[Complex Systems Approaches: Day Two]{.mark}

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Female Speaker:

So, similar to yesterday we're going to have our three morning speakers
make their presentations, and then Dr. Michael Wolfson, who is the
Assistant Chief Statistician, Analysis and Development at Statistics
Canada, will moderate the question and discussion session, which will
run from 11:30 to noon. And our first speaker today is Dr. Scott de
Marchi, and he is going to provide the view from political science.
Scott is an Associate Professor of Political Science at Duke University,
and Scott's research focuses on the fields of computational political
economy and other mathematical methods, individual decision-making, the
presidency, and public policy. At Duke, he teaches a class on the nature
of freedom, and he's written a book on the foundations of mathematical
methods in the social sciences entitled Computation and Mathematical
Modeling in the Social Sciences. So, I'd like to welcome Scott. Thanks.

Scott de Marchi:

Hi. I \-- my training, like a lot of people, is a little bit
idiosyncratic, so I'm not going to be able, like some of the speakers
yesterday, to say a lot about political science directly. Most of my
life was spent in a computer science department and I left, by and
large, because computer science, and I think this is one of the signs of
the decay of a discipline, turned to formalization, increasingly. There
was a battle, and that's not really overblown, between the people that
wanted to solve problems and the people that wanted to work on math. And
a lot of times those are intention. And yesterday, if you saw the talks,
I think the same claims are more or less made by a lot of the speakers
about current microeconomics in economics departments.

I switched, believe it or not, to history for a couple of years, and the
same battle's being fought but had already been lost. Most of you will
notice that history departments are a little bit different than they
were 20 or 30 years ago. And the people that wanted to study sort of,
you know, wars and diplomacy were losing out to people that wanted to do
something very different. And so I've switched now, again, back to more
mathematical stuff. And I'm thankful that you folks invited me to talk
because I think the computational crowd generally, in some ways, needs
you more than vice versa. If we don't do applied work, and it's going to
be one of the threads of my talk, I think bad things will happen. And
bad things have happened to other fields and other fads in methodology.

So, a couple of things I would like to ask your tolerance for. I'm going
to run over a brief, you know, story from history. And then I'm going to
switch to games, toy models of the sort that Scott Page was talking
about yesterday. And like a lot of you, I'm going to base this on
statistics. I would guess, given your training, that most of your time
is spent studying statistical models, and it turns out that even though
I do computational work, about 80 percent of my time is spent either
teaching econometrics, which is what you folks call biostatistics, or
doing statistical work myself. So I'm going to make the assumption that
stats is the lingua franca of everyone in the room. If that's wrong,
it's too late.

\[laughter\]

So how did we get here? History, believe it or not, used to be the sort
of parent of all of the social science disciplines: economics, politics,
sociology and the rest. And they made claims that are not so different
from some of the claims that were made yesterday by the epidemiologists
in the room. It started off in a bad way. In the 19^th^ century, you
probably know there weren't very many colleges or universities. They
didn't do research. They were finishing schools. Some might accuse
schools of Duke like being finishing schools, given the tuition costs
and the people that go there, but we're not.

But research was not the first goal; religion was the first goal.
Inculcating gentlemen with the right values was the second goal. And a
lot of disciplines wanted to professionalize. And professionalization,
ultimately, you know, meant math, believe it or not. But if you look at
the people at the turn of the century when they started to do sort of
theory work, they do not sound so different from you. Frederick Jackson
Turner is one of the most famous historians ever, and if you read this
quote, he's talking about systems theory: "Up until our own great day,
American history's been in large degree the history of the colonization
of the Great West. The existence of an area, free land, its continuous
recession, and the advance of American settlement westward... Behind
institutions, behind constitutional forms and modifications, lie the
vital forces that call these organs into life and shape them. The
civilization in America has followed the arteries made by geology, blah,
blah, blah, blah, blah."

He had a theory, though, and the theory was kind of neat. The theory was
as long as we had a frontier, you know, westward territory, we were
going to avoid the perils that had beset Europe. So we wouldn't have
urban strife, we wouldn't have a lot of the problems that people had in
Europe. And he had a frontier theory, and this actually guided policy
for the better part of 70 or 80 years in American foreign policy. A lot
of people wanted to push us on a natural science model, us being
historians. So Henry Adams, Herbert Baxter Adams, they could read
Darwin.

And one of the things that's hard to remember about this time period is
social scientists could read natural scientists, which is not so much
true anymore, by and large. And they based a lot of their models on
evolutionary models. Herbert Baxter Adams, for example, advanced the
Teutonic seeds theory. I imagine no one's heard of it, but it's the idea
that everything good in German \-- in civilization comes from sort of
German Teutonic seeds that sort of percolated through England and then
ended up on our shores. And they were, you know, claiming they were
doing evolutionary work.

Things went south. And the moral of the story is things can go south
surprisingly quickly. By 1931, the interwar period, Becker, who was one
of the presidents of the American Historical Association, wrote this:
"It must be obvious that living history, the ideal series of events that
we affirm and hold in memory, since it is so intimately associated with
what we are doing and what we hope to do, cannot be precisely the same
for all at any given time, or the same for one generation as for
another. History in this sense cannot be reduced to a verifiable set of
statistics or formulated in terms of universally valid mathematical
formulas. It is rather an imaginative creation, a personal possession
with each and every one of us, Mr. Everyman, fashions out of his
individual experience, adapts to his practical or emotional needs, and
adorns as well as may be to suit his aesthetic tastes."

This is a cliff, right? In terms of a discipline that thought it was
doing a natural science model, in about 40 years, you end up with this,
and it keeps going. The next \-- you know, skip a president, Beard
writes more or less the same thing. And if you think about it, the
question is what happened and could it happen to us in the social
sciences? There was a brief consensus after World War II, driven by and
large by the government wanting historians to be useful for a while. And
the interesting thing, for my story at least, is they didn't agree on
what the end of history was, that they were doing causal work. There was
significant disagreement about whether or not you could do causal work.

They did agree, though, on method, okay? And historical method, or
historiography, as it's called, is a little bit wacky. It's mostly
getting the facts right, and it's hard to overstate how much they cared
about facts, but the training of a historian is you get two years of
coursework, they send you off to an archive, and they give you some sort
of nice advice, like bring Kentucky bourbon, since they usually can't
get that for a cheap price, to bribe the people in the archives to let
you see the good stuff. And make sure you have cards that are all the
same size so you can put them in a nice little box to bring home with
you and have a record of what you actually looked at in the archives.
It's not very sophisticated in terms of methodology. But they did care
about it, which may seem odd to you.

And I want you to tell the \-- a story about David Abraham and the end
of history as we know it, and how at most universities, history went
from a social science to a humanities department. David Abraham was a
fellow who, not so long ago in the '70s, got trained in German history.
And in German history, there's a debate, and it's an interesting debate
about whether the Germans are evil, you know, innately, or whether the
Nazis were just bad and hoodwinked them. And this matters at the margin
\-- I don't mean to make light of it. And the reason it matters is if it
could happen anywhere, that indicates we should all be worried. Or it
could be unique to the Germans, in which case we don't so much need to
worry. Okay? So it's an interesting historical debate as far as
historical debates go.

Abraham was a socialist and he thought that German industrialists were
complicitous in the rise of Nazi party to get war profits. So it's, you
know, a military industrial complex story, with sort of a Marxist
crunchy exterior. The bad news is that there were a bunch of famous
historians who didn't agree with this story. And Abraham, like a lot of
young people going to the archives, made a bunch of errors in his book.
It was accepted at Princeton, it was up for an award. He got a job at
Princeton, a tenure-track job, and the older historians who didn't much
care for him sent their graduate students to the archives to replicate
his work.

Now, have any of you ever looked at a history book, academic history?
About each page, half of the page is footnotes. There's very little text
in these monographs, mostly they're footnotes. And this is, you know,
privileging the fact above all else. And they found two or three hundred
mistakes in a four or five hundred page book. So for them, the
antagonists, that was the end of it. He'd basically been falsified and
he was malicious in their view. So they began a phone call, you know,
campaign to drive him from the discipline. They got him \-- his book
taken off the press. He was taken off of the list for the award, and he
was ultimately driven from the discipline. He's an investment banker
now, or is he a lawyer? He's one of the two. Something that makes the
story, you know, make sense in a cosmic way.

\[laughter\]

But the \-- again, the interesting thing for our purposes is that one of
the journals actually took the time to print a list, like Excel
spreadsheet format, of all of his errors and then a committee evaluated
whether the error was neutral to his argument, helped his argument or
hurt his argument once it was corrected. And the crisis for history, and
again, I can't overstate this, is that people realized that this was not
of itself sufficient to prove that something was false. You could make
errors but your theory might still be right. And the converse is that
you might make no errors and your theory could ultimately still be
wrong.

So they actually had a crisis of method, and history's changed a little
bit. Natalie Zemon Davis is an historian at Princeton and is famous by
their lights, and method has changed. If you look at the brief quote,
you probably \-- some of you are not old enough to have watched this
movie, it was "The Return of Martin Guerre," it had Gerard Depardieu in
it. I don't even think anyone knows who he is anymore, but her claim
about her historiography and method is distinct from the natural science
model was, "Watching him, the actor, feel his way into the role of the
false Martin Guerre gave me new ways to think about the accomplishments
of the real impostor. I felt that I had my own historical laboratory,
generating not proofs, but historical possibilities."

So the brief story is this is about a man who in the 15^th^ century
disappears for a war in France, and someone comes back and claims that
he is this man and is the husband of the wife that was left behind. They
hook up, and it's discovered that, after the fact, that he's an
imposter, and in that time period, that's a hanging offense. They
actually did kill the guy. That's not so good in that time period. But
instead of going through the archives and the documents and all the
rest, of which there are not many from the 15^th^ century for peasants,
they did not write a whole lot, it turns out. She looked at a movie
production that she was a consultant on and used that as her historical
laboratory. That's a different kind of history, obviously. It's clearly
not the natural science model, and most historians are self-identified
as members of the humanities these days. They're not social scientists
anymore.

So, as with all fads and fashion in methods, the question is can
computational science help you? And unlike \-- like Scott Page, I'm
going to present you a couple of toy models today so you can think about
these issues. I think that's a good way to go. Unlike Scott, my parents
were not as nice to me when I was a child so I'm a little more
pessimistic than he is. I think there's enormous opportunity for
computational modeling to help with big questions. I also think that,
like a lot of fads and methods, that there is some chance that it might
disappear, so we'll see. But the bottom line is, we have to do applied
work.

So, political science \-- this will be about all I say about it. The
good news about us as a group is that we are very ecumenical and we
study behavior, which is probably of interest to you. We have, you know,
people doing fMRIs, we have social psych people, we have a lot of people
who increasingly are good at pure statistics or applied statistics and
do bargaining models and computer science. So my training is not really
all that idiosyncratic in one sense. And we do study most everything,
from ethnic violence to interstate conflict to individual decision
making, from elections to all sorts of public health concerns and public
policy.

And like you, we study statistics, by and large. It's the language that
everyone uses to convey results, and we do a lot of hand wringing about
it. I don't know how much hand wringing you do, but the causation
question and work like that of Judea Pearl and computer science, we
actually read that stuff and take it seriously and worry about
correlation versus causation, and you know, again, lots of hand
wringing. And unlike history, we would not study something like the
causes of World War I. We don't care. There's no such thing as World War
I. We'd study war. If you can't make it a general class, you can't do
statistics. If you can't do statistics, we don't so much care about it.
So that is where political science is currently.

My first example is taken from Scott Page. He didn't use it, so I'm
going to. And I want to briefly, even though it was done yesterday \--
I've cut a lot of this last night to try and not be redundant \-- talk a
little bit about how we do things and how our methods are different. We
have three main schools. We have the applied stats group, which you will
understand. The deductive group, which was talked about yesterday,
mostly in negative terms, and it's fortunate we don't have members of
the econ department that would rush the podium today. And the
computational perspective. So here's the problem. Has anyone ever been
in a crowd and you wonder whether or not you should stand and ovate at
the end of it? Has this caused any of you any angst? It might. It's
something you might want to think about. The thing you want to be clear
on, you do not want to be the only fool standing, clapping wildly. Okay?
For most people that would be humiliating.

On the other side, you do not want to be the only person sitting in your
seat like the Grinch, okay? So I'm going to actually sound game
theoretic for a moment here. You have a utility function. And your
utility has terms for things like not being humiliated, okay, we can all
agree on that. The question is how do you study this problem? The one
approach that you'll be most familiar with is the applied stats
approach. You would come up with the dependent variable, okay? It could
be the length of the ovation, it could be whether or not there is one,
and then you do a logit to probit model. It could be how many people are
standing so that you get a continuous variable with more variants, woo
hoo.

You'd ask the usual questions of all the people that are in your
observation: demographics, performance type, audience size. You'd run a
model, and you'd have the usual problems. Your data would probably not
be IID, right? Performances might have inter-reactions between them, you
know? There's buzz, positive buzz or negative buzz. There might be
interactions between people in the audience, so if your unit of
observation is an individual, they sit together and they don't come in
individually, they're not independent. People don't talk about non-IID,
it lurks behind a lot of models and it's kind of an ugly problem. But we
do like sample bias and multicollinearity, and you know, all sorts of
other stuff. And we would talk about that in our model. And the
information we would get would not be valueless. We'd actually come up
with a model that could more or less predict, possibly, if we're
interested in out of sample work, the incidence of whether or not
there's going to be a standing ovation or how long it's going to last,
and the like. Okay? So the information is not useless, and this is the
kind of work that I would say about 80, 85 percent of the social
scientists spend their time doing, okay?

Game theory's a little different. Game theory, we would actually write
down a utility function, and we'd write down the sequence of moves that
are available to you. And in this case, it's pretty easy. Your utility
function is probably to avoid humiliation and to reward a good
performance. And the sequence or strategies available are you can stand
up or you can sit down. It's not rocket science. What might be rocket
science is how you would come up with an equilibrium play, and backwards
induction, as was noted yesterday, is the sort of tool of choice. I
don't think this should be as privileged as it is. There are claims made
that backwards induction is rationality by a lot of social scientists.
That's obviously goofy and wrong. But backwards induction is an
algorithm, it does solve some problems, and most problems are put in the
form of a tree, so backwards induction works.

