Jim Simons – I’ve Always been something of a risk taker I think

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The following interview with Jim Simons is a few years old but the content is evergreen. The discussion is particularly interesting as besides discussing a wide range of topics, focuses on Jim’s early life as a math prodigy. Check out the video and the transcript of the talk below – first here is a nice quote on Simons famous quant hedge fund:

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James: Yeah. Well, yes and no. When we would bring smart guys into Renaissance, we didn't know what they were going to do, we hoped that they would come up with some good ideas to make the firm better, but I didn't say, oh this guy is going to discover, I want this guy to discover this thing, I didn't know what he is going to discover. But it's of course very focused on a narrow set of outcomes whereas the other basic science, anything goes, is very different from that.

Speaker 1: What's your attitude to risk as you almost have created a business that's based on removing risk...?

James: Reducing it.

Speaker 1: Reducing risk, and yet you took a risk while leaving tenure to go and start a business. I mean, are you a risk taker or you are a risk minimizer or were you? You probably need to worry about it, what's it like?

James: Well, I suppose as we get older we get a little less, little more risk-averse, I don't know. I'd always been something of a risk taker I think. But I've had good taste, and especially again, as I say, in people, and have pretty much always partnered up with people. So that cuts the risk. But I did. Certainly, when I started the company, I had to finance it myself, and I didn't know whether it would succeed or not, but I was pretty sure it would, but I didn't know.

Note this transcript is not verbatim and is for information purposes only.

James Simons (full-length interview) - Numberphile

Speaker 1: Should we start at the beginning?

James: Okay.

Speaker 1: As a child, were you good at mathematics, like, was mathematics a natural thing for you?

James: Yeah, it was very natural. I always liked it. I liked counting, I liked continually multiplying things by 2, although by the time I got to 1024 or whatever it is, I was – I'd had enough of it. But I liked math. I discovered, as a very young kid, maybe 4, something called Zeno's Paradox. Do you ever hear of Zeno's Paradox? My father told me that the car could run out of gas and I was disturbed by that notion, it never occurred to me, but then I thought, well, it shouldn't run out, it could almost use half of what it has and then it could use half of that, and then half of that, and then you go on forever, and so it would never run out. So, now, it didn't occur to me, yes, but it wouldn't get very far either, but the idea that in principle you didn't have to run out of gas was kind of a profound thought for a very little boy.

Speaker 1: Were you a talented student? Were you always getting really good grades?

James: Well, I was a pretty talented student. I knew I was very smart somehow. On the other hand, I was very careless, so I'd sometimes screw up arithmetic tests just because I did it too fast or sloppily. But I liked everything about it likelihood about math. I loved learning the formulas for the volume of a sphere 4/3 πr3, I always thought that was a great formula. When I got to high school, we started with plane geometry, proofs, and theorems. That's when I really got gripped, I really loved that, I loved working on – some of the problems were harder than others, and I liked doing that. I was never the fastest guy in the world, but I would plod through it with determination. I just liked it.

Speaker 1: Did it feel like this was where your career was going to take you or were you like another boy who dreamed of being a baseball or something...

James: No, no, no, the only thing I thought about was I would be a mathematician, whatever exactly that meant. I didn't know quite what it meant except that mathematics was the only subject I really liked. Science was okay but it wasn't very well-taught where I went to high school, at least I don't think so. And history, English, I loved to read but I wasn't a good writer. I read a lot. When I went to MIT, I majored in mathematics of course, and skipped the first year because I've had some of those courses in high school and I even took a graduate course in my freshman year of the second semester in math, because it said no prerequisites required, so I said, oh, no prerequisites, I will take that course. I had a very hard time getting my arms around that course. I finished it, okay, but I was very puzzled by some of the concepts. However, that summer, I got another book, I got a book on – this was algebra but not algebra like solving equations, it was abstract algebra, and then in a week, everything came together for me, I understood I was no longer puzzled, I understood why they did everything. Other parts of math got me stymied for a while, but typically it would take some time for me to sort of grasp what it was, what they were trying to do, and then it would go very smoothly. So I graduated early from MIT, in three years.

Speaker 1: Did you imagine you would be a professor sitting at a university...

James: No.

Speaker 1: Like, you had no grasp of what...

James: I didn't – yeah, by the time that my freshman year was over, I kind of realized that that's what it would mean, that someone would pay you for thinking about mathematics and creating mathematics. What I thought earlier was I would do something that involved mathematics, but it was mathematics that I wanted to major in, and I could see quickly that, well that's the way, if you are going to do that, you are going to be a professor somewhere and profess.

Speaker 1: You also talked about this week of algebra where everything sorts of fell into place.

James: Yeah.

Speaker 1: Is that a common thing? Is there like, is this a thing that always happens, there's this moment where all the cogs move into place and it's like, oh, I get it now?

