Judith: Welcome to Berry's In the Interim podcast, where we explore the
cutting edge of innovative clinical trial design for the pharmaceutical and
medical industries, and so much more.
Let's dive in.
Scott Berry: All right.
Welcome everybody.
Uh, back to in the interim, uh, I'm Scott Berry, uh, at Berry Consultants
and I have a really cool topic for today.
We're gonna talk about, uh, Leonard Jimmy Savage, and we have.
Perhaps the best person to talk to it, who's also, by the way, my
father, um, and weird relations, almost a brother and a father.
But we'll get into that, uh, biological father and an academic brother, uh,
uh, in this, so, so Don, welcome.
And, uh, first question.
Leonard.
Leonard, Jimmy Savage.
I feel like I don't know him.
Can I call him Jimmy Savage?
Should I call him lj?
What, what, what, what was.
Tell us about Jimmy.
Don Berry: So nobody called him lj.
Nobody called him Leonard.
They would call him Savage.
Uh, and sometimes with the English word savage, which I'll mention that.
Um, and, uh, but everybody called him Jimmy, and he wanted to be called Jimmy.
Uh, the way it happened was serendipitous.
I mean, the whole story has really, he, um, uh, bad connotations.
I mean, Jimmy's life was not very.
Good, especially when he was a child.
And I'll, I'll get into that because it matters, it really leads to things, uh, in
his, uh, uh, adult life and his attitude.
But the big thing with him was his eyesight.
And he was born with a congenital defect.
Um, and it was complicated by,
uh, myopia.
So that when he read something, he had to take it up to his eyes
like this, and he turned, and his eyeballs were continually moving.
Uh, it was a very sad thing that when he was born, back to how he
got Jimmy when he was born, uh, his mother went through some bad.
Uh, things associated with the childbirth and wasn't able to pay attention to
things like, what's the name of the kid?
Um, and so, uh, at one point a nurse was, you know, didn't have
a name, so she wrote down a name.
It was Jimmie with an IE and, um, when it came time to name him, um.
There was a tentative name, and then the mother sort of picked the name Leonard.
Uh, and, but the, the nurse kept calling him Jimmie anyway, uh, it
stuck and he liked it so that he was, it's his middle name, uh, but
it was also his nickname and, uh, for reasons known only to him, he liked it.
I, I suppose it was, you know, everybody called him that
when he was a young person.
And so he, he went by that name.
But it was, uh, all his friends definitely called him Jimmy and his
enemies, uh, probably called him Savage.
Scott Berry: Okay, so we should let people know how, how, you know, Jimmy, um, uh,
within this maybe, uh, your relationship and how, how you met him, um, in this.
Don Berry: So how I met him was easy.
I was, uh, a student at, at Dartmouth and I didn't know what I was gonna do.
Um, I had three kids, um.
I was, uh, a math, uh, math major.
And, uh, Tom Kurtz, who, uh, I knew from, uh, the, he built along with John
Kennedy, he built, uh, basic language.
The basic language, developed it.
And, um, he, uh, and so he.
Suggested to me and uh, others in the math department since I was interested in
probability I should go into statistics.
So I applied to various places, uh, and I got a fellowship
at, uh, Yale and, and, uh,
went to Yale.
And I met Jimmy when I walked into his office 'cause he was.
Uh, there, he and Frank Anscombe were the two big names.
They had recently formed the department.
Frank was the first chair, uh, and then Jimmy eventually became,
uh, the chair of the department.
But it was, uh, it was small.
Uh, and so I met him.
Uh, I took a course from him, uh, in the very first semester I was there.
Uh, it was a course out of feller and it was, um, a, a course that
undergraduates could take, but it was also for graduate students.
And so I'll, I'll get into that.
So anyway, that's how I met him.
Scott Berry: Okay.
Awesome.
Um.
Did, did he?
I, I, I.
He, he became your, your dissertation advisor.
It'd be interesting to hear about topics and what, what interesting parts of this.
Did he teach a course using his book, the Foundations of Statistics?
Did he teach a course in that?
Don Berry: Uh, maybe he did at some point.
Maybe he did when he was at Chicago.
You know, it was published in 1954 and.
We're talking about.
I was there in 65.
