Prof. Cristopher Moore received his B.A. in Physics, Mathematics, and Integrated Science from Northwestern University, and his Ph.D. in Physics from Cornell. From 2000 to 2012 he was a professor at the University of New Mexico, with joint appointments in Computer Science and Physics. He has published over 130 papers at the boundary between physics and computer science, ranging from quantum computing, to phase transitions in NP-complete problems, to the theory of social networks and efficient algorithms for analyzing their structure.
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[The following is a transcription of the interview conducted by the Strawberry Crest STEM Journal Club President Sonia Teodorescu (class of 2025) and Vice-President Irina Savchuck (class of 2026) with the guest speaker of the University of South Florida 2024 R. Kent Nagle Lecture Series, Professor Cristopher Moore of the Santa Fe Institute, on April 18, 2024. The text has been minimally condensed for clarity.]
ST: Dr. Moore, your research is well known both for its depth and diversity of subject areas. What makes a research problem interesting for you?
CM: I try to choose problems based on whether I will learn something or not. And so that could be a new mathematical technique; it could also be something about society. Some of the work I’ve been doing lately on algorithms in the criminal justice system, I’ve been learning about the criminal justice system, which is very different from learning math. I’ve been learning about legal reasoning, which is not the same as logical reasoning, but it is reasoning. And that’s been very interesting because that’s taken me far outside my comfort zone.
But, also, if I have an opportunity to learn something new about math or about science, it’s like hunting for wanting to learn more has been a very good technique for me. And then, if I get lucky, I have something new to say about the subjects and otherwise, well, I still learn something.
IS: In your opinion, what is the biggest challenge research that STEM is facing today?
CM: You mean in like the biggest intellectual challenge or the biggest challenge in getting people interested in STEM, or… ?
IS: Both.
CM: Okay, let’s see. I mean, there are a lot of things that are moving very quickly right now, like climate science and climate change and what can be done about it. I don’t work in that area, but obviously that’s a huge challenge and it involves all these different disciplines from ecology and biology to physics and even computer science and how to design a renewable power grid. But then there’s also a lot of politics there, of course, which is frustrating.
Other big challenges… I’m trying to, I’m trying to not think about the things that I run up against the most.
I’ll name one in mathematics, which is that there are all these, there are a lot of areas in mathematics where things seem to act almost as if they’re random, like prime numbers act in many ways like random numbers; if we knew that they acted that way, then we could prove lots of things that we can’t prove today. And there are some very basic questions about them that are still unsolved.
Like, are there an infinite number of pairs of prime numbers that are only two apart?
I don’t know if you’ve heard of that one, but if primes acted the way they seem to – kind of just being almost randomly sprinkled – then that problem would be very easy, but they’re not random, they’re fixed mathematical things.
So what does it even mean to say that they act random and when do they and how much do they, and that’s kind of – that’s an abstract challenge that’s very interesting.
I think one of the biggest problems in the structure of STEM is that even though a lot of people are interested in interdisciplinary work, a lot of places are still doing it in a very disciplinary way, like “I’m a computer scientist, you’re a geneticist. You give me some data and I’ll do some cool computer science algorithm on it.”
Well, that’s fine, and a lot of good work gets done that way, but what I want is for the boundaries between the disciplines to really break down: I want the computer scientists to say, “teach me something about how the cell works, you know, really teach me something about the biology or the genetics.” And maybe the geneticist would say, “teach me something about how to analyze this data.”
So I think when you look at the way university departments work, they’re still fairly narrow in a sense.
I’ve changed fields every few years in my career, and most of the techniques I use, I learned after graduating and after getting my PhD, so I would say that in school, I did learn to learn in a lot of ways, but a lot of the specific things that I’ve learned, I guess they’re part of my background.
Um, yes, I think getting to really breaking down the boundaries between disciplines is an important challenge for how we do science.
ST: Thank you. Here’s a more fun one: Could you give a sort of like a “two truths and a lie,” but in the context of research?
CM: [chuckles] I used to play this game with my daughter when she came home from school to get her to talk about school. Two truths and a lie.
Um, this is hard.
Okay, well, I helped discover an orbit of the three-body problem that partly inspired the recent novel and TV show.
Uh, I have a great new algorithm for predicting whether somebody will commit an additional crime if they’re released from jail.
And – I showed with a friend of mine that computers that we can’t yet build can’t solve a problem (can’t break a code) that no one uses, at least not using the techniques that we’ve thought of so far.
IS: I think it’s even harder for us to guess the lie. I think it’s probably the first one, is a lie.
CM: In fact, that one’s true. If you look up the “figure eight orbit” on Wikipedia, which has three equal masses, that (using Newton’s law of gravity) chase each other around a “figure eight”, and this is sort of a braid that they would draw out if, if the third direction were time (the vertical direction were time) – I just, I discovered this computationally, when I was a postdoc in 1993.