James: I think yeah, there are moments like that. Sometimes you are introduced to a new concept, and it's, you wonder, well, what, why, why they are talking about this, why is this , interesting. And then, you are thinking, you see some examples, and you say, oh yeah, okay, this is really good. A lot of mathematics is making definitions. You will see things that really could come together under one aegis, and you can define this set of notions and sometimes just a structure which fallows all these notions, and that's a great way that mathematics has advanced, because when you put a lot of things together that have similar characteristics, then you can try to prove theorems about the general set of things rather than some particular example, and that turns out to be a very powerful approach. So, making a good definition is a good thing.

They wanted me to go to Berkley and get away from MIT, meet some new faculty, because I was quite close to the MIT faculty and I thought, I think they didn't want to get rid of me so much as they thought I was probably pretty good, so I should get exposed to a certain guy named Chern who was just coming to Berkley that year, and I got this very nice fellowship and I went there, I was very eager to work with Chern, meet Chern, except he wasn't there. He celebrated his first year at Berkley, he had just come to Berkley, and he celebrated that first year by taking a sabbatical. So he wasn't there. So I work with another guy which was fine, and by the time Chern came, in the second year, I was already pretty far along with the thesis project. I was giving a seminar at the beginning of my second year at Berkley and in walked this tall Chinese guy and I said to the guy next to me who's that. He said, well, that's Chern. That's Chern? I didn't know he was Chinese. I thought Chern was probably short for Chernowsky or something or he was probably some Polish guy who had shortened his name to Chern. If it had been Chen or Chan, I would have known it was Chinese, but Chern with the R, I didn't know, but anyway, so I met Chern then and we became friends, of course, I was much younger than he, but we became friends and later collaborated.

Speaker 1: Can you give some idea about what your area of specialization is at this point, what are you zeroing in mathematically on?

James: Well, at that point, and pretty much thereafter, the field that I worked in was called differential geometry. And differential geometry is geometry but it's the geometry of curved spaces, it's not flat things and the plane, it's any shape or form, but typically with some distance function, so that knew how far apart things were. And the basic object of study is what's called a manifold, and a manifold is a lot of pieces of space glued together. So for example, the surface of a sphere is a manifold. Now, you can't just take one little sheet of paper and make a sphere out of that, but you have to glue a bunch of pieces of paper together and make a sphere. So, a manifold is something that locally, in your neighborhood, looks like it's just regular space, maybe it's curved a little but globally it could be quite different. And the surface of a tire, of a donut for example, is fundamentally different from the surface of a sphere. You can't deform one into the other.

What really got me onto it is a certain theorem which is called Stokes' Theorem. Now, Stokes' Theorem is the ultimate, I think, a generalization of what was originally called the fundamental theorem of calculus. Now, I don't know, if you studied calculus, but the basic theorem is if you integrate the derivative of a function, so you take a function, you take its derivative, the first derivative, and then you integrate that, let's say, from A to B. Well, that gets you back the organizational function, the answer, when you integrate the directive is of F, well as F of B minus F of A. That's the answer. If you integrate the derivative of a function, you get back the value of the function. Well, that was the fundamental theorem of calculus. Presumably, Newton knew that.

Now, that had been generalized into higher dimensions, so there were other things you can integrate and differentiate and go from boundaries to interiors and so on. And the general statement of that theorem was to me almost breathtaking, it was so beautiful, that theorem, and that's one of the basic theorems in differential geometry. And it's about integrating something over the boundary and that's the same as integrating something else over the interiors, something related to it. It's derivative in effect, that's called its differential. So, there's a whole calculus of things called differential forms and integration and all of that. And that takes place in manifolds in general and anything that looks pieces of space. So, I loved that theorem and that's what got me into differential geometry.

Speaker 1: So, was it a beauty or an elegance you saw or was it a trophy and a prize you saw that could be chased.

James: No, it was just the beauty. It was just the beauty of it. I just liked it. I'd learned a fair amount of algebra and that was fine, but it didn't grab me, but the geometry did. So that's what I specialized in, and when I got to Berkley, I was fooling around and started, I'd made some observations and I mentioned them to my thesis advisor, the guy I was going to work with. And he said, oh, that's interesting, that makes me think about such and such a problem. That was a problem falling around that people had been trying and failing to solve but I thought, oh, that's very interesting. So I started working on that. And he advised me not to because so and so had tried and someone else had tried and these guys were big shots and I was just a little shot. But I didn't pay much attention to him, and I solved it, and just worked through and worked through and had a little help along the way with some people, but basically, I drove that.

I'd done some other mathematics after my thesis, work on something called minimal varieties and it was kind of a fundamental paper. It took me six years to write that paper. People were – I think, how can I take six years. But anyway it did. And that turned out to be a very good paper. That was where I was coming from. At roughly that timeframe or a year or two afterward, I went when I was 30 to Stony Brook to be Chair of their math department. Stony Brook University was a very new university. They had a rather poor math department. In fact, it was very bad but an excellent physics department. They offered me this job as being chairman and I thought that will be a lot of fun.