Uh, so I, he didn't teach a course in his book, um, but it was sort
of obligatory, uh, to read his book and to understand what was going on.
And it was, you know, it was, I, I, I think he's the father of.
Modern Bayesian statistics, uh, how can you argue about that?
Obviously, bays and, uh, so bays is, uh, deserves to be called the
father of, uh, Bayesian statistics.
But Savage, really, Jimmy really.
Made it for, uh, bayesians.
I mean it provided for the first time throughout statistics, it provided for the
first time a rigorous, uh, definition of what statistics, what statistics was that
It was in fact a mathematical discipline.
Uh, and he started out.
Just a, a little bit of that.
He started out thinking that statistics, by the way, he was not a statistician.
He got his degree in mathematics.
He was working in and wanted to work in physics and chemistry.
Chemistry turned out.
You know, that was sort of unfortunate because he couldn't see, uh, and it
was, uh, how he went into the lab.
So eventually he really fell in love with statistics and the philosophy of
statistics, you know, what did data mean?
And to him, he was.
He was a polymath.
He knew everything.
He knew everything about everything.
I mean, it's, it is just amazing and I'll give you a few examples of that.
Um, but he, he knew, uh, about, um, uh, various EE economics,
you mentioned economics.
Scott Berry: yep.
Um, so, so Milton Friedman is, is quoted, and you know, I, we never know if quotes
are right, but is quoted as saying he's one of the few people I've met.
I would, he unhesitatingly call a genius.
Was what Milton Friedman said of Savage.
Don Berry: Yeah.
And David Wallace, um, and, you know, everybody that, uh, knew him deeply,
thought exactly the same thing.
I mean, it's hard to to know him.
Without thinking, he has to be the smartest person in the world.
'cause he seems to know everything and with, with a depth.
That is phenomenal.
So, um, so let me give you an example of that.
And I'll give you an example.
It's a, it is kind of a trivial one.
Uh, I mean, I love to work with him.
We worked on some examples in science that of course he knew about the
science and he brought me into it.
Um, he, his childhood had some ops.
Mostly they were downs.
Uh, we know about his childhood mostly because of Richard.
Savage, who was his brother, younger brother by eight years or so.
Um, and so Jimmy was Richard's father.
Um, uh, Jimmy's father was instrumental in making Jimmy's life
good and was a, a, a big positive.
Um, uh, the rest of his childhood was pretty bad, but, uh, his parents
bought him an encyclopedia when he was very young, and he read it
and, and he, he remembered it.
Uh, just a very simple thing about that.
Uh, the main thing I want to tell you in a minute.
Uh, so he one time said to me, he said.
Uh, Don, uh, uh, Jean, his wife Jean, um, is writing a project on Finland and
she's looking for celebrities in Finland.
She can't find many celebrities.
Do you know any celebrities in Finland?
Uh, from that they Finn and I said, I think Victor Borger is a, uh, fan.
And he said, no, Victor Borger is.
Danish.
How did he know that?
Do you know what vi Victor Berger does?
You probably don't know.
He was, he was, he was a celebrity.
Uh, he was a pianist.
He was a comedian.
And he had a piano that joked, I mean, he worked with the piano and various jokes in
the piano, and he was really clever and, uh, talented, uh, but not very well known.
And he appeared in the TV shows, like, uh, ed Sullivan show some,
you know, uh, things like that.
Uh, and, uh, but he wasn't really very well known.
Then a little bit more known than he is today.
Uh, so how did he, how did he know that he couldn't watch television?
I mean, he couldn't see.
Um, and his, uh, childhood, just, I mentioned Richard.
Uh, Richard, uh, said, um,
that, and I wrote this down.
He was a brilliant child.
But he paid no attention to what was going on in school because he couldn't
see what was going on in school.
Um, and his teachers said, you can't go to college.
I mean, she put in, they put in really negative things about him.
According to Richard, uh, the prevailing wisdom in the school
was that he was feeble-minded.
Scott Berry: Wow.
Don Berry: it was, and, and it was a very bad thing for his.
Um, uh, emotional, uh, circumstance, but so something that involves you, Scott, um,
mom, your mother, um, was pregnant with you and she would go to the doctor,
you know, regular visits to the doctor.