But as noted yesterday, there's no dynamics, there's no knowledge of
what happens in this equilibrium. The games are brittle, there's no
equivalence classes for games. If I have a game for standing ovation and
someone else does, there's no correspondence between them, they're just
different. There's no way to say that they're kind of sort of similar.
And usually, questions like how does everyone start standing up? If we
all have this in the back of our heads, not wanting to be humiliated,
would anyone ever stand first in a deductive model? It's actually tough,
so the question is, how do you get it to happen? You probably have some
exogenous parameter for, you know, a move by nature. How good the
performance is. And then some people would have a function that they
respond to that and stand up by force, more or less, and all the
interesting work in the model, the standing up and then the sitting down
again \-- which is equally problematic. When do you stop ovating? You
don't want to be the first person, right? All of that, you know,
problematic stuff is swept under the rug of exogenous parameters. A move
by history. So I find this deeply unsatisfying even though, again, I
spend a lot of my time doing deductive work of this kind. Okay, so
that's game theory.

And one last note on that. Deductive work does, you know, work in the
sense that we do have some really, really strong results that are really
general. For example, all of you who believe in democracy, you should
Google when you get home the Arrow result, A-R-R-O-W, Kenneth Arrow, won
a Nobel Prize for it. But democracy doesn't work. It's not a good
aggregation mechanism for preferences. It's just wrong and evil.

\[laughter\]

And can be manipulated. So when you are in a room or in a meeting with a
social scientist, we are good at one thing, and that is manipulating
other people to come out with outcomes that we prefer and you might not.
So deductive work has its role and I don't want to understate that.

Computational models. It's not so different from game theory in the
sense that you're going to write down a utility function ultimately for
your agents. What is different, as I think was abundantly clear
yesterday, is you can include a lot more, and it's way, way, way easy to
add in things that are interesting and you think essential to the
problem. Now, the converse again is the kitchen sink approach, which is
you just add in everything that might be plausibly be described or be
interesting to you, does not work. And the problem is, and I think you
can understand this from a stats perspective, is it's the same reason
you don't add everything to a statistical model, okay? A lot of times,
if you open up a paper and you look at it and they have eight or 50
independent variables, and then they have every possible polynomial up
to some order of all those variables, and then they have all the
interactions of those variables, well, what would you do with that
paper? You might put it down if you're sensible, right?

And the thing that people learn that's pathological is degrees of
freedom, you know, more observations than you need, that's actually
wrong and false and we're going to talk about that. You actually need to
span your parameter space. So one of the problems with computational
models, and this is true not just of them but also of a lot of
statistics, like non-parametric stats, is that the dynamics are opaque
because there's too many moving parts and the parameter spaces are too
big. So the question I'd like to ask is how can we actually make
everything better?

So the limiting questions. Why are things so hard is the first question.
Second question is how can these different approaches co-exist? I think
there's a case to be made that they should co-exist and the only way to
make real progress is if we come up with a system to make them co-exist.
Third question is how to make interdisciplinarity work, which I don't
think is easy. And last, how does one build more complicated models? So
I'm going to get to the crunchy toy games part of the talk.

But first, everybody lies. We have a shared methodology in political
science \-- that is Gregory House, a rotten television show. My friends
bought it for me because they claim that I was as horrible as he was. My
wife says that I am not because I don't have writers, so I can't be.

\[laughter\]

So that's a plus and a minus. I'll take it as a compliment. But one of
our better statistical people, Chris Achen, in 2002, wrote this about
our empirical work: "Empirical work, the way too many political
scientists do it, is relatively easy. Gathering the data, run the
regression or MLE with the usual list of control variables, report the
significance, and announce that one's pet variable has passed. This
dreary hypothesis-testing framework is sometimes even insisted upon by
journal editors. Being purely mechanical, it saves a great deal of
thinking and anxiety, and cannot help but being popular. Propose the
following simple rule: any statistical specification with more than
three independent variables should be disregarded as meaningless."

How many of you would still have publications if we adhered to this
rule? I would not, I will confess. I would have no publications if I
only had three independent variables. And the question you should ask is
does this apply to computational work? Yesterday, and I think I agree
with this, is the allure is that you can add all these things you think
are important. But the question is, is computational somehow exempt from
rules like this that you might want to apply to your statistical models?
And some ugliness, I can't help myself, this is from political science.
If you look over here, these are, you know, more or less whether or not
something is significant for coefficients that people thought were
interesting in their models, and this should cause you a little bit of a
belly laugh. What does this slide tell us? It tells us that the .05 mark
is magically wonderful, and that should not be, right? If we run model
after model, we should get a nice monotonic decreasing slope where we
get more .10s than .09s, more .09s than .08s. And instead what we find
is we get this little peak at .05, which if you think about it, and
that's an awfully long tail that doesn't really correspond to what you'd
expect there either. That's reasonably humiliating.

But there's also medicine. There's been a lot of self studies and a lot
of people aggregating individual studies and are finding the same
things, that there's a lot of mistakes everywhere and a lot of work is
more or less falsified within five years of it being published. I think
it's fair to say that you could do a coin toss and come up with a good
test for whether or not an article is right or wrong. So it's not just
us. So, why do we lie? Two hypotheses: number one, we don't lie. Our
methods are blown and if we just switch to something else, maybe
computational, things are going to get better. Hypothesis two is we
can't help ourselves. The experiments where you show people random
zeroes and ones and we magically see a pattern, imagine sitting in front
of Stata or R or, you know, your favorite stats package, you magically
see patterns. You can keep re-running things until you do.

So the question is, are things any different in computational land? My
answer's going to be no. I'm going to be more optimistic about this
soon, but we have a little veil of tears to go through first. The
problem is the curse of dimensionality, which I want to talk about
briefly. Sorry... So, the curse in action for people that are visual.
I'm not particularly visually motivated by learning or \-- that doesn't
parse, but ignore it. If you look, I have data, which are the Xs, and
the question is, what kind of model would you fit to that? The dashed
model would be more interesting in the sense that it perfectly matches
my data. The sample would be nicely described by it. And if you look at
the solid line, that would be a more parsimonious model that wouldn't
have so many moving parts to it.

And the question is how do we choose? What would we do to discriminate?
If we just used sample \-- you know, scratch summary statistics, we'd
obviously choose the dashed line, right? The dashed line on any summary
statistic is going to beat the snot out of the solid line. Why does the
solid line have some appeal? It's simpler, it means in some way that the
researcher has placed handcuffs on himself or herself, and that there's
some chance that if we did out of sample work, we might not get
clobbered. Here's the thing you need to keep about \-- this is a fixed
sample, and we fit a curve to it. But if we go get new data, ostensibly
with the same data generating process, what are the odds that we're
going to have that more complicated model maintained? Almost zero in
most cases, okay? So once in a while, or more often than that, we should
place handcuffs on ourselves. So that's the curse.

The curse, though, is uglier, and the question is if you want if to be
perfect, you didn't want to make assumptions about the data. For
example, if I fit a line to it, how would a line do if I ran an OLS
model? It'd be pretty lousy, right? Why do people do that? Why do they
run a line instead of just running a non-parametric model or something
complicated, they throw in some polynomials? One of the virtues of the
line is if you don't have a lot of data, you get to interpolate. So, let
me see if I can figure out our pointer. You will notice that we have
gaps between the data points. We have a gap between here and here, here
and here, here and here, here and here. When you run a line or any other
\-- you know, something you can hold your pen down and draw it like a
solid thing, what do we in those spaces between data points? We
interpolate, right? We hope that our model is correct, but we're wildly
interpolating.

What would be an easy statistical method that would better than
interpolating and just choosing a priori something to fit to it? The
answer is actually easy. That's the answer. Don't look at it real hard.
What if you just ran the mean? For each interval in our data, we just
picked an interval and calculated the expected value of Y condition on
the X there. Would that work? Think about the way this would work. It's
actually cold fusion, it's not hard. So we'd divide up our line X into
intervals, and each interval, we'd calculate the mean of the Ys there in
our sample, and we'd make a point. We'd then go to the next interval,
calculate the mean of our Ys in that interval, make a new point, we'd
connect the dots. And we'd keep doing that.

Would that work or not? It not only works, it's optimal. And there's a
little bit of math there that we're not going to go through that shows
that it's optimal. That is the best way to do statistics, and the cool
thing about it is, all those biostats courses or econometrics courses
you took, you did not have to take. You could just do expected value of
Y condition upon intervals of X. It's really easy to get through in a
class. I've done it one day and then gone home for the rest of the
semester.

\[laughter\]

So the question is why is this problematic? And the answer is do you
ever have enough data to do that? If you think about it, you don't. So
if we just have two dimensions, you're doing intervals of X. What if we
have two independent variables, what are you doing then? You think
little cubes, right? You have to divide each X into intervals, and then
you're doing cubes and you're doing the mean of the Y and the cube. If
you have three Xs and four Xs and five Xs, you're doing little
hypercubes. Do you ever have enough data to do it? The answer is no.

So what people do instead is they start to cheat. They make the
intervals bigger, so the neighborhood around X, they actually add a
squiggle term to it so you make your intervals larger. The problem still
isn't solved, though. If you look under number two, assume you want ten
percent of each variable to form a neighborhood, so in dividing up each
X into ten blocks, each variable is real valued. You can look at number
three and four, it's really ugly, you need the entire space for every
block and you need a whole, whole lot of data.

So one thing about the curse of dimensionality that's sort of brutal is
if we don't want to interpolate, we don't want to basically just draw a
line with our eyes closed across spaces where we have no data. We need
lots and lots and lots and lots of variables. And if we add these
crunchy bits that everyone is more or less excited about \-- we had
neighborhood effects, we had time, we had space, we had adaptive
behavior \-- what we're doing is we're exponentially increasing the size
of our parameter space, which means we need lots and lots of data. There
is no free lunch in computational methods. The same reason you wouldn't
willy-nilly add variables to a stats model, you by default cannot add to
this sort of model.

So, let's skip ahead to an example. I'm going to argue that despite
these problems, we should build more complicated models. If anybody
wants to rush the stage, there is a lip so don't kill yourself. This is
a little voting model. I have a red person candidate and a blue
candidate, and let's pretend that that little blue line underneath them
is a space of voters. They're distributed uniformly. And each of these
candidates wants to maximize how many of these voters vote for them, and
the voters are stupid, they vote for the nearest person. Imagine they're
sunbathers on a beach, the way the story's traditionally told by Downs
and others, and those are vendors selling hot dogs, and the only rule is
they go to the nearest vendor. If those vendors or political parties can
move wherever they want, where will they end up? Everybody want to be
confident they're going to end up right in the middle, bumping their
carts up against each other? If one of them moves, if they're both in
the middle and one of them moves a little bit this way, what's the other
candidate going to do? Follow him. And they're going to get more of the
space, okay?

This is a simple model, and if you call an economist or a political
scientist on the phone and you ask, "What are the political parties
doing in the upcoming election," we will say, "They're going to converge
on the middle," okay? Now, let's do a robustness test. If we change the
distribution of voters from uniform to something else, does it matter?
It doesn't. There's still a median, okay, so they're just going to move
wherever the median is. Second question is if we change the number of
parties, if we add a third party or more, does that change it? I get a
yes. What's going to happen? What's the equilibrium? Yeah, nobody knows.
There's no equilibrium. The problem is that you're always going to have
one person squeezed in the middle who has an incentive to pop out when
they can move, and you get continual motion if there's three or more
parties, okay? That's not so good for \-- talking to the press.

If you change the number of issues to two or more issues, is there an
equilibrium? Turns out there's not, unless there's a median in all
directions, which is a vanishingly slight probability. There's no
equilibrium if you have two dimensions or more. They're just going to
move around forever. So in terms of the KISS model \-- keep it simple,
stupid \-- is there any intuition from this model that would apply to
anything else? And the answer is really not, okay?

So the question is how do we build these more complicated models given
the cure of dimensionality? The insight that made this field a lot
better, in my opinion, was by Coleman, Miller and Page \-- Page of
yesterday fame. And their idea was that instead of visualizing this in a
deductive framework where people were utility maximizers, imagine the
parties are stuck in a topology, and the topology is \-- thanks \-- is
formed by preferences. And so some preference locations are good for
candidates and some preference locations are bad for candidates. And the
parties can move, but they're moving according to some optimization
technique, like a genetic algorithm or simulated kneeling \[spelled
phonetically\] or something like that.

What happens then? It turns out that you get much more stable results
and much more coherent results when the parties don't know everything,
because they can't always just move to a better location. They sometimes
have a veil of ignorance that prevents them from doing it and they use
polling to search. I extend it a little bit by adding plausible voters,
and we get nice cycles of attention and decay. Mick Laver at NYU
extended it even further and then matched it to European data by making
the parties follow more interesting behavioral norms. And Fowler and
Laver ran a tournament of actual people in the social sciences to see if
we could do better. The neat thing about this, though, is that we took a
relatively bad, simple model that did not have a great amount of reach.
We turned it into a computational model and with simple optimization
techniques, we got behavior that matched up to real data, okay? And the
way that we knew we were right is by matching it to the data. We didn't
know we were right up to that point. And repeated trials against new
data sources have actually borne out that it's a good analogy for the
way things are working.

Last model \-- this you have to think about for a minute. It's a tipping
model in a currency game. Two rules: probability one minus epsilon.
Think of epsilon as really, really small. We're going to basically flip
a coin. If it's greater than \-- if the probability in the population at
time T \-- so PT is how many have gold, okay? There's two currencies,
gold and silver. We're going to flip a coin and we're going to choose
someone from the audience. So all of you have gold or silver in your
pockets, and the question is do you keep your gold or do you keep your
silver, or do you switch to the majority currency? Okay? So PT is the
majority currency. So if I pick one of you at time T and I look at you,
and gold is the majority currency, you're going to switch to gold if you
haven't already, okay? If you have gold already, you're just going to
stick where you are. And vice versa, if I pick one of you and silver is
dominant, and you have gold, you're going to switch to silver. If you
already have silver, you're going to stay where you are. All right?

Epsilon, though, is interesting. With probability epsilon, you're just
going to randomly perturb yourself. You're going to switch, flip a coin
again, and go to gold or silver and you don't care about what everybody
else is doing. So the way this works is easy. All of you pick at the
beginning of the state of nature a currency, okay, uniform draw. I pick
one of you each time period. You switch the majority one minus epsilon
at the time, and epsilon at the time, you just randomly perturb. What's
going to happen to this model over time? Anybody have any guesses? I
called it a tipping model. Let's say we start off with mostly gold
people, I pick the few of you that are silver. You look around and say,
"Oops, it's gold. I switch to gold." It seems like you would drive to
everybody being gold if gold were the starting majority pretty fast, and
that's usually true.

But what might happen instead? Can you ever get back over to silver? You
can, or it wouldn't be an interesting model. What if these epsilons add
up? What if I had epsilon after epsilon after epsilon, let's just
pretend, and I drive enough people over to silver that suddenly silver
is the majority? I then get a nice cascade in the other direction, so
it's actually probable that over a long enough time period, I'm going to
get one of these tips over into the other currency, okay? So it's a
tiny, simple model. Here's the problem. We could study these in a bunch
of different ways. Some ways are better than others. We could run this
with data, Monte Carlo experiment, our dependent variable would be
regime shifts, our independent variables would be size of the
population, and the mutation rate. The easy question then if you're a
stats person is what kind of model would you run? I'm going to skip it,
but the answer is it's a Poisson model, more or less. We violate
condition number one, but not two or three. LOS doesn't work.