In that timeframe, I had decided to try to learn something about an area of topology which is related to geometry, it's kind of handmaidens of each other, called characteristic classes, whatever that might be. And I thought I am going to just learn these characteristic classes because I didn't really know it, and it was important and I wanted to learn about it. So, I kind of started from the beginning and worked my way up, and I was trying to solve a problem, as I later discovered, had intrigued many people, and it's never been solved satisfactorily; so since I say that, you know, I didn't solve it either. But in the course of trying to do that, certain terms came up, certain functions of a sort that began to look just very interesting to me. And I saw, well, so they were a pesky term that I couldn't get rid of, I needed to get rid of that term to make this formula that I wanted but the term seemed to have a life of its own. And one thing led to another, I defined a certain invariant of a three-dimensional manifold and used that to prove some, what's called, immersion theorems, whatever those are. This was a very handy thing. And I showed it to Chern, I said, have a look at these results, in three dimensions; and he looked at it and said, oh well, we could do this in all the dimensions – because it was an area that he really knew about. Not the results that I'd gotten, but the whole, the general area.

So we worked together and we came up with, well, these results, this whole structure. In fact, there's – that's the slides of the presentation that Chern made at the International Congress in the early 70s. It was very nice geometry. I pushed on with it and we defined some things called differential characters which were another chapter. I was working with a guy named Cheeger, but the Chern assignments in variance, about 10 years later, the physicists got a hold of it, and it seemed to be very good for ailed them, whatever might have ailed them. And so, and it wasn't just string theory, as I [inaudible 00:15:35] developed, it was kind of all areas of physics, including condensed matter theory; even some astronomers seemed to want to look at those terms.

That's really what's great about basic science, in this case, mathematics. I mean, I didn't know any physics. It didn't occur to me that this material that Chern and I had developed would find the use somewhere else altogether. This happens in basic science all the time that one guy's discovery leads to someone else's invention and leads to another guy's the machine or whatever it is. So, basic science is at the, you know it's a seed corn of our knowledge of the world.

Speaker 1: What did it feel like at the time you were coming up with it? Did it feel obscure and just like a little diversion and then 10 years later it felt special or did it feel...?

James: Well, it never felt like a little diversion, I really liked the results, I loved the subject, but I liked it for itself, I wasn't thinking of applications. I mentioned to C. N. Yang, who was at Stony Brook at the time, who was a Nobel Prize-winning physicist of great renown. I said I got these things, maybe they'd be useful in physics, because I knew the physicists were looking in the same places using certain mathematical structures, but he didn't bite, and I did not know it would be useful. So I just left it alone. But I was very pleased with the math and then it led to another set of definitions and some math that I did with this guy Jeff Cheeger, which started a sort of a mini-field, although I didn't realize it at the time, it's now called differential cohomology. And so there's a fair amount of people working in that area.

Speaker 1: What does it make you feel, does it become like vindication or what I did was more useful than I thought or what do you think when it gets used 10 years later or by then you'd divorced from it and you were like, do what you want with it, and moved on?

James: It always made me feel very good of course; naturally, you like to think that something you did had far-reaching ramifications. It was nice, but it wasn't – I didn't go to bed dreaming about, ah, now I've revolutionized physics. First of all, I didn't revolutionize physics but I did some stuff there. But sure, it made me feel good.

Actually, in the middle of my mathematics career, which ended when I was about 37 or 38, was that I spent four years at a place could the Institute for Defense Analyses, down in Princeton, which was a super secret government based, national security agency based place for code cracking, trying to break the enemies, whoever it was, Russian, I guess, code machines and cipher machines. I spent four years there in Princeton. During that time, I was working on this – remember I said, it took me six years to work through all this stuff in minimal varieties. I did a lot of it while I was there, but I also learned about computers and algorithms in the code-cracking deal. I was no good as a programmer, terrible, but I was pretty good at coming up with algorithms, and trying to, you know and I found that very exciting to think, oh this might help crack this code, here's an algorithm which could work. Someone else would program it up, then it would run on the computer, and maybe it would work and maybe it wouldn't. And I did one thing there that was quite good. Of course, I can't tell you what it was, it's all classified. So I had a good career there, both doing mathematics and learning about the fun of computer modeling, let's say.

Speaker 1: Was that specialization that you'd had during your PhD, the manifolds, and the topology, was that related to the algorithms, were the two related?

James: No, completely separate.

Speaker 1: Why were you drafted in for that in the first place then?

James: They paid money. I was getting kind of bored, simply being an academic, and also this work, this six-year project was maybe making me feel, gosh, other guys are publishing papers, I am not. But I wasn't thrilled with being an academic, and this place hired mathematicians, a handful. You could do mathematics half your time and the other half of your time you were supposed to work on their stuff. There was no teaching, so it was like, well, all can do is much mathematics as I was doing anyway, and they paid better. And I thought this would be interesting and a nice change, and it was. And I enjoyed it. I did a lot of good math, or that I thought was good, and I did good work for them. But that ended after four years, because I got fired, and...