I didn’t prove that it worked, I just – it worked in the computer. Then, in 2000, some mathematicians (independently of me, to be fair) kind of rediscovered it and proved that it’s really there.
So the first one is actually true.
ST: The next question is, how do you view the role of mathematical sciences in society?
CM: Uh, ah, great. Um, yeah, it’s interesting.
I have a lot of colleagues at the Santa Fe Institute who are interested in using mathematics to model humans and human behavior; I’m actually very hesitant about this, because not only do I think humans are hard to capture mathematically, but I want them to be hard to capture mathematically.
If you have, say large masses of people who are just following their tribal loyalties and not thinking very much, yeah, you might be able to study them mathematically, but that’s not how I want people to act, I want them to be very individual and thoughtful and original and creative.
Of course, social scientists have been using mathematics forever; and the second thing I told you was the lie, when I said that there’s an algorithm to predict whether people will commit a crime if they’re released from jail: That turns out to be very, very hard to predict.
Maybe someday, in a creepy surveillance state, where we can keep track of what everybody is doing and saying all the time, it’ll get easier, but it turns out that if you look at somebody’s past criminal record and what they’ve been charged with and their age and a few other things, it’s still very, very hard to predict.
I have also studied epidemiology, which is partly about human behavior, but when I do that, especially when there’s a real epidemic going on, I tend to describe it in a very modest way, like, “Hey, I’m studying this mathematical model.”
Whether it will help us predict or respond to a pandemic is very much an empirical question, and I’m not claiming to solve that problem; that would be too arrogant.
So I sort of tip-toe around, and then I try to find partners who know more than I do about the real social situations, like people who are really epidemiologists out there on the ground who are advising their local governor or whatever how to respond. Sometimes they think that the math insight will be helpful and sometimes they don’t, and that’s okay.
I did do one fun project involving some statistics on different language groups, and that was interesting, but it was just sort of data analysis – it wasn’t trying to predict or model how languages change.
I mean, I think it’s something to tread carefully. I think it can be helpful, but whenever you are trying to model human beings, there’s a lot of ideology involved, and I think it’s, well, it’s just really important to look at the reality.
I’ll give one other example: there is a model called the Schelling model of how housing segregation might appear: our society is frustratingly segregated with people who look like this living there, people who look like that living there, with often astonishingly sharp boundaries between them, in a lot of American cities.
This model says, oh, well, if people have a slight preference for living next to other people that look like them, then this can kind of just happen voluntarily.
That’s a fun model, but that’s not how this happened at all, right? You look at the actual history, and it has to do with the government building housing around the time of World War II for defense workers and for returning soldiers, and a lot of governments from the federal government down to local governments and banks explicitly saying only white people can live here, so it didn’t happen through this sort of mathematical process, it happened through a kind of top-down intervention, which of course now we view as very unfair.
For me, if I’m going to think about society, I want to learn about society, which often involves talking to social scientists, historians, even philosophers, and I’m going to be pretty modest about how much math will add to the conversation.
Of course sometimes it should.
ST: We were also wondering, for students who are interested in pursuing some kind of career in STEM or have interest there, is there anything you would tell them or any advice you would give?
CM: Sure.
I mean, my own professional trajectory is a little unusual, but there are a couple of (things that I’m really grateful for. I went to Northwestern (University, editor’s note) as an undergraduate, and they have a program that’s still going, which I think was kind of ahead of its time, which was explicitly interdisciplinary. It was called the Integrated Sciences Program, and even though I was a physics and math major, I got to take really good courses in molecular biology and other sciences that it would have been easy for me to skip.
I’m also personally glad that I got a Bachelor of Arts, which rather than a BS, which meant that I took classes in theater and literature and so on.
I think being well-rounded that way, both in other sciences and in the humanities, I think it’s made me a better scientist.
I know it’s made me a better person.
Actually one of the things where I think the university system here is preferable to some in Europe is that we don’t specialize too early; I think that’s a good thing, and I can say that a lot of friends of mine have, they’ve been like philosophy undergraduates and then ended up in ecology, or they’ve been physics undergraduates and ended up in computer science or whatever.
I’ve seen people go every which way, and I don’t know, I think that’s to be celebrated.
Of course, if you find something that you love and you’re really passionate about it, and you just want to drill down and get really good at that thing, that’s fantastic.
But for whatever reason, that’s not who I am: I like being spread out and learning about a lot of different things. Sometimes I’m worried that I’m too broad, but not deep enough, that I’m a little bit shallow.
Like people use this word, “dilettante,” like you’re sort of dabbling here and dabbling there, so that’s a danger, but also being too specialized and too narrow is I think a bigger danger.
I think that to an increasing degree, I think that both companies and universities (both the public and private sector) want to hire people who have a broad point of view and who are able to work with and talk to people in a lot of different disciplines, so I think that if you find opportunities to do that, you should.
IS: So the next question is, do you think it is possible to build a successful STEM-related career without a background in computers or programming?