We could all do that, and when you look at the results, look at the
bottom side, we'd get something like this. You see that the population,
we get a negative coefficient, which means that as the population gets
bigger, we're less likely to see one of these tips into a different
regime. Does that make sense to everybody? And as your mutation rate
gets larger, we're more likely to see these tips. We can all read that,
it's actually pretty comfortable, and you can do, you know, comparative
work to see for different populations and different mutation rates what
happens. So that's great. That is the deductive result above it, which
probably none of us can read and none of you want to read. So one claim
that I'd like to make about Monte Carlo work in computational models is
we can always just do that, which is way easier to understand.

The better thing, though, is we can extend it. Skip that for a second.
We can easily add in a bunch of different things. We can add in to our
network model space, we can add communication, we can add adaptive
behavior to our little currency game. We can do all the things that we
want to do in a nice constrained way because we can always run data. You
don't, however, want to do, in my opinion, a keep it simple model
because you want the output to do something you can do stats on. One of
the main advantages, for me at least, of computational work is that if
you choose a level of analysis that matches the dependent variables
you're interested in, you can use all your statistical tools on the
results of the computational model. So you're going to get results that
are familiar to you, and if you have data, you can do much as was done
yesterday with the flu problem that Epstein presented. You can actually
match it up with actual data to see if your model's doing well or not. I
think that's the way to go forward.

And, in terms of interdisciplinarity, it's easy for people with
different types of research experience to get together. People with
really domain specific knowledge about clinical work or biology can
actually tell you how to do the model, and the people that are the more
modeling types can do the applied work of getting the model to work and
doing the test of it statistically. So the promise of computational, for
me at least, is that it puts things in a framework so people can work
together and you get results that everybody can look at and understand
given the way that we're all trained.

The negatives, though, are that kitchen sink models don't work and toy
models don't work, so I'm arguing for sort of a knife edge here, which
is you have to choose a model at the right level. And for me, the right
level is a dependent variable that is produced that matches up with your
empirical and applied concerns. If you're doing it \-- too simple of a
model, all of the heavy lifting of making the model analogous to what
you're really interested in is going on inside your head, which is a
black box. I just don't care. It's never helpful to do that. And on the
other hand, if you make it too complex, you're going to get a bad model.
It's going to be garbage, there's just too many parameters. So the way
you actually constrain these things is to get people with constraints
from their practice areas, whether it's clinical work or biology, to
actually keep your model in check and to produce outputs at the right
level analysis. Thanks.

\[applause\]

Female Speaker:

Okay, great. Thanks, Scott. So our second speaker for today is Dr. Mark
Newman, and Mark is going to be speaking on the importance of networks.
And Mark is an Associate Professor of Physics and Complex Systems here
at the University of Michigan, and he's also a member of the external
faculty of the Santa Fe Institute. His research is on the structure and
function of networks, particularly social and informational networks,
and his research has investigated scientific co-authorship networks,
citation networks, e-mail networks, friendship networks, and
epidemiological contact networks, among others. He also studies
fundamental network properties, such as degree distributions, centrality
measures, and assortive mixing. So I'll leave that to Mark. Do you need
to plug in your computer, or are you \-- okay.

Mark Newman:

Very good. Hello, everyone. Pleasure to be here. Thank you to the
organizers for inviting me. It's very nice to be here in Ann Arbor...
Oh, wait, over here.

Male Speaker:

Mark, we need you to use the microphone \[inaudible\].

Mark Newman:

Okay. I hate these things. I like walking around. Stride around the
stage. Okay, I'll stand here. Yes, so I'm going to talk about social
networks and how they affect the spread of disease. I can't resist
starting off with this picture. This is a map of the world. You're
probably familiar with it. And this is a slightly different map of the
world. In this one, the sizes of the countries have been changed to
represent how many people live there. So China and India are very large
and the United States is relatively small and Canada has almost
completely disappeared.

\[laughter\]

Sorry, Canadians. So everybody get the idea of what this map is showing?
Okay, so now, take a look at this one. This is a map of prevalence of
HIV. That large blob in the middle is Africa. There's the United States.
There's Western Europe. So, that's a pretty devastating picture. It's a
fairly forceful representation of the scale of the AIDS problem in
Africa. But it also tells you something else. Why is it that all those
people with HIV are in Africa? Well, there's a lot of reasons for that,
of course, but one of them is, and this is sort of obvious, but you have
to say it. One of them is that they're all catching HIV from each other,
right? There's a network effect here that everybody who gets it has to
be getting it from someone else, and one of the reasons why they're all
concentrated geographically in one area of the world is because
everybody has to get it from someone else there.

So, if you want to understand the distribution of disease, then you need
to understand the patterns of how it's being transmitted from one person
to another. Incidentally, if you're interested in maps like this, we've
made a lot of them, you can go to this Web site here, worldmapper.org.
We've made about 400 maps like this, they're called cartograms, showing
all sorts of things, many of them public health variables but not all of
them, and I think that it's an interesting way to visualize a lot of the
data that we're getting now about what's happening all over the world in
public health and in various other things.

So what I want to talk about today is social networks and their effect
on the spread of disease in traditional modeling approaches in
epidemiology. Several speakers yesterday talked about this kind of
thing. You have these compartmental models where you divide up your
population into different classes \-- susceptible, infective, recovered
and so forth. And then, if you make certain assumptions, you can write
down equations that describe how people move from the susceptible class
and the infective class to recovered class, and so you can make some
predictions about how a disease of interest will develop.

You can make these models a lot more complicated. You can add additional
compartments, you can make the flow chart of how \-- what changes are
possible more sophisticated. But they essentially all make the same
important assumption, this assumption of full mixing or mass action, in
which you assume that within any population group, the probability of
two people having contact sufficient to transmit the disease is the
same. It doesn't matter who the people are. And this is a very
convenient assumption, it allows us to write down equations and solve
things and actually make predictions, and we've learned an awful lot by
doing this kind of modeling, something we've been doing for decades.

However, this assumption is obviously not correct, right. It's obviously
not correct to assume that any two people have equal probability of
contact sufficient to transmit the disease. It's certainly true that I
am more likely to have contact with somebody who's my friend then some
random person who lives on the other side of town or the other side of
the state. So it's clear that there are real networks of contacts
between people that affect the spread of disease, and if you want to
know how diseases spread, then you need to know about the structure of
these networks. If you don't, then you're missing out on one very
important element that dictates how diseases spread through communities.

So, Carl Simon showed this picture yesterday and talked very briefly
about contact networks. This is the network he showed, which comes from
data from the health study which \-- this is a network of who dated whom
in a US high school. The pink and blue dots are the girls and the boys,
and the lines between them are who's dating whom. So in the jargon of
the field, the dots are nodes, the lines we call edges, and they
represent some kind of contact. It could be who's dating whom. If you're
interested in the spread of sexually transmitted diseases, for example,
then this is probably the network you want to look at. But it could be
other kinds of contacts as well, physical proximity, so forth.

Carl actually only showed this portion in the middle of the network
yesterday, this sort of main, connected clump here, but there are in
fact \-- there are all these other people who are not connected to the
main clump of the network. That's a pretty common pattern that you often
see in these cases. So there's various people around the edge here who,
you know, only dated one other person during their high school career,
and you can certainly imagine that this could have implications. If
you're thinking about the spread of sexually transmitted diseases, for
example, there's no way the disease can get from this sort of this main
clump to these separate clumps over here because there's simply no
connection between them. So, even at a very simple level, just looking
at this picture of network, you can certainly see that there could be
consequences here.

Here's another example, this is also data from a school. This is a
friendship network of a US high school. This is data that came from Jim
Moody, who's at Duke University. So there are nodes here representing
students in the school, and there are edges representing who's friends
with whom. And looking at this picture, you immediately see that there
are these four big clumps in the network. So there again, there's
something interesting going on here that you might not have known about
if you hadn't looked at this network. Actually \-- in actual fact, this
is really two schools \-- these two clumps up here are middle school and
these two clumps at the bottom are the adjacent high school. So there's
obviously some sort of division between the two. One could imagine that
an outbreak of flu in the middle school would kind of spread like
wildfire through the middle school but might take a while to get to the
other school 'cause there's some separation between the two.

Within each of the two schools, you also see that there's two clumps,
this one and this one, and this one and this one down here. Any guesses
about what those are? Sorry? \[laughs\] The jocks and geeks. Good guess,
nah. Somebody said it. It's race, in fact. The blue dots are the black
kids and the yellow dots are the white kids. So this particular \-- so,
this is not a big surprise to sociologists, of course. This is something
that's well known, but it's very forceful when you see it in a picture
like this. And again, you can imagine that that can have substantial
outcomes of the way the community is divided in this particular case.

So these are nice pictures, but they're actually rather rare. It's often
not the case that you can make a picture of the network. So you can do
some study, you can circulate questionnaires and ask people who's dating
whom, or who's friends with whom. You can certainly construct these
networks, but one problem with this field is that very often it's
difficult to get a picture of what the network looks like. There's
various computer programs that can draw your network for you, but most
often, when you do it, you get something like this, which is essentially
useless. There's not much you can tell from it. This is just some
hairball of nodes and lines. You really can't see anything that's going
on in this network.

That doesn't stop people from publishing pictures like this. You see
them very often. People seem to think that they're very pretty, but
really they're not much use. So this one, you know, actually came from
\-- somebody actually published this, came from somebody's paper. Mark
Vidal at Dana Farber likes to call these pictures ridiculograms \--

\[laughter\]

\-- which I think is a pretty good name for them. Actually, the
definition, apparently, of a ridiculogram is that it should be visually
stunning, it should be scientifically worthless, and it should be
published in *Science* or *Nature*.

\[laughter\]

So there's a practical issue here that you can collect the data but how
can you tell what's going on, because you can't really make a picture of
these things? Well, so one thing you can do is you can look at
statistics of the network and try and decide whether those statistics
are correlated with epidemiological outcomes that you might care about.
So one very common thing that people look at, for example, is the
so-called degree of nodes in a network. The degree of a node is the
number of connections it has, so how many friends you have if we're
talking about a friendship network. And one of the things that you
immediately realize is that people with high degree are far more likely
to get infected than people with low degree. If you're this person over
here and you have a lot of contacts, then you have a lot of
opportunities to get infected, much more than this person over here. So
that could certainly be something to be correlated with outcomes that
you care about.

Here is one of the rare pictures of networks where you can actually see
what's going on. That's mostly because this particular network is very
small by the standards of these network studies. This is a network that
I got from Valdis Krebs. It's \-- it represents data from a study of the
spread of TB, and what you immediately see by looking at this particular
picture is that there are some of these stars, these hubs in the
network, where there's one person who has an awful lot of contacts with
other people. So that's a person who has high degree, degree is the
number of contacts you have, and there are typically a few of these
people with high degree in many of these networks. There are some people
whose job brings them into contact with many people. For example, if
you're, you know, the person who drives the bus or the person who sits
on the reception desk or something like that. There are some people who
by the nature of the work that they do have contact with 10,000 people a
day, or something like that. So you can very well imagine that if this
person here got sick, that it would be a bad thing. They could spread it
to a lot of other people.

So, so let me just make that a little bit more concrete. Well, so let me
say briefly what the idea here is. So certainly, it would be bad if this
person here got sick. They could spread it to a lot of people. So if you
had to think about it, your first guess might be that the important
thing to worry about here would be the average degree of a node in the
network. What's the average number of people that a person knows? And if
that's high, then the disease is going to spread quickly, and if it's
low, it's going to spread slowly. It turns out, in fact, that that's not
quite right. It turns out that the correct answer is it's not the
average degree that matters; it's the average squared degree.

Now, let me see if I can explain the reason for that. So, so if this
person here has got maybe, I don't know, 100 friends or something like
that. So if they got sick, there's 100 times more people they could
spread it to then somebody who only has one friend. But that's not the
whole story. This person is also more likely to get sick in the first
place because they have so many contacts. They're 100 times more likely
to get the disease in the first place because there are 100 people they
can catch it from, and 100 times more likely to pass it on. So overall,
they are 100 times 100, or 10,000 times more effective at spreading this
disease than somebody who only has one friend. So it's not your degree
that matters, it's your degree squared. And that means that these hubs
\-- I mean, you would think that these hubs would be important at all,
but actually they're even more important than you think they are because
a person who has 1,000 contacts is a million times more effective at
spreading the disease 'cause it's 1,000 times 1,000.

So here are almost the only equations in my talk for those three of you
who actually care about the mathematics, I thought I would just go
through this quickly. Suppose, for instance, you're this person here who
has degree five, five contacts. And you catch the disease from one of
your contacts, this person over here, so in comes the disease. Then
there are four other people left over that you could spread that disease
from. Obviously, you're not going to spread it to the person you got it
from 'cause they already have it. Or it can be sort of in general. If
your degree is K, then there are K minus one other people you could
spread the disease from.

Now, an important parameter in these kinds of models is what we call
transmissibility, which is the probability of passing the disease on to
a susceptible neighbor. So when you have contact with someone sufficient
to transmit the disease, you don't necessarily actually transmit the
disease. Sometimes it happens and sometimes it doesn't. So T, the
transmissibility, is the probability of that happening. And then, on
average, the number of people you're going to spread the disease to is
the number you could spread the disease to multiplied by the probability
that it actually happens. So it's T times K minus one.

The fundamental parameter in the mathematical study of the spread of
disease is this parameter R0, the fundamental reproductive number.
That's the number that says, on average, when somebody gets sick, how
many other people do they pass the disease onto? If they pass the
disease, on average, onto more than one person, then the disease is
going to spread, because at every sort of step in the chain, it spreads
to more people. If on average they pass it to less than one person, then
the disease is going to die out because each person is passing it on to
fewer and fewer people at each step of the chain.

So you can calculate a value for this R0 now. Basically, we just want to
average this thing, T times K minus one over everyone in the network,
but now, here's the catch: you're more likely to get the disease if you
have more contacts. So people with 100 contacts are 100 times as likely
to get the disease as people with one contact. So when you do this
averaging, you have to weight by the degree of the person. That's this K
sub I, that's the degree of person I. And then average over all people
I, so that's what this sum here is doing. And if you just fiddle around
with that for a while, you find that it's equal to T, the
transmissibility, times the average of K squared \-- that's the average
of the degree squared \-- minus the average of degree divided by the
average of degree.