Speaker 1: You can't just leave it at that. Did you get fired?

James: I got fired yeah.

Speaker 1: Why did you get fired?

James: Well, I got fired because – this was the middle of the Vietnam War, which I didn't like, the war, not that we were doing to support the war in this particular organization, but nonetheless I just didn't like it. And the head of the organization, he was down in Washington, he was a big shot named Maxwell Taylor, General Maxwell Taylor, he wrote an article in the New York Times magazine section of a Sunday magazine, a cover story how we are really winning the war in Vietnam, we just have to stay the course and it's all going to be great. And I thought that's a lot of baloney. So I wrote a letter to the Times saying, not everyone who works for General Taylor, supports his views, and in my opinion, blah, blah, blah. It was a good letter and they published it right away of course because it was unusual that someone writes such a letter in his situation. I didn't hear a peep, no one said anything at the company, they didn't say, you shouldn't have done that, but I was clearly on the watch list.

Speaker 1: When you wrote it, did you know you were something reckless?

James: No. I didn't really think it was reckless because it was my opinion and it really didn't have anything to do with my work. But then, about three or four months later, a guy from Newsweek Magazine came to see me and he said, he's doing an article about people who work for the defense department, who are opposed to the war and he says I have a lot of trouble finding such people but could I interview you. Well, I was 29 years old, no one had ever asked to interview me before, it sounded like, hey, maybe I will be interviewed, okay, you can interview me. So, he asked this and that, but he really wanted to know, so what is your policy.

So I sort of made up a policy. Although it was pretty much true. I said, well, you know, here at IDA, you have to spend at least 50% of your time on their stuff. In those days, it was secret that it was even codes and ciphers, so I just said their stuff. But you could spend 50% of your time on mathematics, and so my algorithm now is, until the Vietnam War is over, I will spend all my time on mathematics. And then after it's over, I will spend all my time on their stuff until the two things match up again, and that was kind of largely true, I was doing mostly mathematics. But I was doing a little of their stuff.

So then, it occurred to me to tell my local boss that I gave this interview. He said, what did you tell him. I said, well, I told him what I just said to you. He said, you did, I better call Taylor, his boss. And he called Taylor and I came back into his office, and he said you are fired. I said I am fired. Yes, you are fired, Taylor fired you; Taylor told me to fire you. So, well, I was fired. I said I don't know how you can fire me, my title is a permanent member, which it was. I started as a temporary member and then I became a permanent member. And the boss was very funny, he said, well, here's the difference between a temporary member and a permanent member; a temporary member has a contract, a permanent member doesn't. I had no contract. So, I was out of there. And it was amusing, I mean, I wasn't – I had solved this big math problem that I told you about, this six-year thing. I knew I was going to get a job very easily. So, I wasn't really worried about that.

Speaker 1: You've honed your skills with algorithms and you've learned a lot about computers, so I am seeing the writing on the wall here for where things go next. How do you progress till you...?

James: Yeah, so at a certain point – then I went to Stony Brook; after this, it was Chair and did this work with Chern, and did some work with Cheeger, and we got stuck. We were trying to prove something, mathematical thing and it was very, very frustrating, worked on it for about two years, we got nowhere. It's okay to work for a long time if you feel you are getting somewhere, but we got absolutely nowhere on this problem. And my father had made a little bit of money and I had the opportunity to try investing it and that was interesting. And I thought, you know, I am going to try another career altogether, and so I went into the money management business so to speak.

Speaker 1: So you started with some of your dad's money and that got you a taste for, an interest in that?

James: Yeah, some family money. And then some other people put up some money, and I did that. No models. No models for the first few years.

Speaker 1: So what were you doing then, you were just using, cunning and just like normal people do?

James: Like normal people do, and I brought in a couple of people to work with me, and we were extremely successful. I think it was just plain good luck, but nonetheless, we were very successful. But I could see that this was a very gut-wrenching business; you come in one morning, you think you are a genius, the markets will fall. We were trading currencies and commodities and financial instruments and so on, not stocks, but those kinds of things. And the next morning you come and you feel like a jerk, the market's against you. It was very gut-wrenching.

And in looking at the patterns of prices, I could see that there was something we could study here, that there were maybe some ways to predict prices, mathematically or statistically. And I started working on that and then brought in some other people, and gradually we built models and the models got better and better and finally the models replaced the fundamental stuff. So it took a while.

Speaker 1: I would have thought, with your background and mathematician, this would have almost occurred to you immediately like you would have straightaway seen this. What was the two-year delay?

James: Well, two things. I saw it pretty early and I brought in a guy who was a wonderful guy, also from the code-cracking place, and he was – I thought together we will start building models. That was fairly early but it wasn't right away. But he got [inaudible 00:27:59] and the fundamental stuff, and he says, the model is not going to be very strong and so on and so forth. So, we didn't get very far. But I knew there were models to be made. Then I brought in another mathematician and a couple more, and a better computer guy, and then we started making models which really worked. But the general – there's something called the efficient market theory, which says that there's nothing in the data, let's say, price data, which will indicate anything about the future, because the price is sort of always right, the price is always right in some sense. But that's just not true.