Uh, you have three older brothers.
Um, and the doctor would inevitably say, you're due for a girl.
she would tell me that, and I'd say, no, you're not due for a girl.
Uh, and I, I mumbled things about, you know, uh, um, what the probability was,
and I was really interested in that separate from my own personal interest.
You know what?
How do you do this?
How do you find the probability that the unborn is, is going to be a boy?
And this was in the days PR prior to amniocentesis and any of these.
And it used to be that people would say.
Um, you know, pregnant woman would come into a room and would, would meet somebody
and they'd say, oh, you're carrying low.
It must be a, it must be a boy.
Uh, and you know, it was really these, uh, sort of old wives
tales thing.
But, so I was interested in, in, in, in pressing your mother with
something that was, uh, uh, legitimate.
And so I said.
Uh, to Savage, I said it, it's silly to say, to use a maximum likelihood estimate,
which is one I know it's not one.
Uh, so how do I do this?
How do I find a prior distribution, let's say I'm willing to accept
that, um, my wife and I have the same probability forever, uh, of, of a boy,
but it varies potentially between Madrs.
Um.
And, uh, so let's assume exchange ability.
Um, and, uh, but, but how do I find a prior distribution?
And so he got up and went to his bookcase and pulled out a book by
Cardo Genie, C-O-R-R-A-D-O-G-I-N-I, who you may know if you're an economist.
Um, about the Genie coefficient, which is used in, in, in,
especially in the economics.
Uh, and Genie turned out to be also a demographer.
So I looked at the book and in the back of the book in the last half
of the book or so, was amazing data on families throughout the world.
A Australia, uh, Africa, the United States on family size and number of boys.
Scott Berry: Wow.
Don Berry: And it was truly amazing.
I mean, when you looked at it, it was absolutely clear that it was not binomial.
That was absolutely clear.
Um, and what.
What what was clear about it was there was a,
Scott Berry: a,
Don Berry: um,
a, more It, it,
Scott Berry: effects.
Don Berry: it could have been, uh, it, it, it, it could be, it looked like
it was a, um, uh, a distribution of, of p and a beta binomial, and, but.
There was a, an interesting aspect to that, uh, some sort of an
added effect that if you looked at families with, I mean these number of
families, number of, uh, of children, the family went up to like 18.
Uh, there wasn't very much data at 18, but let's say you went up to 12 and, uh,
the proportion of boys and families of 12.
Uh, that was one that is, all of them were boys, was bigger than the
corresponding proportion for 11.
Scott Berry: Yeah.
Don Berry: you know that in a binomial it's, it's 12
times in the other direction,
so something is going on.
I eventually learned.
That, uh, there are some women throughout the world and this was, you know,
constant, uh, throughout the world.
Uh, some women who cannot carry a male fetus.
Uh, and so that would explain a point mass.
So it was a mixture of a point mass and a beta was fit pretty well.
And I calculated that the probability you were going to be a boy was 57%.
Uh, based on that prior probability, of course it went,
it went up for the next one.
And, uh, uh, mom and I had two girls.
Uh,
So,
Scott Berry: what what's fantastic about that is, is first of all that somebody,
he knew exactly what book to go to.
He remembered that what, what I, what, what's amazing is I, I kind of thought
maybe he's a pure theoretical guy with the foundations and he came up with this
amazing axiomatic approach to probability.
But he worked in economics.
Apparently he has some, uh, uh, some aspects that maybe he brought
brownie in motion to asset pricing.
He worked with Milton Friedman in World War ii.
He worked with John Von Neuman as a mathematical statistician.
I remember Edwards Lindman in Savage where it was behavioral sciences
he worked in, sounds like he was actually an, an applied statistician
despite being a genius mathematician.
Don Berry: Yeah.
And he, he got his, he got his rocks off by looking at, um, uh,
uh, science and applying statistics.
I worked with, we used to have, uh, we had a, uh, a, a fish.
Um, expert that wanted some help and he knew everything about the ocean and about,
uh, you know, a diurnal effect and, and the like, and was really interested in it.
We did another thing where I remember it was really neat.
We did mathematics along the way.
It turned out that the, uh, the, along the route we saw that, uh.