So here's this average of degree squared thing that I was talking about
that comes in there. And that affects this fundamental R0 parameter. If
that's greater than one, then the disease is going to spread. Or another
way of saying that is that the transmissibility has to be greater than
this number here, which sort of depends on the structure of the network
for your \-- this degree and degree squared. It has to be greater than
that number for the disease to spread. If it's less than that number,
then the disease is spreading, there is some non-zero probability if the
disease spreads, but it actually won't become an epidemic. It'll just
die out because the probability isn't quite high enough to make it
spread. So this gives rise to this well-known epidemic threshold effect
that it's not true that the disease will always spread if there's some
non-zero chance of it being passed on. Actually, it has to be non-zero
and sufficiently large for the disease to spread, otherwise it'll just
die out.

So, the interesting thing is that it depends on this average of K
squared quantity, and if that average of K squared is very large, then
this thing on the right is very small, meaning that the threshold value
of the transmissibility is very small. You don't have to have a very
high probability of transmission for the disease to spread. Well, that
happens, in some cases \-- here's a figure from a paper I did with Betsy
Foxman and some collaborators a couple of years back. This is for sexual
contact networks, and it's showing for a particular survey we looked at,
it's showing the lifetime number of partners that people in this survey
had, and what it shows is essentially what I showed on that previous
figure, that there are a few hubs in the network.

Most of the people \-- so this is a histogram \-- most of the people are
up here with small numbers of \-- lifetime numbers of partners, two,
three, four, something. But there are a small number out here, you know,
Don Giovanni with his 1,003 lovers, a small number of people out here
with, you know, 1,000 lifetime sex partners, and those people are the
hubs in the network who you have to worry about. And because of those
people, and because the thing that you care about is the average of the
degree squared, that average of degree squared becomes very large
because of these few hubs out here in the tail, and that means that the
epidemic threshold becomes very low. The chances of the disease
spreading are very good, even if the probability of transmission on any
particular sex act are very small. So this is kind of worrying when you
see these hubs in the network, you realize that there's something here
that you should worry about.

So, so that's sort of interesting mathematics, but maybe I can sort of
give you a flavor for, sort of in practical terms, why that's happening.
This is a figure from some Steve Borgatti, which shows the spread of
SARS in the outbreak in Singapore in 2003. So they have very good data
for their outbreak, and they know sort of all the cases. Well, they know
all the cases that they know, I guess. There could be some other ones
they don't know, but all the cases they know, they know who got the
infection from whom. This is the first case they know about, person
number one here. And the interesting thing is that this person was a
hub. They spread it to a lot of other people. They actually invented
their own word in the case of SARS; they called them superspreaders. But
it's just the same thing that other people called hubs.

This person, unfortunately, was a hub and spread it to a lot of people,
but look, almost all of those people didn't spread it to anybody else.
Or some of them spread it to like one other person, and then it died
out. Most people in the network are not hubs. Almost all of them, you
know, 99 percent of them are not hubs, pass it on to one other person or
pass it on to no other people. A very small fraction are hubs, but it
only has to be a small fraction. See, this person here, well, most of
the people they passed it on to, it went nowhere, but one of the people
they passed it on to was another hub. And that person spread it to a lot
of people, most of them it went nowhere, except that one of them was
another hub, number 35 there, and that person spread it to a lot of
people, and so forth. So you can see that what's happening is you have
this very small fraction of people in the network who are hubs, but
nonetheless it's enough because you're just encountering one of them
just often enough that gives the disease a big boost and keeps it alive.
So they're having a big effect on the spread of the disease even though
they're very small in number.

Okay, well, so this is a sort of a nice general idea. And there are
various other similar things you can talk about. You can say things
along the lines of, "If my network has this kind of structure, these
kinds of things are going to happen." But one could also very reasonably
ask, "Well, can I make this more concrete? I actually want to make some
predictions about the spread of a particular disease. Can I also do that
using these kinds of network techniques?" So I want to tell you \-- so
the answer is yes, of course, otherwise I wouldn't be talking about
this. I want to tell you about a few example studies, some of them are
things that I've worked on, some of them are other people's work, that
show how you can apply these kinds of network ideas to understanding the
spread of particular diseases.

And the first one I want to talk about is a study I did with Lauren
Meyers from UT Austin, and Mike Martin and Stephanie Schrag from CDC,
and it was a study of mycoplasma pneumoniae, a particular outbreak of
mycoplasma pneumoniae in a hospital in Emerson, Indiana, and Evansville,
Indiana, and... So this is a fairly mild form of pneumonia. Of course,
mycoplasma is sort of colloquially called walking pneumonia, you may
have heard of it under that name. It's the sort of thing that's endemic
in the population all the time, and for most people doesn't cause any
serious health problems. For people who are fit and healthy, it's not a
big issue, but if you're very old, very young, sick, otherwise immune
compromised, then it can be a serious issue. So people care about it,
for example, in hospitals.

So Mike and Stephanie studied this particular outbreak in this hospital
in Evansville and this is a particularly good example because in this
case we have very good data on what the structure of the network is. So,
let's see, this is a hospital and it has a bunch of patients in it, and
we know which ward each patient was on. And then it has a bunch of
caregivers, and each of them is assigned to specific wards and we know
which wards they're assigned to. Sometimes they work on one particular
ward, sometimes they work on several different wards. But we have all of
those details, so we pretty much know the whole contact network of who
could have come into contact with whom in this hospital. That's an
unusual situation.

It's not something you usually know about in most communities. But this
is a good example to start off with because we have pretty much all the
data that you would like to know in order to make a comprehensive
network model of how this infection might spread through this hospital.
So the kind of picture you should have in your head \-- this is just a
sketch, of course, this is not the real network \-- is that you have a
bunch of wards, and each of them has a bunch of patients in here, and
then there are caregivers, some of whom just work in one ward and some
of whom work in several different wards. So that's the sort of network
picture you have.

So what we were able to do is take this network and make a model of this
infection spreading over the network. There are still some things that
are missing, and the main things that are missing here are these
transmissibility parameters. We don't know what the probability of
transmission is of this disease from one person to another person when
they have contact. So what you do is you put those parameters in and you
guess some values and you just sort of fiddle around with them. And, you
know, there are fairly rigorous ways of doing this in order to try and
reproduce something similar to the actual outbreak that was observed in
the hospital, for which we also have good data because Mike and
Stephanie collected very comprehensive data. They tested everybody who
was in the hospital for mycoplasma during the outbreak, so they know who
got infected and who didn't.

And what we find is an interesting thing. We find, which is sort of what
this figure down here is showing, we find that in order to fit the model
to that outbreak that was observed, it must be the case that two things
are true. First, that the probability of transmission from an infected
patient to an infected caregiver is pretty low. It was about 20 percent.
But conversely, if a caregiver gets infected, the probability of
transmission to one of their patients is extremely high, in fact, not
measurably different from 100 percent up here. So that's kind of
interesting because usually people don't even worry about the
caregivers, right? Because the caregivers are mostly young, healthy
people for whom \-- they're probably not even going to get any symptoms,
they probably never even know that they're infected. But what it's
telling you is actually the caregivers are pretty much the only people
you should be worrying about. They're the ones who are spreading the
disease. The probability of spreading from the patients is actually
pretty small. From the caregivers, it's extremely high.

So, the standard strategies for dealing with outbreaks like this are \--
well, there are two of them. There's patient cohorting, which means that
you take all the infected patients and you stick them in a room together
and tell them to fight it out. But that's well known not to be a very
effective strategy for mycoplasma because \-- because it has rather a
long incubation period, which means that there's probably a lot of
people who are infected who you don't know they're infected yet. So the
other common strategy is chemoprophylaxis for the patients. You give
azithromycin to all of the patients, even the ones who are not infected,
just in the hope that it will prevent them from getting the disease.

What this is telling you is that you're probably doing the wrong thing.
Actually, you should be giving the antibiotics to the caregivers.
They're not \-- you know, you think that you should be giving them to
the patients because they're the ones that are at risk if they get
infected. But actually that's not what's going to stop the spread of the
disease. If you have a sort of public health outlook on this, you should
be giving it to the caregivers. They probably never even know that
they're sick, but nonetheless, if they do get sick, it's a disaster,
because there's 100 percent chance that they're going to infect all the
people in their ward.

So we made some recommendations based on the outcome of this model. I
actually don't know if those recommendations have been implemented yet.
These things take rather a long time to work their way through the
bureaucracy. So this is a nice example of the sort of thing that you can
do, but as I say, it's also rather rare that you have this kind of data
for an outbreak. In this case, we knew the entire structure of the
network of contacts between people, and you don't usually know that. In
fact, the common type of thing that one wants to do is ask about how an
infection is going to spread through an entire city or an entire
country. And if that's what you want to know, then it's obvious that you
can't get the whole network. Obviously, you can't find the social
network of everybody in the whole country, so what can you do? Can you
still apply these kinds of ideas to those kinds of problems? Well, yes
you can, but you have to, you know, sort of make some additional leaps
in order to do it.

There are various things that you can do, but perhaps the commonest one
is to do a sort of combination of data gathering and some kind of
computer simulation to fill in the gaps, the bits that you don't know.
So I'll give you an example of that kind of study as well. This is from
some work I and my collaborators did on SARS, and this was a study where
we tried to predict how SARS would spread through the city of Vancouver,
British Columbia, which is actually one of the two Canadian cities that
was affected by SARS in the outbreak in 2003.

So, we don't know the whole social network of the city of Vancouver, of
course. But what we did have was pretty good demographic data for a lot
of things in the city. So what we tried to do is make up a network that
matches the things that we know about the demographics of the city. So
at the simplest level, for example, you could look at the degrees of the
nodes in the network, how many contacts people tend to have. And then
you could make a network that has the right degrees of nodes in it,
okay? But in this particular case, actually, we can do something a lot
more sophisticated.

We made quite a complicated model. We had data on the sizes of houses,
we had data on people's occupations, we had data on ages of children and
where they went to school and things like that. So you can make up quite
a complicated network in which the people are divided into households,
and people from individual households go to various locations \-- kids
go to school. The schools themselves are modeled as being divided up
into classrooms with a bunch of kids of a given age in them, and
teachers and so forth. So you can put in quite a lot of detail there.

You're not looking at the real network because we don't know the real
network. What we're doing is we're taking what we do know about the real
thing and then making some simulation to try and guess what the
structure of the network is. Making up a network that seems plausible
given what we know about it. In this particular case, we knew sizes of
households, ages, where people work, where they go to school, the sizes
of school classrooms, so forth. So we had quite a lot of data about the
city. So this is a typical approach that people take. It's a combination
of computer simulation to generate a reasonable network based on actual
data, demographic data that you measured about the population of
interest.

So here's an example of the sort of things that you can get out of these
kinds of simulations. So on the horizontal axis here, I have this T
parameter, which is again the transmissibility. That's the probability
that the disease gets transmitted if a susceptible person comes into
contact with an infective person. We don't know what that is for SARS,
but what we can say is how we expect the outbreak to behave as a
function of that parameter. If T is very small, a very small probability
of transmission, then we're below that epidemic threshold. Even though
the probability of transmission is non-zero, the disease still won't
spread. There'll just be a few cases and then it'll fizzle out. So
that's what you're seeing in this left-hand portion. There's a few
cases, you know, three, four, five, but then the disease dies out.

Here's the epidemic threshold, this gap in the middle, and beyond that,
you get a large outbreak that affects some fraction of the population,
so the axis here is the fraction of the population infected by the
outbreak. So over here it's just a few people, over here it's 20 percent
or 70 percent or something. And you see as the transmissibility goes up
exactly as you would expect, the number of people infected in the
outbreak rises. There are several different curves here. The blue one,
actually, is the one that came from our detailed model of Vancouver. The
other two are two other simple models of spread of disease on networks
that people have studied in the literature. This green one down here is
a so-called power law network, sometimes called a scale-free network,
something that people have made a lot of fuss about in the recent
networks literature. It actually turns out not to be a good model in
this particular case.

This \-- this purple one up here is actually a pretty simple model. It's
called a Poisson random graph, which turns out in this particular case
to mimic the real world data pretty well. So maybe that's telling us
that in this particular case, we could have cut out a lot of the
complexity of the model and modeled it using this much simpler one.
Trouble is, in order to come to that conclusion, you had to do the
calculation on the more complex network to begin with in order to know
that you could do that. So I'm not sure really how useful a conclusion
that is.

You can also work out various other things. This is something that
people were interested in, in particular, in the case of SARS. It's the
question of if there is an outbreak of the disease, and I see it going
on, and I see have an outbreak of the disease, what's the chance that
this is going to become a large scale epidemic? Sometimes if you get an
outbreak of disease, it'll just fizzle out and die away, and sometimes
it'll go on to infect hundreds or thousands of people. And it turns out
that it depends on how big an outbreak you already saw. You know, if
you've already got 100 people infected, that's it. It's probably
hopeless, there's nothing you can do to control the disease. If you've
only got one or two people infected, then it may be that you can keep it
under wraps and it won't get out. And that's what this figure is
showing. For various different values of this R0 parameter, which is
sort of how virulent the disease is, then it tells you what's the
probability that you're going to have a large scale epidemic, given that
you've already seen five cases or ten cases or 15 cases, and the more
you've seen, the worse it is. If you've seen a lot of cases already,
then it's very unlikely that you're going to be okay. There's just going
to be a big outbreak.

So this is something that you would like to know if you have some
surveillance operation which can spot outbreaks as they begin to happen,
and you can see that we have a small number of \-- a cluster of cases of
this disease. Depending on how many there are in that cluster, you may
be able to do something to control it and prevent a large-scale
outbreak. This is a kind of similar thing. If you just have one initial
patient bringing this disease into your city, what's their degree? If
their degree is very high, then you're very likely to get a large-scale
outbreak. If their degree is relatively low, you're not.

This was illustrated, in fact, in the two outbreaks that happened in
Canada. There was one in Toronto and one in Vancouver. The one in
Vancouver was a businessman who returned from a business trip to Hong
Kong. He returned \-- he was married but childless. He returned to his
empty house because his wife wasn't there at the moment and he died in
his house, and that was it. End of outbreak. The one in Toronto, a woman
returned from a trip and she was sort of the matriarch of a very large
extended Chinese family. There were a dozen people living in her house
with her. She returned from her trip, gave the infection to several
members of her family, and from there it took off and became a large
scale epidemic. Well, it eventually affected, I think, about 150 people
in Toronto. So the number of contacts that initial person has can make
quite a big difference.