So there are anomalies in the data, even in the price history data. For one thing, commodities especially used to trend, not dramatically trend, but a trend, so if you could get the trend right you'd bet on the trend and you would make money more often than you wouldn't whether it was going down or going up. That was an anomaly in the data. But gradually we found more and more and more and more anomalies. [inaudible 00:29:11] it's so overwhelming that you are going to clean up on a particular anomaly because if there were, other people would have seen them. So they have to be subtle things. And you put together a collection of these subtle anomalies and you begin to get something that will predict pretty well.

Speaker 1: How elaborate are these things? Because in my head I am imagining some equation like Pythagoras equation, you put a few numbers in and something spits out, but are these giant beasts of equations and algorithms or like are they simple things?

James: Well, the system, as it is today, is extraordinarily elaborate. But it's not a whole lot. It's what's called machine learning. So, you find things that are predictive, you might guess, oh such and such should be predictive, might be predictive and you test it out in the computer and maybe it is, maybe it isn't; you test it out on long-term historical data and price data and other things. And then you add to the system this if it works; and if it doesn't, you throw it out. So, there aren't elaborate equations, at least not for the prediction part, but the prediction part is not the only part. You have to know what your costs are when you trade. You are going to move the market when you trade. Now, the average person will make a – buy 200 shares of something and he's not going to move the market at all, because it's too small, but if you want to buy 200,000 shares, you are going to push the price. How much are you going to push the price? How are you going to – are you going to push it so far that you can't make any money because you've distorted things so much? You have to understand costs, and that's something that's important. And then you have to understand how to minimize the volatility of the whole assembly of positions that you have and be – so you have to do that. That last part takes some fairly sophisticated applied mathematics, not earth-shattering, but fairly sophisticated.

Speaker 1: What discipline of mathematics or disciplines – is it multidisciplinary?

James: It's mostly statistics. It's mostly statistics and some probability theory but I can't get into what things we do use, what things we don't use. We reach for different things that might come, that might be effective, so we are very universal, we don't have any... But it's a big computer model. For one thing, there is a capacity to the major model, it can manage a certain amount of money, which is rather large; but it can't manage an enormous amount of money because you are pushing – you are going to end up pushing the market around too much. So there's kind of a sweet spot as to how much it's reasonable to manage. Therefore, it would never grow into some behemoth which would take everybody out and you'd be the only player – well, of course, you will be the only player, there would be no one to play against. There are limitations at least the way we see it. But we keep improving and we have about a 100 PhDs working for the firm.

Speaker 1: That's what I mean, I mean, how did you get to that point? Did you start to think we need this, we need that, what did...?

James: We just hired smart people. My algorithm has always been – you get smart people together, you give them a lot of freedom, create an atmosphere where everyone talks to everyone else, they are not hiding in a corner with their own little thing. They talk to everybody else. And you provide the best infrastructure, the best computers and so on that people can work with, and make everyone partners. So, that was the model that we used in Renaissance so we would bring in smart folks and they didn't know anything about finance, but they learned.

Speaker 1: What was your employment criteria then? If they knew nothing about finance, what were you looking for in them?

James: Someone with a Ph.D. in physics and who'd had five years out and had written a few good papers, and was obviously a smart guy or in astronomy or in mathematics or in statistics. Someone who'd done science and done it well, and was interested in applying his mind or her mind, although it's mostly his, to modeling markets and making money. But it's a very good spirit; now I've been six years away from the company, so I am not running it anymore, but I am the chair of the board and I go to a monthly board meeting. I think the morale is very good, the spirit is good, and it's really a very good way to work scientifically. It's a big collaborative effort and everyone is happy to see someone else come up with something good, because he, the first person, is going to share in that because everyone is shared in the profits. So okay, you might wish it was you, to show how smart you are but nonetheless, good, he did it, I am going to make money from it.

Speaker 1: I would imagine, lots of people want to be financially successful. Most people want that of course.

James: I suppose.

Speaker 1: And lots of people are good at mathematics and know a lot about computers like at your level I would imagine. Why did you do it? Why didn't someone else do it?

James: I don't know. Well, first of all, some, other people have done it. I think that we are – our firm is better, but nonetheless – I am pretty sure of that – but nonetheless other people have done some very good modeling and so we are not alone. But it's not easy to do, and there's a big barrier to entry. For example, huge datasets that we've collected over the years, programs that we've written to make it really easy to test hypotheses and so on. The infrastructure is exceptionally good. So everything is tuned right. That took years to learn how to do that. People don't leave our firm or if they do, they would leave to just do something else altogether. Everyone has signed a forever non-disclosure agreement and so on, because we are very secretive about what we do, because you can't patent that stuff or copyright it because then everyone would see what it is and someone would just work around it, and say, oh the patent's no good, look I made this twist and that tweak and it's different.