Uh, the, what the integral equation was that you had to solve in order to
do this thing, but it was about a real application, like this thing, um, the
distribution of of gender in, in families.
Scott Berry: Yeah.
So I, it it, was he interested in clinical trials?
I mean, you went into bandit problems, you talked about the
bandit problems in clinical trials.
Was he interested in clinical trials and how did you, how did
you come to the topic with him?
Don Berry: So, um,
it, it was my topic.
Uh, he was not.
Into clinical trials.
I mean, he was sort of like fisher in that way.
Fisher was amazing with his, uh, agriculture stuff.
Um, he was not into clinical trials.
He, he became interested in clinical trials because of me.
I mean, this, he was interested in strategy.
You know what, uh, his other most famous book is, uh, how to Gamble if you Must.
Where, uh, it was based on a utility function that's
different than a typical, um.
Namely, there's an amount of money that you really want and need
to buy an airplane ticket to go home and nothing else matters.
And so it's utility, one above that and utility.
And then there are some strange things that happen, uh, in the strategy and
in particular, bold play is optimal.
Um, and so he took this seemingly small problem.
And, uh, built finite adaptivity.
It made it show why you need finite adaptivity and why it's more important.
Um, and so it was, and, and that was not unusual, where at the end of the road
was a revelation about the mathematics.
Uh, so, uh, anyway, he was, he was, um, not interested in clinical trials.
The one person who was was Frank anco.
Scott Berry: Ah.
Don Berry: so my initial, when I, where did the Bandit problem come from?
From my perspective, I took a course in the management, uh, uh, department.
It was like the business school at Yale.
Uh, and there, uh, I came across a problem.
It was kinda like.
The one arm bandit where you, uh, ask yourself, should I keep playing this game?
Should I keep pulling the one arm?
Um, and so, uh, you, but you learn over time and you learn, uh, you know, when
you've, when you've had enough and you're convinced that you're never gonna win
at this, so you go to do something else.
Uh, and so I.
I said, you know, the two armed version is even more interesting.
And so I started working on it, did some dynamic programming,
did some computations, and I really became, uh, interested.
Um, and so I got Savage interested
Scott Berry: Did, did you think about ANCO as an advisor?
Don Berry: I did.
And I, I, you know, I, I, I thought he was, uh, an amazing mind.
I thought he was, um, a, he was an amazing mind, and a, and a great person, and
a, and a wonderful person to work with.
But I was, so,
so one of the things I said, uh, in my comments about,
uh, Savage, it, it, I said.
It was like the world around you.
When you're with Savage, the world around you is tingling with intellect and it,
it, it, it, that was so attractive.
And so, I mean, I just, and I knew he was interested in strategy
and I knew that this problem would really interest him.
I didn't know the, the name Bandit.
Eventually I knew that it was called Bandits.
It's a very hard problem.
Uh, suffice to say that the computer scientists call it NP-hard Uh, suffice to
say that, uh, Peter Whittle, who was uh.
Uh, uh, worked in World War ii.
It was a, a statistician, uh, worked in World War II on, uh, strategic things.
Said that, um, they talked in, in the UK about out making a one
sheet description of the bandit problem and dropping it on Germany.
So that the mathematicians would get hold of it and waste their
time trying to solve this problem.
Scott Berry: Uh,
Don Berry: Uh, so it was really hard.
Uh, but it was, so I thought it was, I was doing clinical trials
Scott Berry: yeah.
Don Berry: and um, of course I wasn't.
And that was the, you know, that's the rest of my story.
Um.
So the, what else did I want to
Scott Berry: So, so, uh, that, interestingly, it ties into the, my
comment earlier of, uh, academic brothers.
So, Jay Cade was part of your committee, uh, within this Jay Cade.
Uh, interestingly, Moy DeGroot was a student of his, and Jay
Cade went to Carnegie Mellon.
Uh, I went to Carnegie Mellon and became a student, uh, of j Cade,
and he was my thesis advisor.
So I I, in some ways I'm almost your academic brother in this and, um, my
daughter Lindsay, um, interestingly her last two choices were CMU and Duke.
And if had she gone to CMU, she may have been j Kade student, and so it, it
may have been that much more twisted.
And interestingly, Lindsay.