So you can take these kinds of things a lot further. This is one example
of quite a well-known study called EpiSim, so this is not work that I
did, this is stuff by Steve Eubank and his collaborators at Virginia
Tech. And they did this quite well known study of Portland, Oregon, in
which they did a simulation, a very detailed simulation of Portland in
which they have all the roads and all the cars moving about on the roads
and the pedestrians moving about and people getting up and going to work
and taking their children to school, and it's a very detailed version.
Beside this thing, our own version of Vancouver pales in comparison.
This thing is a very large scale simulation, very detailed
reconstruction of the social network of this one city, which you can use
to make quite detailed predictions about the spread of disease,
including where the most infections will be and how many days after the
outbreak there'll be infections in what particular parts of the city and
so forth. So they've taken this to really great lengths, this kind of
detailed microsimulation of the spread of disease over social networks.

So the other thing I wanted to talk about was time-varying networks. So
an important issue here that I've sort of glossed over is that these
networks are not really static. I've talked about them as if the network
just sits there and the disease spreads over it. Sometimes, actually,
that is, roughly speaking, the case. If the network is changing slowly
and the disease is moving quickly, that's a good approximation. But in
many cases, that's not the case.

And one case where it's not is in the spread of, for example, HIV, where
you're talking about a disease which spreads through a population over a
period of years or decades, and it's spreading over sexual contact
networks, and those networks are certainly changing substantially on the
period of years, and so you have to take those changes into account.

So here is \-- I wanted to tell you a little bit about a project I did
with Betsy Foxman and some other collaborators on trying to understand
how these networks change structure over time. So here we were
interested in HIV and \-- so we were looking at sexual contact networks,
and we analyzed data from a survey, a telephone survey conducted in
Seattle, Washington. So I should point out that I was not involved in
actually conducting the survey. It was designed and implemented by Betsy
and by her collaborators. My contribution here was analyzing the data.

But, so the survey involved dialing random telephone numbers. So a
computer generates random telephone numbers and dials them for you. In
this particular case, 37,000 of them, of whom, you know, a lot of them
you just get an answering machine or it's somebody's business or
something like that. But about 8,000 of them actually answered the phone
and were just people's households. About 2,000 of those met eligibility
criteria, meaning they were over 18 and various other things. And of
those, about a thousand actually agreed to participate in the survey. So
starting from 37,000 phone calls, you got about 1,000 people who agreed
to answer questions in this survey where you ask them a bunch of
detailed questions about their private lives. And you learn a lot of
interesting things by doing this kind of stuff. It's amazing what people
will tell you over the telephone.

\[laughter\]

So, here's just a few sort of summary statistics. We learned that the
median length of a sexual partnership in this survey was 92 days, about
three months. So this network is definitely changing over the time scale
on which HIV is spreading through the population, no question. The
median gap between partnerships is about 120 days, about four months. So
we're going three months on, four months off, three months on, four
months off.

\[laughter\]

Male Speaker:

That's sleepless in Seattle.

\[laughter\]

Mark Newman:

Right.

\[laughter\]

Thank you. I wish I'd thought of that. Here's an interesting one. About
a quarter of the partnerships actually overlapped with the previous
partnership of the same person. They were sleeping with two people at
the same time. That's a big issue if you're concerned about the spread
of sexually transmitted diseases. Many of these diseases, including HIV,
are primarily infectious during the first few weeks that you have the
infection. HIV is quite infective in the primary \-- so-called primary
stage before seroconversion, about the first eight weeks or so, and a
lot less infective after that. So you care about whether the person is
sleeping with two different people during that period. If you wait until
after the eight weeks before you have your next partnership, that
enormously reduces the probability of transmission.

So this concurrency is an issue. Here's an interesting one. The median
length of these overlaps is 400 days, over a year. So some people are,
you know, sleeping with the same two people for more than a year.
Actually, some of them it was like for more than ten years. You know,
this François Mitterand and his mistress, or something like that.

Here's some pictures of the kinds of patterns of relationships that
people had in this study. So each of these boxes here is one person in
the study, and the lines represent as a function of time horizontally
the periods over which they had each of their relationships.

So for example, this person here had a bunch of short relationships and
then sort of settled down with one person. So maybe they were sort of
looking around and then they finally found a person they liked and they
settled down. This person here was in a steady relationship, and then
they had a fling! Look, right there.

\[laughter\]

That's a one-night stand or something. Here's another person who's a bit
like person A. They were, like, trying out a bunch of different people
and then sort of settled down with one person. But the trying out sort
of overlapped with the settling down. Maybe they hadn't quite decided on
this person here yet. So you know, there again you've got some
concurrency. That could be an issue. Here's somebody who sort of
switched from one person to another person, they had a little bit of
experimentation going on the middle. This person, you know, was in a
relationship for a while and then obviously things were not going so
well towards the end.

\[laughter\]

So these are common patterns. We see these same kinds of patterns. And
obviously you can imagine that these patterns could affect the spread of
disease. Again, you're going to need to know about these things to
understand what's going on here. This is distribution of length of
partnerships. Most partnerships are fairly small but there's a small
number out here in the tail around thousands of days, this is 20 or 30
years. My parents are over there on the curtain somewhere. This is the
distribution of lengths of gaps, and some of the gaps, you'll see, are
negative. That means they're overlapping a bit, as I said before. And
there's some correlates with demographic parameters like age, people of
different ages behave somewhat differently.

So the story here basically is that there's a lot of interesting time
structure in these data, and that's clearly going to affect the spread
of HIV or other sexually transmitted diseases. The obvious next step in
a study like this would be to then make a model of the kind that I
talked about before, and model the spread of HIV. We haven't gotten onto
that stage yet, we've just analyzed the data, but that's clearly the
sort of thing that needs to be done next, and we are thinking about
that. Before I finish, I just want to tell you one last example where
somebody has actually done that kind of thing, taken time, ordered data,
and used it to make an actual model of the spread of disease. This is
not work that I've done, this is from a recent paper in PNAS by Vittoria
Colizza and her collaborators, and this was on the effect of airline
travel on the spread of influenza.

If you're interested in the international spread of disease, then it's
unquestionable that airline travel is the primary vector for the
international spread of disease. Airplanes are perfect vectors for the
spread of disease. They're just these little flying tubes of germs
hurtling at hundreds of miles an hour from one country to another. You
get all these people together, you put them in a confined space for ten
hours, and you move them to a new country. It's crazy. It also happens
to be an example where we have really good data for what's going on.

I just want to quickly show you this little movie here of the flights of
airplanes. So this is real data for actual airplanes made by this guy
Aaron, and what this is showing is flights of airplanes. The dots are
airplanes flying around the United States, and there's not very many on
the picture right at the moment because it's nighttime. You can see a
few red eyes coming in there from the left. But after a while, you get
the sun coming up on the eastern seaboard and you start to see airplanes
taking off and flying over to the west, and then you start to see the
sun coming up in the west, and if you wait long enough, you'll also see
the sun coming up in Hawaii, down there.

So we have complete data on exactly where the airplanes are, where
they're coming from, where they're going to, how many people are on
them. Well, I don't have the data but these people did. So they made a
very comprehensive model of the spread of influenza, with all of these
airports in it and a model of mixing within the towns where the airports
are. And this allows them to make explicit predictions of the spread of
disease. This is predictions for a putative outbreak of influenza
starting in Hanoi and initially spreading to various other countries in
Southeast Asia, and then you go on in time a little bit more and you see
that it's spread to some countries in Western Europe, it's spread to
Russia, it's spread to India.

And they can make very specific predictions about exactly when they
expect disease to get to particular places because they know the way
it's getting there is it's traveling on these airplanes, and they know
exactly when the airplanes are taking off. So they can make very
specific predictions. They can even go down to the level of individual
cities and say how many cases they expect to see in individual cities at
specific times. So I'm out of time there, I should stop talking. I hope
I've convinced you that there are a variety of interesting questions
here, many of which have not been tackled yet. It's early days yet.
There are many things still to be done. I do want to thank my various
collaborators here and also people who paid us money, and I guess
there'll be a question and answer session later. I'd also be happy to
answer questions privately, and thanks for your attention.

\[applause\]

Female Speaker:

Okay, so we're going to have a 15-minute break now, and so we'll start
back up at 10:45 and we'll have another talk and then our discussion
session.

\[low audio\]

Female Speaker:

\-- and she will be presenting the view from economics. And Jasmina is a
Professor of Economics in the Department of Economics at Simon Fraser
University, and she is also the director of the Center for Research in
Adaptive Behavior in Economics at Simon Fraser. She teaches
macroeconomic theory, monetary theory, and computational economics, and
her research interests focus on adaptive behavior of economic agents and
experimental economics. She is currently working on an evolutionary
model of currency crisis, comparison of performance of adaptive and
rational agents, and tacit coordination games, among other projects.
Welcome, Jasmina.

Jasmina Arifovic:

I hope they turned the \-- uh-huh, okay. Okay, so as the title of my
talk points out, I'll be giving today a view from economics on \--
basically related to complex systems, first. And then I'll try towards
the end of the talk to see whether \-- and find out, sort of, ways in
which these things and the work that I'm doing can actually be
implemented in studies of population health.

So the talk is going to consist of basically motivation for the kind of
work that myself and a number of other economists are doing. And then
I'm going to try to tie it into a definition of complex as basically \--
as the work that is \-- that belongs to the category of complex systems.
I'm going to illustrate the type of work that's been done using two
basic examples that are pretty different. One is in terms of the
coordination games, and the other will be about behavior of exchange
rates and currency crisis. And in my conclusions, I'm going to
basically, again, relate the work and the modeling strategies to the
possibilities of using it in studies of population health.

Now, before I get into the actual type of work that I'm doing, I wanted
to emphasize, first of all, what it is that we actually mean by a
standard economic model. We've heard quite a bit about why we don't like
or why neoclassical economics does not work, and so on. I just wanted to
give a quick overview of actually what we mean by a theoretical economic
model.

So basically, the ingredients of any of these models would be that we
have a certain, well-defined specified economic environment. We have
agents, and agents are defined by the preferences, by their utilities,
profits, and they also are defined explicitly by the beliefs that they
hold about the environment in which they are operating. And we are
explicit also about the knowledge and information that these people
possess. This is going to turn out to be an important part of my talk
today, talking about different ways of modeling these beliefs, and how
that affects the outcomes.

So what do we do in an economic model? Well, we look at these agents
with set preferences and set beliefs, and we put the constraints on
their basically optimization problem that come from the environment. And
then we let these agents solve optimization problem that can sometimes
be quite complicated, depending on, again, the type of the environment.
We derive their decisions out of this constrained optimization problem,
we bring all these decisions together, and then we compute what we call
an equilibrium outcome. So this would be an outcome of this model where
everybody at that point is doing the best they can, given what everybody
else is doing, and given the constraints. So, whichever way you look at
this, whatever kind of an economic model you want to discuss, it's going
to contain this \-- these elements.

Now \-- so, in this equilibrium, what this concept basically assumes \--
what it is about is about the fact that everybody, again, is doing the
best they can, given the constrained environment in which they operate.
And so basically, this solution to our problem is always to be \-- going
to be a combination of what we call best responses. So, in this sort of
a situation, nobody can do better than they're currently doing if they
tried to do something else, except for this optimal decision.

Okay, so two types of environments that we usually study could be
basically broadly divided into something that we call market behavior,
where agents do not know that they actually can affect the outcomes of
what is going on in the environment. And then the other types of models
where there is \-- this is what we call strategic behavior, where agents
are explicitly aware of the type of effect that their own actions have
on other players, on other participants in the model, or on the
environment in general. But both of them would still have the same sort
of a definition of an equilibrium. Again, the best you can do in the
given environment. These models do provide us with the ability to make
some predictions about the behavior of our agents, and that's, again,
what we call equilibrium outcomes of these models.

Now, broadly speaking, we have micromodels and macromodels. Micromodels
do look at a single market usually and treat everything else as
constant. Macromodels in general look across the entire economy and look
at how behavior in one market affects the behavior in some other market
in the economy.

Now, underlying assumptions related to the beliefs and behavior of our
agents are what we call rational behavior, and they \-- what is \--
basically what we mean by this is that these agents have all usually a
complete knowledge of the environment, or if they don't, it is up to
some basically stochastic error, and they also contain and have enormous
computational ability. So in some more complicated models, these people
are able to solve very complicated dynamic programming models and
problems over infinite horizons. They also have great calculation
ability, and basically, they have knowledge of the environment that is
actually larger than knowledge that any of us as modelers do have. So
what we impute with these agents is a complete knowledge of the
environment while we as modelers do not really know the exact way in
which, you know, our economies look like.

So there's a lot of basically computational ability and expertise, and
these models in general can serve as a useful benchmark, but they also
have a \-- there is also quite a bit of problems with them, as we have
already heard yesterday. And, you know, if you look at what any of the
sciences is all about, at the end of the day it is about trying to
explain, and social science is to explain behavior of people; in our
case, behavior of economic agents and how that affects economic outcomes
in various areas.

So with these equilibrium models, what has happened is that they failed
\-- they basically failed to capture a lot of features that we observe
in real world in terms of economic time series or in terms, say, of a
lot of evidence that comes from the experiments with human subjects. And
thus, they basically failed to capture all the \-- all the dynamics
that's observed in the real world.

Now, these problems, conceptually, where do they come from? Well, again,
as I've already mentioned, agents know more than even our \-- our
theorists who are modeling these agents know, and in some sense,
everybody in these models is too perfect for any real dynamics to take
place. And by which I mean the following: say you're an investor in one
of these theoretical asset pricing models, and there is a bunch of other
investors who have to make their portfolio decisions about where and how
to invest. Well, in our equilibrium models, our agents would make this
optimal portfolio decision right at the beginning of time. They would
formulate their optimal portfolios, do the necessary trading,
investment, buy and sell, and that would be it for the rest of time, for
the rest of history in a model. And this is because they've taken
everything into account, including the features of the stochastic
process that might be governing dividends or other things in the model.

They do know the probability distributions of things as well, right?
They will not know the actual realizations \-- realization, but they do
know the moments of these distributions. As a result, one of these
classical asset pricing models, say, would never be able to basically
predict or explain the features of the actual financial markets that we
observe where there is excess volatility and excess amount of trading
that takes place all the time. And again, this is because they are too
smart. They don't need this, and the outcome is basically, very kind of
a static behavior, even though we try to put in the dynamic component
through putting in sort of an infinite number of periods through which
they live.

So where the departures are going to be is to say that these agents, in
fact, are not so rational as in our neoclassical theory, and that in
fact, that they're boundedly rational. Now, that departure means that
basically we are getting many free, you know, degrees of freedom here,
because we can assume various things about how bounded these agents are.
They can be just slightly bounded, you kno7w, they just can have some
slight imperfectness in terms of their calculation ability and so on.
And we can go all the way down to assuming that they're very kind of
dumb and do not have much of \-- much to say about \-- about the
appropriate behavior.