So you can't – what you have is your intellectual property and you have to keep that to yourself. So, yes, we were very successful, continue to be, and I'd say there are others, but most – very few investment operations are a 100% because this is a 100% model driven, it's not 90% or 80%. Some people have models and for advice, and so what does the model say, oh yeah, oh I don't know. Forget that. I don't want to pay attention to that. But Renaissance is 100% model driven. No trade is ever made because someone walks into the trading room and says, hey, let's buy IBM, that's a sure one or anything like that. We got too much Google. We got to short. Nobody does that. It maybe that we had too much Google but nonetheless he might have been right. But, it's just what the model says. And that religious sticking to the model is the only way you can run such a business because you cannot simulate that guy who walked in and said hey, Google is too high, let's sell it. I can just [inaudible 00:37:48] like that, you don't know what might have happened. But you can simulate, you can come up with a model or a new predictor and you can simulate it in the past and see how did it do. So, you have to stick to it.

Speaker 1: I know you guys made the model. So, you do have the ownership of it and feel proud of it, but is it hard to follow the model religiously? Is it hard for your ego to think all the success is because of the computer, like and I just sat there and watched?

James: No. The computer is just the tool that we use to – a good cabinet maker doesn't say it's all because of a wonderful chisel. You may have great film equipment, but that's not why you are a success at doing what you are doing; you are working with good equipment but another guy would make a mess of it with the same equipment. So, no, we don't feel the computer is doing everything. A computer does what you tell it to do.

Speaker 1: Mathematics is very collaborative and things that are learned are built upon by sharing. Are you learning things behind the locked doors at Renaissance that could help mathematics, but you are not sharing because you want to keep them a secret to help the business?

James: No. There's nothing that we are learning behind locked doors in Renaissance that would help the general field of mathematics or the general field of science as far as I can tell.

Speaker 1: Do you think that something about your personality that made you able to develop or is it just luck?

James: Well, I think a lot of it is luck. I probably have a good personality for running a group of people but there are other people who maybe even have a better personality for doing that. We underestimate the role of luck. It's typical that if someone fails at something he'll say I had bad luck. And if he makes success he'll say I was a smart guy – oh, I was just lucky. People don't usually say that if they make a big success. And of course, obviously, not everyone can make a big success, but I think that luck played a role, I was in the right place at the right time, but I really – I think what I am good at is getting good people to work together.

Speaker 1: So, it was more – we think it was more your managerial ability than your mathematical genius that resulted in...

James: Yes, definitely was my mathematical genius. I think, I am a pretty smart guy, I can understand the stuff, but I wasn't – and I did come up with a few predictive algorithms, so that was fine. But, many people did, that's – I knew I wasn't going to come up at all, and so that's why we have all these folks. So, it wasn't my mathematical genius but I think people respected me because I was a good scientist, so they – well, he may act stupid but we know he's really smart.

Speaker 1: You had a few runs on the board I would say.

James: Yeah.

Speaker 1: Given that you will put some of it down to luck, what are you more proud of, all of this in the business or the mathematics from that first half of your career?

James: To the extent that I am proud, I think I am proud of both. I think, yeah, I've done some mathematics and some but it had a positive effect and I guess I am proud of that. And I've built a nice business and I am proud of that, I don't say I am proud of one and the other. And now for the last number of years, I've been working with my wife on this foundation, which she started actually in '94 with her money but nonetheless she started the foundation, and then I joined, I got more and more involved with the foundation as time went on and now that's my main thing, and I am pretty proud of the foundation.

Speaker 1: Let me focus you more on the mathematics versus the business then. Would you trade any or all of your business success for being the man who cracked the Riemann hypothesis or something like that?

James: No, that's a good question. Would I trade that for – well, I probably would trade some of it, I mean, for the Riemann hypothesis? It would certainly, I guess, be a thrill to solve the Riemann hypothesis. I am pleased mostly with the way my career has gone. So would I trade part of it for something? I don't know. I've never looked back and said I wish, at least in business, I wish I hadn't done that or I wish I had done this and not that, whatever it is. I've never looked back that way.

Speaker 1: I guess, the thing I am getting at is what do you define yourself, as a businessman or a mathematician?

James: Well, I would not be very good at running an ordinary business. If I had to run a big manufacturing business or something like that, I don't think I would be very good at it. There's a level of detail that I would find tedious, I am not the best-organized person in the world, and frankly, I would be bored. So, I am not your typical businessman. The kind of business that I did run was very natural to me, so in that sense, this is [inaudible 00:43:31]. I like this as sort of, I like, I always say what's the important mathematics skill in business is subtraction. You have to understand when your revenues are going to exceed your costs. Some people, they just look at revenue and say, hey we are growing. Yeah, but you are losing money every day. Well, that will take care of itself after a while.