CM: Without a background, yes. I mean, I know plenty of people who don’t, for instance, who don’t have computer science degrees at either the graduate or undergraduate level, but they pick up what they need to pick up and figure out the tools that they need to use for their research, and certainly some people don’t really do computational work at all.
They’re doing field work or they’re doing physical experiments or they’re doing kind of high theory in mathematics.
I do think having the use of computers in your comfort zone at some point is important. I think the most successful scientists I know are able to, it’s a little bit like what we were talking about a moment ago, they’re able to move around.
So if they need to do some theory, they’ll do some theory. If they need to work in an actual lab, (which I was terrible at, but it was kind of fun, but I never got the results: everybody else had this beautiful white powder and I had this brown sludge or whatever,) but they can do work in a real lab or they can go out on a boat and do measurements and they can then take their data and use some computational tools to analyze it.
One thing I would personally caution against, I think it’s important to understand the tools we use. There’s a lot of tools, especially in data science and computation, which are sort of off the shelf things. They’re software packages and, “Oh, I’m using this because that’s what everybody uses,” but sometimes that tool itself makes assumptions about the data that could lead you astray, and I think that can be a little bit dangerous.
It’s important to look under the hood and see what techniques the tool is using, what mathematical assumptions does it make, what is it actually doing in between the raw data and popping up something pretty and colorful on a screen saying, “Hey, here’s the trend or here’s the pattern in the data.”
It’s easy to get sucked in by those things by visualizations that look nice and sometimes those patterns that aren’t really there, so I think it’s important to be able to use computation like you would any other tool, but maybe be able to understand what it’s doing inside and not just take its word for it.
ST: I think you actually started to answer our final question, which was, is there any kind of limit that you think there should be to people using technologies like computers for computation or other tasks?
CM: Limit to it? Like there should be laws against it or do you mean like I would advise against it or what sort of limit?
IS: An ethical limit where people should stop using computers and use their own skills.
CM: Well, I mean, there are things like I enjoy writing and I don’t want to use ChatGPT to do my writing because I want to be the writer. Of course, everybody’s talking about this now and it’s one example that just popped up.
Some people were talking about, well, let’s use ChatGPT to give people medical advice, right?
I mean, after all, it can read all these articles and all these medical journals and then it can give the doctor advice – maybe it can give patients advice.
I’m really nervous about this.
I feel like these tools are not yet ready because they’re not grounded in truth, you know what I mean? They don’t have common sense. What they do is they read lots of text or they watch lots of video and then they try to produce more stuff which looks like that kind of stylistically or statistically. It doesn’t mean that they’re really grounded in the real world.
There was an example just recently where the city of New York, which has a big, complicated city code full of lots of laws about things, trained a chat bot to answer people’s questions about the law. It gave all sorts of wrong and illegal advice like, “Oh yes, if you’re an employer, you can take a portion of your employees’ tips.”
That’s not true at all.
Or “Yes, it’s perfectly okay to tell people that you’re not going to rent to them because they’re paying you with a voucher from an affordable housing program instead of with regular money.”
That’s not okay.
I mean, I get the idea- there’s a lot of knowledge out there; I know that doctors and surgeons and so on are already having a lot of trouble absorbing all this knowledge.
You would think that everybody has it at their fingertips, but no, it turns out that a doctor who’s seen a case like yours before is going to make a much… they just have more knowledge in their mind than somebody who never has.
You would think that, “Oh, this person can sort of search for that and then apply that knowledge,” so I understand the idea of using AI to help us with that, but I think we should watch it like a hawk.
I think it does a lot of silly things and the mistakes it makes are all so strange, they’re not the mistakes a human would make. But it gives us answers with so much confidence and it’s so good at imitating the style of a confident human speaker or writer that I think it’s very easy again for us to be kind of sucked in by that and not apply enough critical thinking.
Where math is concerned, of course I use computational tools every day to do experiments to help with calculations, but in the end, I want to understand if I’m going to come up with a proof, I want it to be a proof that I can understand and explain to a friend.
I don’t want the computer to just have 30 pages of symbolic shuffling and then say, “Good news, the theorem is true,” that’s not what we expect from a proof.
Maybe you’re familiar with the story of the four coloring problem – I’m actually going to mention this in my talk tonight – about can you always color a map with four colors so that no two countries that touch have the same color.
This was an unsolved question for many years and then it was proved in 1977 using a lengthy computer search that went through hundreds of cases. People believe that this is a proof in the sense of, okay, now we know it’s true, but it’s not satisfactory because it sort of doesn’t tell us why it’s true.
So I think going for that human understanding is important. And even if I’m a cancer patient and some AI says, “Oh, well, based on your genome, we should give you this treatment,”
well, okay, I mean, yes, I want to live, give me the treatment that you think is best, but I kind of want to know why.
I at least want my doctor to kind of want to know why, I don’t want it to be a black box, this magical black box, which we can’t see inside, to just spit out answers. I want us to understand.
ST: Thank you so much for speaking with us.
CM: Sure, thank you.
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