So we have to \-- the way that I approach this modeling is through \-- I
would call for this talk, evolutionary adaptation, and that basically is
a very simple algorithm based on the following: agents form different
beliefs, they have different strategies, and over time, in response to
the environment and the payoffs that these strategies are earning, they
adapt, and they do this by adopting the strategies that belong to more
successful agents, if they get to see them, and through occasional
experimentation. So it's very \-- fairly simple, but as you will see, it
does generate a sort of powerful dynamics. And even though these agents
are not going to be very smart, it turns out that in aggregate, a lot of
\-- many times, in fact, they do behave as if they had all those \-- all
the computational ability and knowledge of rational agents, even though
it's not intentional in their case.

Why use these, and how \-- what are the criteria to \-- which we can use
to say, "This is better than some other way of modeling bounded with
rational behavior?" Well, in my work, what I've done is always tried to
basically compare the behavior generated in these models to the behavior
that's observed in real world data on macro or micro, basically
empirical evidence, as well as on the data that is generated in the
experiments with human subjects.

I should probably here mention just briefly what we mean by experiments
with human subjects, since the audience is probably not familiar. So one
of our ways of generating evidence to test our hypothesis and our models
is to basically put our, what we call subjects, basically human
subjects, into usually a lab. Put them in front of the terminals, and
give them instructions on how to behave in a given \-- in a given game.
And the instructions usually describe a certain model that we are
interested in. The subjects participate as actually our economic agents,
and they make these decisions over time. And to make it really sort of
be worth their while and their time, we pay them. And the payments are,
of course, related to how well they do in these economies.

There's a long \-- it's a lot \-- it's a huge discipline now. There is
lots of things to say about it, whether it's good to do it or not, but
anyways, this is one of the ways in which we can actually generate data
and where we have more control over what is going on than if we just
take a data set that's been collected from macroeconomic time series, or
some other second source of information. Okay, so that's the adaptive
behavior I'll be talking about. I will still have my environment, and
agents will have preferences, but they will not be rational, right? They
will have this sort of adaptive behavior in there. And so we're going to
put them in different situations to see what is happening, whether these
situations generate interesting behavior, by which I mean whether, in
fact, this behavior corresponds better to what we observe in, again,
real world.

And to \-- I was thinking about this definition of complexity, and so
I'm going to use some of what Carl said yesterday. One of the things he
mentioned was homogeneity versus heterogeneity of agents, and their
behavior. Well, our equilibrium models characterized by homogeneous
agents, they basically always do the same because if you know it all,
then you're all going to do the same thing because there is usually one
optimal thing to do.

And so our agents in our models are going to be heterogeneous. There
will be some other dynamics, and these dynamics are going to be
different than equilibrium dynamics. He also mentioned random matching.
For the time being, in the models that I'm working with, there is just
random matching but it's easy to add a structure, some sort of a
network, and it's not far from \-- these models do have ability of
incorporating some other sort of a structure in them, and then, in
addition, no feedback as a characteristic of classical economics,
neoclassical. There will be learning and adaptation. And this learning
and adaptation is going to result in changes in agents' decisions and
what they do in these environments. They will affect environment and in
turn the environment is going to affect their payoffs. And then again,
the way they change their behavior. So there is \-- we call these
self-referential systems because there is this interaction between the
agents and the environment. By environment, I don't mean just the
physical environment in which they're operating, but also other agents
in the economy. Everybody's decision affects everybody else's payoffs
and behavior.

And as the last one here, and I think Scott also mentioned this one as
an emergent phenomenon, basically we are going to see that there will be
non-trivial relationships generated between micro and macro phenomena.
So agents will be doing something at the micro level, and
unintentionally, this is going to create something different which we
can call emergent phenomenon at the macro level. Okay, so again, what
\-- another thing that Scott talked about, and it is again about complex
systems and these emergent phenomena, there will be a difference between
micro, basically, individual behavior, and what we will observe at the
macro \-- at the macro level.

And a lot of times, in fact, the behavior, as I've mentioned already,
that is quite simplistic in the way it's \-- it's modeled is going to
result in the outcomes that are quite close to the optimum outcomes of
some sort of a equilibrium model. However, the dynamics are not going to
be static, as the equilibrium outcomes would predict in this same
environments. It's also going to be bottom up approach modeling. It is
what is called agent-based modeling, and I'm going to talk about
explanation versus prediction, what we can do about these models, and
how much we have succeeded so far. Another thing I will emphasize here,
and this is always what I have to worry about because I always have to
stand up and usually defend, you know, the things I'm doing, is just to
insist on empirical validity.

And why is this important? It is important because that is, you know, a
good way of saying, "This approach of modeling is better than the
standard neoclassical way of modeling things. I can explain more of the
\-- what goes on in real world than you guys can, and it makes \-- also
makes sense." It's all based \-- because it's a sort of evolutionary
dynamics, it's all based on the exactly impact of the environment on the
payoffs, and that affects subsequently on the behavior of these agents.
And the second thing I always \-- is, which I think is also very
important in these studies, is robustness. What I mean by this is
repetition of the same, you know, things under different conditions,
examination of how that is \-- how that affects the outcomes, how the
changes in the parameter values of the model, in the number of agents in
the model and so on, affects the outcomes. So that once we make some
claims about what the model does, we have, basically, the backup of all
these robustness tests that we perform.

Okay, so I'm going to talk about \-- these are general, you know, many
applications of this sort of approach. Game theoretic, coordination
games, signaling games \-- you can assume there's some applications in
endogenous growth and monetary policy, evolution of organizations,
design of trading institutions, international finance, and others. I'm
going to talk here about two applications which I think \-- hope will
illustrate some of the features and potential of this approach that
might be useful for the types of things that people in the audience work
on, and even though the topic \-- the topics themselves are certainly
far away from any sort of \-- any of the topics in the sort of area of
population health. And I chose two applications, one is basically \--
two different ones, coordination game, and the other will be related to
the behavior of the exchange rates and currency crisis. And the
environments are going to be different, but of course, there will be
common elements of the two environments as well.

Okay, so here is a \-- what we call a team production game, and there's
going to be N players in this game. The best way to think about this
game is take an example of a few people who have to work on collecting
survey data, and they have to spend a certain amount of time, or hours,
doing this. Now, basically, the \-- nobody wants to \-- and they're \--
in all of this, they'd represent different, basically \-- they're
collecting different parts of the data set such that at the end of the
day, the person that collected the smallest number of data points is
going to \-- or elements is going to determine the size of the total
data set, which serves some useful role.

So, what is \-- what happens here now in this team production game?
Well, here are the payoffs. Hmm. The payoffs of our players and \-- so
basically they depend on \-- there are two \[inaudible\], A,
\[inaudible\], and zero. E minimum is the minimum effort that person in
this group of N players puts in, and then the I is the individual
players' effort. So basically, the random part of the whole equation is
determined by the person who works least. While your own costs are the
costs that you incur as costs of your own time. So basically, what
happens here with rational agents would be that nobody wants to work
more than what a minimum effort person does, right? Because you're just
going to incur costs while the payoff on the other side is going to be
determined by this minimum time put into that. So there is a tenden \--
there is a strong force that pulls everybody back \-- you know, to sort
of a small \-- to the lowest common denominator, to the lowest amount of
effort that can be put in, because you're going to be worse off by
working more than somebody who works least.

However, overall, the game is structured in such a way that if everybody
worked \-- put in the highest possible level of effort, that's going to
be what we call payoff dominant for every player. In other words, that's
going to be a situation that is better for everybody. And this is the
game that we call usually a game of strategic uncertainty, and the
uncertainty is exactly in this thing. How much are other people in the
group going to work? Usually there is an assumption that you cannot
directly observe the hours that other people put in. And it's a very
common situation for many different working environments and
relationships, contracts, and so on.

So basically, what economic theory would predict here is that, you know,
any of these level of efforts that are feasible, and we can think of
some integer numbers from one to N, are actually going to be these
equilibrium outcomes. In other words, whatever level of effort you put
in, you want to put that level of effort to be equal to the minimum. And
it can be in any of these numbers, but everybody works the same amount
of time. In particular, everybody can work the minimum or everybody can
work the highest number of hours that is feasible.

And so basically, economic theory is silent on choosing one of these
equilibria. Why? Because there are all \-- there are some criteria that
would say that what we need to do is use what we call pay of dominant
equilibrium because agents are smart enough to know that that's going to
result in the highest payoff, and other criteria that say you want to
basically maximize under worst possible conditions, in which case you
will go to the lowest level of effort, and thus the whole econ \-- you
know, the whole group is going to end up with the lowest possible
payoff.

This type of environments have been used extensively in sort of a
literature that deals with modeling macroeconomics and how crises and
boons can arise just based on people's sort of expectations and changes,
and their beliefs of what other people will be doing. And so if you
think that nobody else will be investing or working hard, then you won't
work hard as well. If you become optimistic and start thinking that,
"Oh, you know, everybody else will be investing a lot," you'll do the
same.

So, now with the experiments with human subjects, we put human subjects
to play these games, have shown \-- and this is overwhelming evidence
that's been replicated many times \-- is that basically the outcomes of
these experiments depend on the size of the group. And generally
speaking, small group sizes do tend to go and work harder and get higher
payoffs. Now, this is the environment where we do not really \-- we
cannot communicate to the other person, right? We \-- the person is
anonymous, we only know that we are playing the same game with the same
person repeatedly. And so group size of two or three people will go and
work the hardest possible amount, while if you go to the \-- and put in
ten to 14 people in the same group, they converge very quickly to the
minimum level of effort. And again, this is pretty strong evidence of
this.

So what do we want to do here? We want to see if we can come up with
this model of adaptive behavior that is going to generate some dynamics
and see how well these dynamics, first of all, capture the experimental
evidence, and whether we can say something more about why \-- why we
observe these sort of coordination problems. So what I'm going to do
here is show you the way I'm going to represent our players, and it's
going to be by these strategies so that \-- so what is happening here is
my \-- basically, I have these positions in these strategies, P1, P2,
P3, and P4, and what these positions are telling me with these numbers,
two, four, one, and four, is what the player is going to do given that
the minimum level of effort in the previous period was equal to one,
two, three, or four.

So suppose that he was informed that in the previous period, minimum
level of effort was one. He looks up the position P1 in his strategy,
and that position instructs him to play a level of effort equal to two,
and so on. If the minimum effort from the previous period was equal to
two, then this strategy suggests that he should play four and so on.
Obviously this doesn't look like very rational behavior, but we do start
our players here exactly randomly generating these strategies. So the
number of these positions is going to be equal to the number of possible
levels of effort, and they are going to tell the \-- instruct the person
what to do given what has \-- what happened in the previous period.

So they're going to play this repeatedly. And they're basically \--
there is a whole population of these players, and they're going to be
split into groups of different sizes, so we're going to change these
groups. And so, what we can see here is that this is one type of an
equilibrium that can occur in this environment, and this is that
everybody is playing the maximum level of effort, four. And this is
basically \-- so if somebody played four in the previous period,
position four instructs the player to play four. And to be in this
equilibrium, what is required is that everybody has a number four at
position four in their strategy. Important thing is that other elements
of this strategy then become irrelevant and they will sort of serve the
role of what we could call genetic drift. In other words, this can be us
\-- as long as the strategy has four here, it can have other elements
here because they are irrelevant for that particular outcome. It will
turn out to actually be relevant and important for the dynamics of this
model.

The second type of a behavior that we can observe is what is called \--
what we would call cyclical behavior, and this is the behavior where \--
so somebody played two in the previous period. We look at position two,
the strategy instructs this person to play four. Well, they play four,
so in the following period, if the minimum effort was four, the person
plays two and everybody else plays two. If they play two in the
following period, position two instructs them to play four. So they
would be cycling back and forth between these two levels of efforts,
just based on this coordination.

So, here what we have in those, as I've said \-- as I've mentioned
already, it's what's called neutrally evolutionary stable strategies. In
other words, if you're thinking about adaptation of these strategies
over time, these strategies, again, can have different elements here and
still be an \-- you know, still be the equilibrium outcome in this
particular situation. However, they can be invaded by strategies that
will have different elements in other positions, and then as a result of
that over time, enough of basically experimentation is going to bring
about a change, and the change in the sort of an average level of effort
that is played within a group.

So, they are split into these groups of different sizes, and we are
going to basically examine what the outcomes of these \-- of these are,
and what they \-- after repeated interactions, their strategies are
going to be updated using basically evolutionary algorithm based on \--
basically, it can be genetic algorithms, stochastic replicator dynamic,
or something else. But the main ingredients of these algorithms are that
agents are going to copy strategies of those agents who had higher
payoffs, and the frequency of these strategies increases over time. They
overtake the population, and then at some point, there is some
experimentation with new and different things. And so that's what they
will be doing here. So, we have the fitnesses evaluated, and then
updating the frequency and, again, the frequency of those that are
better fit increases in the population, and then there is
experimentation with different things.

Now, one possible \-- you know, if you think about this process, you
might think that, in fact, this is going, at the end of the day, to sort
of converge to a specific outcome as, you know, better strategies will
be taking higher and higher fractions of the population. But because of
the specific type of this model, it'll turn out that this type of
environment never reaches and never settles to a particular outcome.

Okay. So what happens here, it turns out that all different group sizes
do tend to play all kinds of equilibria. They go through different \--
though different levels of effort. The small group size spends most of
the time playing the highest level of effort, which is in this case,
level of effort goes from one to eight, and you can see that group size
G of two has placed highest effort 84 percent of the time, and then this
number decreases as we go and increase the group size here. So that as
we go to group size of six, seven, eight, ten and 12 and 14, you can see
that this basically \-- the groups play different \-- different levels
of effort, and this is all done within a single simulation. So within a
single simulation, groups of the same given size do go through \--
through different levels of effort. And at the same time, they do play a
high percentage of best response, by which we mean they do spend most of
their time playing the same effort within the group, equal to the
minimum effort. So there is an element of this best response in it all
the time, which I talked about before, and so they are \-- it's not just
wandering around and doing whatever. They do coordinate on this in a
particular way, but they do change over time. As a result of what? As a
result of these \-- basically things that have \-- they're taking place
on part of strategies that do not matter at a particular \-- at a
particular moment.

So this is example where \-- I didn't talk about this P \-- P0, that's
just what they do in the initial time period. This is an example where
we can see that the players \-- these are the players, one to 14. The
whole population consists here of 40 agents. This is just cut for \-- so
that it can fit the page. And you can see here that this population,
basically, at this time period, is in an equil \-- is in an equilibrium
where everybody's playing the level of effort five. So everybody plays
five, then in the following period, the players look up their
strategies, position P5 tells them to play five, everybody plays five,
and so on. So this goes on for some time.