But I don't think I'd be – I once had to for about six months run a small business in the computer area, a business that we had started or invested in and then wasn't going well and we had to get rid of the head and find someone new and I found myself running it for about three to six months, communicating to Philadelphia. And I came to the conclusion I am doing a better job than the guy who was there before, but I am not doing a very good job, and I found myself in business meetings like the distributor from St. Louis had some, and I remember saying, what am I doing, spending my time with some distributor from St. Louis. I mean, this is not what I wanted from my life. Finally, we got someone in. He really knew how to run a business and that was his – that kind of business.

The foundation is focused on support of scientific research, primarily basic science but not completely, because we have a large autism project, which involves a lot of basic science but treatments are a goal, but the rest is support of mathematics, physics, computer science, biology of all sorts, neuroscience, genetics, we support basic science and that is what we like to do. There's a certain amount of outreach, we have a Math for America, we spend maybe 10 or 15% on outreach and education. But 80-85% is support of basic science.

Speaker 1: Almost the nature of basic mathematical research is you can't really know where it's going to go.

James: That's right.

Speaker 1: So, what – do you just – are you throwing money at the wall and seeing what sticks or how target [inaudible 00:46:00]

James: Well, to some extent, yes, we have investigated grants which give outstanding mathematicians and physicists and computer scientists a 10-year run where they have support for 10 years, and they can use that money for a post-doc or something like that, to help their work along. We don't know what work it's going to be. We have no – well, we know what the guy has done, but it's not – these are very, very competitive and they are just the best people, and it's helping their careers, whatever they might do. We also have collaborative projects that we support, there, there is a goal, but it can be a rather vague goal like one is origins of life. Well, we'd like to know how we all got here, how did life originate, outside, inside, what was the path to life. So there's, I don't know, 40 or 50 people working on that, and it's a very exciting project, we are making some progress. So, there's a goal, but do I think that in the next five years we are going to know everything about the – no, I don't think so. But we will learn more about this path that started with primitive organic molecules and ended up with an interview like this one. So, there is a goal, but it's a relatively big goal.

Speaker 1: This model of philanthropy, of supporting the basic research and not knowing where it's going to end up, almost seems the opposite of what you were talking about with your business.

James: Totally.

Speaker 1: You were talking about with business, revenues, and costs and making sure...

James: Yeah. Well, yes and no. When we would bring smart guys into Renaissance, we didn't know what they were going to do, we hoped that they would come up with some good ideas to make the firm better, but I didn't say, oh this guy is going to discover, I want this guy to discover this thing, I didn't know what he is going to discover. But it's of course very focused on a narrow set of outcomes whereas the other basic science, anything goes, is very different from that.

Speaker 1: What's your attitude to risk as you almost have created a business that's based on removing risk...?

James: Reducing it.

Speaker 1: Reducing risk, and yet you took a risk while leaving tenure to go and start a business. I mean, are you a risk taker or you are a risk minimizer or were you? You probably need to worry about it, what's it like?

James: Well, I suppose as we get older we get a little less, little more risk-averse, I don't know. I'd always been something of a risk taker I think. But I've had good taste, and especially again, as I say, in people, and have pretty much always partnered up with people. So that cuts the risk. But I did. Certainly, when I started the company, I had to finance it myself, and I didn't know whether it would succeed or not, but I was pretty sure it would, but I didn't know.

Speaker 1: You put a lot of money into mathematics, so you've got some right to comment on it. How are you feeling about it?

James: Oh I think mathematics is really going quite well worldwide, the research end of it. A lot of new ideas are coming up, new fields sort of are flourishing. It feels like a pretty healthy enterprise to me. What's not healthy is the state of mathematics education in our country, that's very unhealthy for young people. That's why we started this thing called Math for America and so on. But we don't have enough teachers of mathematics who know the subject and even of science, and that's for a simple reason; 30-40 years ago, if you knew some mathematics, enough to teach let's say in high school, there weren't a million other things you could do with that knowledge. Ph.D. maybe you could become a professor, but let's suppose you are not quite at that level, but you are good at math and so on. Being a math teacher was a nice job, but today if you know that much mathematics, you can get a job at Google, you can get a job at IBM, you can get a job in Goldman Sachs. I mean, there's plenty of opportunities that are going to pay more than being a high school teacher. There weren't so many when I was going to high school, such things.

So the quality of high school teachers in math has declined simply because if you know enough to teach in high school, you know enough to work for Google and well, they are not going to pay that much in high school. So, how do you redress that, how do we redress that as a country? Well, so we work, a person works for a combination of financial reward and respect, right. So a guy becomes Supreme Court Justice, he's not doing it because he's going to make a fortune, he'll be well-paid, I suppose, but Supreme Court Justice, everyone says... that's a big deal, you have a lot of respect and you respect yourself presumably.