Was an honorable mention for the LJ Savage Award for her
dissertation In Bayesian statistics.
Don Berry: It's wonderful.
Scott Berry: Yeah.
Don Berry: So I, I, I have to mention, um.
The dissertation and, uh, Savage's role in it.
He was, I showed him my first draft.
My first draft had only the beta distribution and I had some really
nice theorems, uh, when the prior distribution is the beta distribution.
It was 10 pages long.
And he said, well, this is fine.
This is a dissertation, but let's try a little bit more.
And let's try to, you know, understand more generally.
And so it ended up going through five drafts and I tell you that every single
one of them, he looked at every single word he recorded what he was doing.
You have to understand it's a little bit delicate 'cause you
know, of his writing and, and.
Uh, eyesight.
Uh, but he would record his, what he was reading and explaining to me.
He taught me how to write.
Scott Berry: Wow.
Don Berry: Um, and it, it, uh, it, it, it sort of carried over.
I mean, you've learned a little bit of things from me for writing,
and it's, it comes from Savage.
Um, it was, the end was so well written.
That when I submitted it to the Annals of Mathematical Statistics, it was
published in the last year of the Annals of Mathematical Statistics.
After that, it split into two Annals of Statistics, annals of Probability, uh, and
it, the associate editor was Tom Ferguson, a very famous decision theorist, and
Ferguson didn't send it out for review.
He read it himself, he approved it himself.
It was published 27 pages and there were no revisions at the journal level.
Um, and it was J it was, it was impeccable because of Jimmy who was.
Uh, uh, he was a perfectionist.
It was his downfall.
Some you mentioned, uh, some other people that might not have been.
So, I mean, I loved Savage.
I mean, he was like a father figure.
He was a father figure for me.
Um, and I'd have done anything for him.
Um, uh, but other people had been in conversations with him that.
Uh, we're not all that positive.
I know Herbert s Shernoff, who I think is an amazing person.
I, and I love him too.
He's still alive.
He's 101.
Um, and, uh, so I, I said to him he's interested in, in sequential things.
Uh, he was Jake Kaine's advisor,
Scott Berry: Yeah.
Yeah.
Don Berry: um, uh, and, and.
Uh, so I, I was ex we were talking about things, mutual interest
and, and sequential things, and he was interested in bandits.
I don't, uh, turn off that is, I don't think he ever published on Bandits,
but he was very interested in them and he had done some calculations on them.
So the subject came up of Jimmy Savage.
And so I mentioned, you know, something about Savage and he said some very
disparaging things about Savage.
He and I said, well, but Herman, he, he was human and Chernoff said
he had some human characteristics.
So it, it, it, and, and, you know, everything comes back to the.
The, the eyesight and the way he was treated.
Uh, let me just mention, and I said I was gonna say something about Bill Cleveland.
Uh, Janice Cleveland was his wife.
We had a party one night at, at, at, uh.
Uh, Savage's house and, uh, there were the, the, the women, the, the
wives mostly, uh, were talking.
Um, and Jimmy came up and, uh, entered the conversation and, uh, uh, Janice
Cleveland said, uh, uh, professor Savage we're talking about children and, you
know, what are the good ages for children?
What's your favorite age for children?
And he said, your age.
And now people laughed.
But it was real from his, it was from, he was serious from his experience.
Uh, he didn't want to be exposed to these people that had really
done some, uh, nasty things.
I mean, you can imagine the bullying and the like.
Uh, so it's a Sergeant Freud.
It was a, a very good story.
Uh, he died too soon.
He died at age 53 from angina.
Scott Berry: Yeah, it was, it's incredible all he accomplished to work in the war
and economics and the, the, the two books, uh, the students he had and he was 53.
Uh, it's unbelievable how much he accomplished in that time.
Don Berry: yeah.
Scott Berry: Yeah.
Don Berry: I, I, I haven't told you.
Maybe we can do another one.
Uh, can, can I tell you about, um, my experience with, uh,
teaching and, and, and with Savage?
Uh,
Scott Berry: let's end it with that.
So make it a bang.
Make it a bang.
Don Berry: Alright.
Here's a bang.
So, uh, I had experience with.
With, uh, Jimmy in his class, we, I, I took where he said,
if ever I don't show up.
To class.