However, you have to look at other positions as well, and these are the
positions that will change over time, and then affect what happens to
the \-- what happens to this group of people. They will not stay all the
time playing five. Another thing to note here, if you look at other
positions, there is \-- like, P1 will play five, P4 will play five, and
so on, and this is what we would call is sort of serves a protective
role. In other words, these positions kind of evolve so that there is a
protection against some sort of a mutant, so if by accident somebody
plays one, then the player goes and says, "Oh, minimum effort was one,
well, I'm going to play five," and they get back to the higher payoff
equilibrium.

But there are other things going on, and mutation will create different
things in there. So after a few hundred periods, it's the same
population, it's the same simulation with the same group size. They're
no longer in that equilibrium playing five. They have evolved to
something that could be called, basically, two cycle, and where they're
playing, basically, cycle of three and eight. If you look at the
position three, that means somebody played three in the previous period,
minimum. I'm going to play eight. And this is like that for everybody,
right? So, everybody is going to do the same thing. Okay, so the
following period they play \-- they play eight, and then they get
information of the minimum effort of eight. Then they say, "Okay, now
I'm going to play three." Everybody plays three; they're back to check
P3, which is eight. So there's cycling in this case that resulted just
as a, basically, of the changes that were taking place and the parts of
the strategies that were not important while they were playing five. And
so, that's this role. So, in general, in this sort of behavior, one has
to be careful about what happens in other parts of your, basically, rule
or strategies, that they're not relevant for a current situation, but
that might along the way trigger, basically, different changes in the
behavior and in the environment.

Okay, so I'll quickly say about the two-currencies environment, which is
completely different \-- different applications. Agents make \-- in
these environments\-- savings and portfolio decisions, and they have to
decide on how much to invest in \-- in each of the two currencies.
Again, utility is based on their investment decisions. Now, what they do
is going to affect the rates of returns on these currencies, and that in
turn affects their payoffs, and changes their behavior. So then we have
this evolutionary approach. What happens in these economies is that we
observe, basically, persistent fluctuations in an exchange rate, which
is quite the opposite from, basically, the usual neoclassical models.
Usual models of international finance cannot generate any sort of the
volatility that is observed in the real world. However, here, these
agents chasing higher returns all the time are going to be perpetually
sort of switching and changing their portfolios so that we end up with
these persistent dynamics, completely different from equilibrium \--
equilibrium behavior.

And, basically, there has been a lot of work done by myself and some
other people about comparing the features of these models to the real
world data and, again, to the experiments with human subjects. So to go
back to Carl's point about \-- we have to worry about the tails as well.
Well, it turns out that these distributions that are created have what
we call fat tails and high kurtosis, higher than normal distribution,
and that is exactly what's observed in real world data. There are also
chaotic time series, indicating this emergent complex behavior, and the
\-- the agents do actually perform, in terms of their utilities, very
close to what the rational agent would be \-- would be doing in the same
environments. Okay?

This is a similar thing but only with, sort of, a developed and
developing country. Here, agents are updating on their probabilities
that a devaluation will take place. High probabilities of devaluation
\-- there will be pessimistic investors investing in a safe asset; low
probabilities of devaluation, optimistic investors investing in a
riskier asset. Then there is sort of an average of opinions that forms,
and everybody who falls below the average is going to end up investing
in a risky asset because they're, on average, more optimistic, and those
who are investing in \-- who are above the average will invest into a
safe asset. They're going to be above the average.

So, again, there is \-- they receive returns from this. In general, a
riskier asset will give them higher returns except if there is a
devaluation, which can take place with a \-- in a developing country, in
which case, that return gets to be smaller than the safe \-- return on
the safe asset. Again, strategies updated in a similar way. So here,
again, there is another thing, we do observe the phenomenon of recurrent
crisis, which is again something that characterizes the real world.

So, the model does not settle into a particular outcome. They \-- they
do basically fluctuate all the time. They are moving back and forth into
the market, and out of the market, and so these changes are driven by
changes in their estimates and differences in their payoffs. So,
basically, again, if you look at the time series properties, they fit
real world data well in terms of duration, frequency, and so on. And I
just wanted \-- this is like a picture of, basically, of how the
estimates of the probability of devaluations go up and down, and every
time that they go up, the \-- there is a, you know, very much \-- there
is a possibility of a devaluation, and devaluations which are presented
here on the third panel, here \-- these green lines are these
devaluations. If you look back on the probabilities that devaluation
will take place, they have hikes here, which means people get out of the
foreign market, and as there is \-- the devaluation takes place.

So, here is a simulation that shows, over a certain period of time, how
this population of beliefs moves from being very optimistic to being
very pessimistic. So here, what is on the X axis is basically the
fraction of agents that, well \-- it's the probability that there will
be a devaluation, and so zero is the small pro \-- right \-- very small
probability values closer to the right hand side are very high
probability. So at this point, there is no devaluation. People are
earning higher pro \-- higher payoffs if they're in the foreign market,
so everybody's trying to be optimistic about the market and they will
all try to be \-- to be squeezed into the \-- as, as you know, to have
as low as possible probabilities, because there is an average, and there
will still be some who will have to end up in the safe asset as \-- as
relatively more pessimistic.

So they're all moving towards left, and you can see that there's a tail,
that distribution is skewed. And this is still going on, but there are
some people who are now to the \-- going to the right. There is a blue
vertical line showing the average payoff. So this is just a genetic \--
sort of a genetic drift that's taking place. There's some more that are
more pessimistic, meaning less funds in the \-- in the emerging market.
And now this causes devaluation. So devaluation occurs, everybody who's
stuck in the emerging market is going to suffer now, lower returns, and
you can see there's still devaluation and there are people moving
outside of the emerging market quite quickly. Moving further, but
devaluation is still taking place, and now they're how \-- how \-- how
they can get out of the market is by increasing their estimates of
probability of devaluation. And so, basically, they have to take \-- get
out of the emerging market enough as to make it possible for the
devaluation to seize, to exist. And this goes on all the time.

So that was my \-- what I wanted to just say about the \-- is that,
basically, these are self-referential systems. Interacting of these
agents' adaptation over time, dynamics driven by changes in these
strategies, and dynamics depend on the underlying environment.
Everything depends also on the details of the model and parameter
values. And so if you're think about modeling similar dynamics in terms
of population health, it is easy to sort of see the same sorts of
environments if they're set up in the appropriate way, to \-- for \-- to
address issues in, basically, related to health. And then to, kind of
model and think about the ways in which \-- in which we can set up
strategies or beliefs or behavior of people in your models that might be
of interest, and how they change over time in response to the changes in
environment and vice versa. Okay? That's all.

\[applause\]

Female Speaker:

Okay, I'd like to thank the three speakers this morning for their
interesting and thought-provoking presentations. And I'd also like to
ask them to come up to the table for the question and discussion
session. And I'd like to also invite our moderator up, Dr. Michael Wolf
\-- Wolfson, who I mentioned is the Assistant Chief Statistician,
Analysis and Development at Statistics Canad \-- Canada, and he clearly
wears many hats in this capacity, including analytical and modeling
activities. And I'll let Michael take it from here.

Michael Wolfson:

Thank you. Can I start by just checking on our time constraints, because
we have lunch scheduled for noon, which is in twenty minutes and \--

Female Speaker

We're about 10 minutes behind now, so \[inaudible\].

Michael Wolfson:

And a shorter lunch.

Female Speaker:

Yeah.

Michael Wolfson:

Because I actually prepared a few comments, so I'm going to usurp a
little bit of the question and discussion time, but anyways, we \--
hopefully it'll all be lively anyways. We've had three very stimulating
and varied presentations, and I want to exercise the prerogative of
having the microphone for a second. First, to pick up on Scott de
Marchi's things, where he talked about the need for the right level of
complexity or simplicity. You might call this the Goldilocks principle,
not too hot, not too cold. But it really does, to my mind, bring forward
the point that this \-- there's an art of modeling, not just a science
of it, and choosing the way you paramaterize or structure things.

He posed as alternatives but it seems to me one should think also of
them as complements, the statistical estimation process, all the
econometrics that you seem to feel you're forced into teaching, and the
agent-based or network models that you showed and Mark showed, and
Jasmina. And as we move from toy models to some of the things that we do
at Statistics Canada, which are more what I would call "industrial
strength microsimulation models," obviously we need to draw on data and
empirical regularities.

I'd observe a bit of a tension between Mark Newman's and Josh Epstein's
model. Mark showed some networks, for example the one on sexual
partners, which he characterized as dynamic, but I think it was dynamic
in a very narrow sense.

There was changing partners over time, but the networks themselves are
fixed, whereas Josh was showing people running into the basement, and it
seems to me building in some kind of endogenous response of these
networks in infectious disease outbreaks, for example, is \-- is \-- got
to be a crucial part of the real world exercise. You know, for example
this idea of social distancing varying as a function of fear. More
generally, we've been exposed to a range of modeling methods. A key
theme of all of them has been an acceptance of a degree of complexity
that forces us to abandon some crucial mainstream methods, for example,
random mixing and differential equation models with a combinatoric
explosion, if you have many different compartments in epidemiology, and
omniscient maximizing frictionless agents in economics, something I find
personally distasteful.

I think we can array these methods. We heard a bit about network
modeling from Mark Newman, agent-based models with fixed behavior, and
then Jasmina had agents with adaptive behavior, and if I could briefly
characterize them, I think the network models are very helpful for
informing our intuition on a number of things, but I think they're too
simple for real world questions. The models with fixed behavior of the
agents are practical, and they can still incorporate quite complex
behavior and some endogenative response. The ones where the agents
actually change their theories of the world is ultimately the most
realistic, but very difficult, and I think it's signaled by \-- in the
last paper \-- how highly abstract the models are, even though they give
you interesting textures and flavors of results.

One more comment before I open and ask \-- hopefully, peop \-- yes,
there's at least one person at the mic. You know, and here we get
questions from the floor, is that these \-- you know, this was the views
from field A or the views from field B, and my sense is that all of
these views are still very far away from population health. The \-- you
know, that, if we recall George Kaplan's opening remarks, you know, he
was talking about the lack of correlation between money spent on
healthcare and health as measured by life expectancy and infant
mortality, the relationships between income and income inequality and
polities \-- so those are two different levels \-- and health. The story
in Eastern Europe and Russia about the decline in mortality, which
couldn't be genetic; they don't change genes that quickly.

So the challenge that I think we still face in this conference, which
I've found quite stimulating so far, is can we find elements of their
various models and analytical approaches that can be brought to bear on
the big questions in population health? For example, how do we explain
or understand the socioeconomic gradient in health? And I'll shut up
after just saying my conjecture or answer to that question is we can,
and it's to mix a, you know, the ubiquitous single-equationitis of much
empirical social science and epidemiology with what I would call
metasynthesis, sort of gluing things from disparate topics together and
using an agent-based model as the grand container that holds and brings
all this stuff together and makes it coherent. But \-- Alan, first
question.

Male Speaker:

Thank you. First question is for Mark, and then I've got a brief
follow-up question for the whole panel, if I may. Most of my work is
around evidence-based decision making in healthcare, and in particular
the question of how research moves into policy and practice. How do we
do that? And as I listened to your presentation, Mark, I was intrigued
by the idea of positive epidemics. Essentially, if there's any kind of
new evidence-based guideline or something, it's going to move through a
\-- the network exactly the same way that an infectious disease does,
and we can model it very nicely because often we know in advance the
network structure and lot of the individual characteristics and others
that may moderate that response. Have you done any work with positive
epidemics?

Mark Newman:

I haven't but this is something that many people have looked at, so
another use of these models of social networks is to study how ideas
about public health move through communities, best practices, and how
things are adopted by members of the population and so forth. And as you
say, those spread in a way very similar \-- the ideas spread in a way
rather similar to the way the infections spread. So there has been,
actually, a lot of work on that kind of thing, starting from the 1960s
onwards. People have looked at things like the adoption of practices in
hospitals. They've looked at the adoption of contraception in rural
communities. There's \-- there've been a variety of studies on things
like that. So, no it's not something I've done, but it's certainly
something that quite a large community of people are looking at.

Male Speaker:

And then the follow-up question for the whole panel. In the real world,
I think what we are looking at is a \-- an interweaving of teams and
networks. Teams being the groups of people who have some kind of formal
responsibility to work together, and networks being more informal \--
networks of the kind of people that end up working with you to get the
job done. So that what we need is a blend, if you will, of Jasmina's and
your methodology so we can study teams and networks in combination over
time. Does the panel have any thoughts about that?

Jasmina Arifovic:

Can I? Yes, as I also mentioned in my talk, that's definitely an
important next step to be done, and I think that that can really bring
some interesting results, blending in networks and studying those
together with bringing in teams, which is part of our everyday life in
different \-- in different aspects. And I would just like to make a
quick comment on this \-- the models that are presented here today are
two very simple models. The idea was to just kind of try to convey this
idea of what it is about. However, agent-based modeling has made
progress in terms of putting more things together. We have to be
careful, though, in how we do it, so that we still can keep trea \--
track of the dynamics and that they actually mean something that we
think is interesting. But I definitely think that is an important next
step to be done.

Michael Wolfson:

Other \-- Mark or Scott? No. George?

George Kaplan:

Well, I think you want \-- yeah, let's alternate.

Michael Wolfson:

Okay. No.

Male Speaker:

They'll \-- they'll bring it up.

Michael Wolfson:

Start talking and see what happens.

Allison Pinto:

Well, I'll just talk super loudly. My name is Allison Pinto, and I'm in
Tampa, Florida, and I'm a child psychologist by training, so one of the
things that I'm interested in is how we can apply these different
frameworks and methods to understanding population mental health, and in
particular, population early childhood mental health. And one of the
things that I'm really curious about is how we might begin to use the
different methods that \-- that you all have talked about this morning
to inform our understanding of what it would take to actually transform
whole communities so that we see radical improvements in the health and
well-being of, in my case, young children. And so there were a few
things that were striking me across the presentations, though, because
it seems like you're talking about social network analysis as one
approach, and then we're talking about agent-based modeling as another
approach, and in each case, though, there's this talk about
understanding the networks and interactions between agents as they
reflect or don't reflect states of equilibrium.

And so one of the things I just wanted to clarify with you all is, is
the emphasis on equilibrium, is that \-- I guess I've been surprised by
that here, because one of the things I \[unintelligible\] in complexity
approaches is that we're kind of stepping away from equilibrium as the
emphasis. I'm thinking more in terms of self-organization. So that's one
question. Is \-- your approaches using these different methodologies as
a means of understanding equilibrium, is that the same as understanding
self-organization of human systems, or different? And then \--

Michael Wolfson:

Let's pause there.

Allison Pinto:

Okay.

Michael Wolfson:

Okay, that was a long question. Does anybody want to comment?