So, there are many – so you can't pay, let's say, high school teachers of math as much as they would get at Google. But you can give them a bump, pay them more, we give people $15,000 a year more than they would make the regular salary. But we also create a community of math and science teachers which they love and it makes them feel important, and they are important. They interact with each other. They are not forlorn, stuck in some high school, no one to talk to. So we've created a community with a greatest [inaudible 00:52:32] and these people don't walk out of the field but turnover is very, very low, whereas ordinarily, the turnover is quite high. So, in Finland, for example, which has very good mathematics teaching, those are real professionals and they have a lot of statuses, societal status, a teacher in Finland. Here, in the United States, teachers don't have such good societal status, and of course, the whole thing of getting rid of these bad teachers, get them out, so on and so forth. Okay, maybe we have some bad teachers. But there's nothing said about, hey, let's reward and recognize the really good ones, right.

So if you are running a business, yeah, you want to get rid of the deadwood if there are people who just aren't doing the job, but most, more important is to recognize the people who are doing a good job and reward them and extol them and make them feel good in one way or another. We don't do that at all with teachers, we are just bashing them and bashing them and bashing them.

Speaker 1: Why has that happened in the United States but not in Finland?

James: Well, it hasn't happened in most of the European countries. Why? Well, for one thing, we have teachers' unions, but that shouldn't be the reason because Finland probably has unions too, I don't know. There's been a shift, maybe it started about 10 years ago that we have to measure these teachers by outcomes. All right, fair enough. But what outcome? Well, we are going to give that student standardized tests. If they have value added and so on, well, then they are good enough; and if they don't have value – well, it turns out judging people on student standardized tests is a disaster, it's a very, very weak statistics, it's not very highly correlated with how they are going to do the next year by the same measure. But somehow, the theory is we have to measure these people; if they are not doing a good job, we have to get rid of them; somehow or other we have to get these people out of the schools. Well, there's an old saying in the British Navy, floggings will continue until morale improves, and that's kind of what this is. It would just be these people up. So, how did it start? I don't know. Someone got the idea that we can measure output, but we can't, or at least we – this is we've imposed the system which stinks for measuring output. And the longer we stick with this system of rating teachers on the performance of the students, on standardized tests, the worse it's going to get.

Speaker 1: And the solution, as you put it, sounds to me like, just respect them a bit more and make them feel good about themselves.

James: Yeah. And recognize and reward the very best ones and that encourages people to say, hey, I could get that, maybe I could rise up and get one of those, become a master teacher or whatever. And someone can even come into the field and say, hey, my cousin is a master teacher, he's having a good life. So, yes, but it's going to take a – we are doing it in New York pretty successfully, but it needs to be all around the country and it needs to be bigger than it is in New York.

Speaker 1: You seem to be a believer in [inaudible 00:56:21]. Is this not something that would correct itself like over time like if the teaching drops and then the [inaudible 00:56:28] mathematicians and they have to improve. Would this not take care of itself? Does it need a [inaudible 00:56:32]?

James: No. Here's how we are taking care of it now. We are importing people, H1 visas, and we are bringing in guys from other countries who do have this knowledge, and all the technology companies only want more and more H1 visas, because they can't get enough homegrown talent, the talent that they need. Now, that's okay for a while, but about 12 or 13 years ago, for example, of the graduates from the Indian Institute of Technology, IIT, about 80% of them left India and would come to Europe and the United States to get jobs. Now, it's 20%, because India is doing much better, there's plenty of technical jobs in India, so hey, I can stay home, I don't have to run [inaudible 00:57:35] I don't have to run to the United States to work. So, well, there are people from other countries who are coming, and we are taking advantage of that, but a day will come when we just won't have enough people here of our own folks who know enough to do this work.

Basic research is, as I have said, the sort of the wellspring of human knowledge about our world, but the federal funding for basic research has become restricted. For one thing, federal funding for science has come down anyway overall; and second of all, there's increasing the tendency for these agencies to fund what's called translational research rather than basic research. Translational means, okay, you are working on cancer, great, we are going to have a cure for this in three years, okay, fine, we will give you money. You are working on how the basic cell is working. Well, the applications are too far away. So, there's less – the Congress pushes the NIH and the NSF too probably too, hey, we want to see some return on this money, we want to see results. So, they are more conservative, wild ideas less often get funded, because – so the government's not doing such a good job at supporting basic science, and so there's a role for philanthropy, an increasingly important role for philanthropy.

Speaker 1: Do you give your money to basic research, because you feel somehow indebted to it for your own success or do you do it just out of like belief or do you feel like you are giving something back to what gave something to you?

James: That's an interesting question. I do it because it feels good. I like science. I like to see it flourish, I like to be around scientists, I like to learn new things. My wife feels the same way. She loves science. So, we are very happy to do this. Do I feel I am giving back? Not especially. I could give back in a lot of ways. There's a lot of things I could do besides support science.

Speaker 1: Do you have a favorite number?

James: 7. Next question.

Speaker 1: Do you have a favorite mathematician?

James: Well, Archimedes and Euler are my current favorites. But maybe you meant somebody... I am very impressed with those two guys.

Speaker 1: Thank you so much for so much of your time.

James: All right. Well, this was kind of fun.

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