He told me, he said, you take the class and, and uh, just, uh,
you know, uh, continue to where you think we should be going.
So I was hoping and hoping that wouldn't happen, but one day it did happen.
Uh, and so I got up and I said to the class, which was mostly
undergraduate math majors at, uh, Yale.
Uh, so we had homework last night.
Anybody have questions about the homework?
And, uh, there was a question and the guy asked, uh, really the
hardest question in, in the homework.
Uh, and it had to do with the acidotic, uh, tail of the normal distribution.
And so I showed him how to do it.
I started to show him how to do it, and then Savage came in and, um.
I went up and I handed the chalk to him.
He said, no, you continue.
So I continued and uh, at, I showed him how, how to do the problem, but
then I related the problem to some real things and this impressed Savage.
Um, uh, and he then asked me, uh, if I would be willing to teach a course.
Uh, at Albertas Magnus College, a Albertas Magnus College was in New Haven.
Still is.
It was, uh, all girls co-ed now.
Uh, but they had a statistics course that had not, um,
Scott Berry: um,
Don Berry: uh, they lost the teacher and so they asked Savage if he could
provide somebody that would do this.
And so he asked me if I would do it, and he said it's $400.
This is a whole semester for $400.
Uh, uh, and I said, sure, of course.
I, and, you know, in part because of Savage.
Um, and then I bought a car, um, a 58 Chevrolet with the money.
And, um, I would give Savage a ride home, uh, with, with the car.
And so one night he got into the car and he said, so how's Big Al?
I said Big L.
Who's Big L?
And he said, Albertas Magnus.
The car, the car's name is Big L.
And so that's part of the story.
But he, and then he kept calling it Big L but then we had a very
famous guy, and you've probably heard of him, um, Fred Mueller.
Scott Berry: Oh yeah,
Don Berry: Uh, I think it was Feinberg's, uh, uh, advisor,
uh, very famous guy.
Bayesian didn't write a lot about Bayesian.
He did the Bayesian thing of the Federalist, Federalist papers.
You know, the authorship, the Federalist paper, uh, uh, that was
Bayesian and that was, uh, him and, uh.
Uh, somebody else.
Uh, anyway, so Fred Moeller visited and, uh, lots of people visited, uh,
new Haven when, uh, Savage was there.
You know, Dennis Linley, um, George Barnard.
Uh, uh, uh, Jerry Kornfield, I mean, it was a mecca to go to.
Um, and so, uh, when he was going back, Savage asked me if I would take
him back to the airport, and he wanted to go along with, because they had
worked together, he and, and Ello.
So the.
Scott Berry: in the car with Jimmy Savage and Fred Mosell is
they're, they're, they're chatting.
That's awesome.
Don Berry: It's chit chatting and they started to talk
about some scientific problem.
And the, i, I, I was lucky on that, uh, thing where I talked
about the asymptotic distribution, the asymptotics of the normal,
uh, I.
Scott Berry: Mill, Mills ratio I'm sure came into that, but, but, but, but, yep.
Don Berry: but here I was lucky too.
Uh, they were talking about the scientific thing and I kept, uh,
adding things and commenting on what the scientific problem was.
And so, uh, Jimmy says to me, Don, how do you know so much about this?
And I said, well, I, I read the article in the science encyclopedia
in the stat library, and his eyes lit up and he was delighted.
He said, you know, I, I worked so hard.
I had to convince.
Yale to let me buy those, that thing.
And so what you've said now makes it all worthwhile.
That it is a useful thing and it, it hearkens back to the
encyclopedia that he read.
You know, I mean, he was really.
A human encyclopedia, but that's too technical a thing.
I mean, it isn't, he wasn't technical.
He was brilliant and innovative and, uh, uh, uh, I'm, I'm, I'm, I'm glad
we had him in the Bayesian world.
We probably wouldn't exist, Scott, if it were not for him.
Scott Berry: So Berry consultants, uh, has a lot to thank for,
for, uh, Jimmie Savage.
And so let's end it with, uh, three statisticians and a 58 Chevy named Big Al.
Alright, so, so Dad, thanks a lot.
Appreciate it.
Until next time, in the interim.
Don Berry: Thank you.
Thanks everybody.
Bye.