of the story that, you know, when I say that it's conceivable, that's, I think, really important for people to recognize that we live in a time where, on the one hand, it's very scary. Everybody is very aware of all these different things that could go wrong that we could destroy our, you know, our planets or our civilization. But it's not

only the worst of times, it's also the best of times. We also are living at a time where, in principle, if we could just pull ourselves past all the, all the bickering, you know, and the, and the, you know, the lying and the cheating, whatever, to try to maintain power or get power or preserve from one to the other, you know, if we could manage to be just a little bit more working together to solve things, you know, I think we have a reasonable chance that this could be an amazing generation to live in, that, you know, we would actually be working together to build a planet where everybody was fed and clothed and housed and educated. And we'd be worrying about things like, well, what are they going to do with their time if we've got all these, you know, robots,

and AI doing other things that we used to think we would be doing. How is it that we want to have a meaningful lives? I mean, that would be a fascinating challenge if people felt like that was where they wanted to spend their time and not be spending their time trying to fight to control some political domain. And in some sense, it's a bizarre moment to live in because we live in a time where either one of those could be the way we spend our time.

And right now we're seeing a little bit the worst of it, you know, where we all feel a little bit like, you know, mostly we're doing is trying to stave off disaster, you know. And but it doesn't have to be that way. I think, you know, this could be the period in which we're actually taking advantage of the fact that we're, that there's an exciting activity that we could be doing, that we've never had before the option of let's all work to build something that we would just feel proud to be living in this planet, you know. And so that was the sense of that statement. And it's no guarantee that we'll do it and that we've got it right. It's just that we should be aware that we're making that choice in one way or the other. And if we can help each other make the better choice, it would be a particularly fun period for us all to be living in. Well, that's a lovely, an optimistic response.

And the future is terrifying, but it's terrifying. because it's exciting and because we can change it. And I think that's a wonderfully upbeat way to end, but I'm going to actually going to give you the last word in a different way. I just gave you the last word impromptu, but I'll give you the last word by reading something in the last page of your book, which I think is important. We said, we hope to leave the reader with a realistic but exhilarating sense

that with a collective push, we just might be able to turn this corner into the third millennium with a new collaborative approach to our problems and opportunities from the small to the global. and I applaud you for trying to do that and it's been a real pleasure to chat with you. One of the things I just was thinking about I really enjoy about doing these things is I take people I've talked to many times

but I don't think you and I had talked that long and substantively at one occasion before. And it's been a true pleasure, Saul. I hope you enjoyed it as much as I did and I'm sure our listeners well, but thank you very, very much and I hope you had a good time. Absolutely, I'm really glad you're doing this. it seems like a really important way to get these stories and these ideas out. Well, thank you.

Hi, it's Lawrence again. As the Origins podcast continues to reach millions of people around the world, I just wanted to say thank you. It's because of your support, whether you listen or watch, that we're able to help enrich the perspective of listeners by providing access to the people and ideas that are changing our understanding of ourselves and our world and driving the future of our society in the 21st century.

If you enjoyed today's conversation, please consider leaving a review on Apple Podcasts or Spotify. You can also leave us private feedback on our website if you'd like to see any parts of the podcast improved. Finally, if you'd like to access ad-free and bonus content, become a paid subscriber at Originsproject.org. This podcast is produced by the Origins Project Foundation as a non-profit effort committed to enhancing public literacy

and engagement with the world by connecting science and culture. You can learn more about our events, our travel excursions, and ways to get involved at originsproject.org. Thank you.