Jasmina Arifovic:

I can comment. Yeah, I realize that as I was talking it kind of came out
as probably the \-- a focus of the \-- of the analysis. In fact, why I
used this was because in some previous talks, the microeconomic
phenomena that we talked about came up and people \-- and there was \--
and then people said, "Okay, you know, even though there is this other
thing going on at the individual level, at the macro level we can see
something that corresp \-- you know, that is close to these." But by no
means is this meant to be something that is \-- that studying just how
far we are from the equilibrium or whether we get there. In fact, all
these dynamics are actually persistent in terms of being away from the
equilibrium.

Now we have to use some outcomes here as the reference points, in this
case, just to make, you know, to be able to explain some of the things,
and to try to understand them. But they are moving away \-- none of
these, well, at least the two that I presented, are not really trying to
say, "Okay, we can get to equilibrium." It is the behavior that's
observed, but is it not \-- it is moved away from the equilibrium
prediction would be.

Allison Pinto:

So, so \-- but there is still an assumption that we're orienting around
equilibrium as opposed to self-organization, or are you using those two
terms to mean the same thing?

Jasmina Arifovic:

Well, I'm \-- I don't see them as exclusive. I just use those as
reference points as to where we are in our self \-- self-organization,
so that's \-- maybe you \--

Allison Pinto:

Because I guess one of the assumptions, it seems here, and it's a
challenge we have, and then I'll stop there, but is \-- we don't seem to
be asking the question of, when is illness healthy, right? That's the
question that, I guess, I'm having a \-- some trouble understanding how
we approach using the methods that we've described, and I don't have the
sense that we're talking about the fullness of human experience. So if
we're talking about human complex systems, then I've heard talk about
rational beings, so we're talking about thought and we're talking about
behavior, but where does human feeling and where does human physiology
get in the mix of micro self-organization so that we can understand the
phenomenon of health or mental health at a population level?

Micahel Wolfson:

We should move on. I'll take that as a profound comment that we need to
reflect on. George?

George Kaplan:

Okay, I'm going to try and be short, and \-- and I think this applies to
all three of you, but it could also equally apply to the final panel. I
take it that the goal of modeling is to somehow represent the world of
animate and inanimate objects, and how they come together and move
apart, and what happens as a consequence of that. And, you know, the \--
we're happy when the \-- the relationship between the \-- our starting
conditions and the ending conditions, what we observe in the model, is
in some way isomorphic with what we believe is happening, or what we
observe is happening in the real world.

But my question is, there must be within that isomorphism, many, many
possible sets of mechanisms that would generate the same relationships,
and that seems to be tremendously critical if we \-- I guess it's not a
short question \-- that tends to be tremendously critical when we think
about taking that knowledge and translating it into practical
interventions, et cetera. And I would \-- I would ask you to just
comment a bit on how do we know when the model is, in fact, a nice \-- a
good model of reality?

Mark Newman:

So I think that's a really good question. You can build a model and it
gives some outcome and the outcome looks like something that happens in
the real world. Is that a confirmation of your model, or could some
other model have given the same result? I think this is a really common
issue in this kind of modeling, and you frequently see this kind of
mistake made, you know, the kind of, bears like honey, my wife likes
honey, therefore my wife is a bear \[laughter\], kind of logical
deduction. You know, because this model agrees with what you see in the
world, it doesn't necessarily mean it's correct, so \-- and I think that
the answer is that you have to have some other information that comes
from outside of the model.

For example, here you \-- in the kind of things that I was talking
about, if you actually have some knowledge from the public health
community of how this disease spreads and what the important mechanisms
are, if you really believe that you understand it, and then you build a
model that incorporates the things that you believe are important, then
that gives you more of a feeling that your model might be a correct one.
You could have made some other model with completely different
mechanisms in \-- that would have given the same results, but you
wouldn't believe it because it \-- it doesn't build on this external
launch that comes from somewhere other than the model. And I think that
you need to have that kind of external knowledge in order to develop any
kind of faith in the model that you \-- that you built.

Scott de Marchi:

Can I expand a little? I think that one of the goals of my talk is the
standards you would use when someone comes to you and says, "I have this
new independent variable, I have some term-wise non-linearities I want
to apply to it, and the mean squared error gets better." And you would
say to them, "Well, okay, that has to be true. There's always, you know,
systematic parts and non-systematic parts and you might be leveraging
one or the other." And so your reasonable standard would be, let's
generate new data or partition our sample into an out of sample set and
see if you do better on that. And the same has to be true of this.

I'm always not so sympathetic of explanations. I like predictions. And
in all of these models, if you fit it to one flu, let's say the 1918
outbreak, and without tweaking your model very much, you fit it to
another outbreak and it works, with just changing the parameters in
reasonable ways based on the differences in the cases. Then your betting
odds improve. I don't think the standards are any different for
computational models than anything else, and that applies to the parts
as well, the long laundry list of parts you could add, neighborhood
effects and all the rest. If they make the model better, they make the
model better, but that means you need to have an unobjectionable
dependent variable that you're using to evaluate. Which is a tough
standard.

Michael Wolfson:

Good. Next person.

Male Speaker:

Hi. It \-- it seems to me, as we think about moving to the end of this
conference, as w \-- should talk about next steps and sort of where are
we now? What are the big challenging issues and how might this whole
approach inform impacts on real people in the real world, which is sort
of getting from the modeling to the practice. And one of the things that
we haven't talked about much, but I think came up indirectly in several
of your presentations, was an attempt to go back and forth between
calibrating real time data in the real world, whether it's a laboratory
simulation or looking at the actual flight of airlines across the world,
and anchoring some parameters in the real world.

Well, if you think about other technologies, for example, the MIT Media
Lab developing real time sensors that can be worn and uploaded through
PDFs, so that you can envisage, let's say, the Oregon model of tracking
a whole city in real time, and getting volunteers to \-- to sort of wear
devices where you could, for example, in real time, gather data at the
individual level, perhaps even at the neurocircuitry level about how
people make decisions, but scaling that up to aggregate neighborhood and
community and city level. The idea of using these new technologies to
create a cyber grid or a cyber infrastructure at multiple levels of
aggregation, at different time units, from rapid-cycling individuals to
perhaps long-cycling community aggregate data, for example, would allow
us to go back and forth so that computer modeling could inform what kind
of data we should be collecting, that in turn would feed back more
accurate parameters for the next iteration of modeling.

So I wonder if you could just talk a little bit about, I guess what I'm
loosely calling a continuous quality improvement cycle between
practitioners and real world data on the one hand, and modeling, and
even hybrid models where you could imagine agent-based models being
integrated with higher order models as you go up to, say, dynamic
systems modeling. And how that might all work as a way of moving the
field forward towards models that model reality closer and inform
targeted and tailored interventions that might make a big impact on the
real world health outcomes. How do we close that gap?

Michael Wolfson:

Panelists? Who wants to jump \--

Scott de Marchi:

I think our moderator had a similar statement, which is if you have to
pick a distribution, don't pick it as a nuisance. Pick it based on real
data, which means you need people to have domain-specific expertise to
help you. You can't just throw data, though. I mean, one easy example in
AI lit is the face recognition problem in airports. So they want to
photograph you and, you know you're \-- you're sometimes occluded
depending on how the photo was taken and all the rest, and the pixels of
themselves aren't very useful. You need to derive a features base based
again on people saying that the length of your nose is a relative ratio
with the width of your face map matters rather than pixel data.

So at no step of the way, I think, are we every going to have modeling
where you can throw data at multiple levels and magical things would
come out. You always need to have this partnership with people that do
applied work to say, "This is the exact form the data has to take. These
are the measures that are parsimonious and effective in other practice
areas," and then build from that. And that is not often done. It takes
time.

Michael Wolfson:

Mark or Jasmina?

Mark Newman:

Scott also, in his talk, touched on this idea of choosing the level of
modeling, which we've heard from some other speakers at this meeting,
which I think is an important one. You're talking about developing these
very detailed data sets that give you a lot of microscopic information
about what's going on and would allow you to make extremely detailed
models, and those models can be useful for certain things. When you make
very detailed models, they give you good predictive ability. When you
make simpler models, they give you more insight, in many cases, about
what's going on.

And sort of the extreme example is if \-- if I really had all of this
data about where everybody was moving and what everybody was doing at
every moment, then I could make it \-- an entire copy of the whole city
or something on my computer, and it would give me zero insight. It would
give me no more insight than the actual city in the real world, because
it would just be a copy of it. But it would be ver \-- a very good
predictive tool for things that are going on.

So you can go from, you know, the one end of excellent prediction and
zero insight, to the other end of these simpler models, which really do
have a role to play because they actually help you understand what's
going on rather than just predicting it.

Michael Wolfson:

Can I sneak in one more question?

Male Speaker:

I've just got a comment, so it's going to be a short one.

Michael Wolfson:

Okay, fire away.

Alan Shiell:

Alan Shiell, University of Calgary. I'm \-- I'm just a little bit
concerned that we're digging a bit of a gulf between the systems guys
and the population health guys, and I think it's a function because the
system guys that we're talking to here don't know a lot about population
health, and some of the population health speakers don't \-- apparently
don't know a lot about the systems stuff.

So let me give you three quick examples where they have come together
and where in fact we are using these ideas. The first is \-- is
descriptive, so we're not getting into interventions yet, but work by
Spencer Moore, who's now at University of Montreal, has shown how the
place of a country in a trading network, in its networks, its place in
the structure, over and above GDP and education levels of women and so
on \-- it's place in that network structure independently predicts
infant mortality. So we know there there's a relationship between
networks and health.

The second area is in the field of social capital, which before it was
purloined, if you like, by Putnam and then Ichiro Kawachi, was a network
variable, and work of Valerie Haines at the University of Calgary and
Jenny Hurlbert and her partner, who's name escapes me, at the University
of Louisiana, has shown how network capital, how you're positioned in a
network, mediates the effect of trust, the Kawachi type of social
capital measures on health, and how we can use that network structure as
an intervention. They, remarkably, have got network data pre-Katrina.
They said, "a hurricane's going to hit New Orleans at some point, let's
go in and get network data." They've got social network data now.

And what that's showing is that the dense networks, typically of lower
socioeconomic groups, which are usually protective against health
threats, there were damaging, because if your network is all in the same
position and a hurricane hits, it wipes out your network. The people
that survived Katrina had diverse networks and could get out of New
Orleans. And then the third example I'll give you is a local one,
because amongst the best people I know who are using network ways of
thinking, and system ways of thinking to promote health, are here. I'm
talking about Barbara Israel's group, Amy Schultz working in the Detroit
\-- the Detroit east side, where they're using community development
techniques based on social networks, to empower local communities to
tackle broader social determinants of health, and doing it very
successfully as far as I can see. Thank you.

Michael Wolfson:

Good. Thank you. And it's lunchtime. Is lunch in the same place, you're
going to handle all that.

\[applause\]

\[break in audio\]

Male Speaker:

\-- a Michigan product, Kristen Hassmiller Lich, who's currently an
Assistant Professor at the \-- at University of North Carolina, Chapel
Hill. She received her doctorate from the School of Public Health, but
had her other \-- had one leg in the School of Public Health and one leg
in the Center for the Study of Complex Systems, I think it's fair to
say. So we're thrilled that she's able to come back and tell you about
some very interesting work she's done on the application of systems and
complex systems thinking to the tremendous problem of tobacco. Kristen?

Kristen Hassmiller Lich

Thank you so much. It's wonderful to be back, and let's see if I can
figure out how to get this to work. Let's see, where are we? Okay, here
we are.

Male Speaker:

There you go, okay.

Kristen Hassmiller Lich:

Okay. So, today I want to talk to you all about my dissertation
research, that I \-- that I completed while here, on the interaction \--
the association between smoking and tuberculosis. This is \-- this is my
attempt to sort out some of \-- many of these issues that we've been
talking about over yesterday and today in terms of \-- if you're going
to model, why? And what are the purposes, what are the benefits? How
simple versus how complex? So hopefully you all will be able to get some
insight on \-- on what I've learned through this process. I also want to
talk about \-- I want to talk about this example, hopefully because
you'll find it interesting, but also so that it may inspire \-- inspire
you to think about other population health examples.

I also want to provide some intuition on the model, and go a little bit
quickly through this because Carl and Josh Epstein yesterday did a \--
did a great job of that, but I'll see if I can provide a little bit more
intuition for those of you for whom this is \-- this is new. I also want
to give you guys some sense of why, in particular, I think that this
type of modeling work is useful. Let me start by just giving you some
background on tuberculosis so that this whole presentation will make
sense. TB is an airborne communicable disease. It's a leading cause of
death worldwide. An estimated nine million new cases of tuberculosis
occur each year, and two million individuals die of tuberculosis.
Tuberculosis causes one quarter of all avoidable deaths worldwide, and
is the second most important cause of \-- of death and disease from
infectious agents after HIV.

Growing up, sort of, academically in the tobacco policy world, I knew
very little about tuberculosis and was just absolutely amazed at what I
found. One third of the world's population is infected with TB. That's
really amazing, that's really mind blowing. This is a huge problem, and
\-- and among those who are infected, an estimated 10 to 15 percent will
go on to develop disease at some point in their lifetime, making this a
very complicated disease to wrap your minds around. So we'll talk a
little bit more about \-- about TB pathogenesis later but I just want
\-- want to make that very clear. So, a lot of people are infected, a
small percentage of them will develop disease later in their life, at
which point they are infectious. Okay, and although an effective and
cheap treatment for tuberculosis has been around for fifty years, the
global incidence rate of tuberculosis continues to rise.

The first \-- the first time that I really heard about tuberculosis,
took an interest in tuberculosis, I was sitting in a tobacco control
conference, and Sir Richard Peto had just finished a huge
epidemiological study in India and mentioned, as an aside, that
tuberculosis killed more smokers than all forms of cancer combined. And
that really blew my mind, because as someone with an interest in complex
systems issues, tuberculosis isn't like lung cancer or heart disease.
The disease risks don't stop with that individual who smokes, so if I
smoke, not only am I at a higher risk of developing disease myself, but
then I'm more likely to spread on my infection, increasing the risk that
you, my \-- my husband, or my neighbor, or someone I sat next to on a
bus, may be infected and develop disease later in their life.

So I started digging into the literature and I wanted to understand this
association a bit better, and what I found was that ever smokers, people
who've smoked, have a higher risk \-- unconditional risk of being
infected with tuberculosis, of developing TB disease, and of dying of
tuberculosis. These risks are not explained away by controlling for
potential confounders, such as age, sex, alcohol consumption, income, or
HIV status. There is a strong and significant dose-response
relationship, so the more an individual smokes, and the longer an
individual smokes, the higher are these risks. And lastly, there are
indeed feasible physiological mechanisms that have been proposed in the
literature that would \-- that would explain how this relationship may
be causal. Okay.

Let me just talk a little bit about smoking to sort of paint the other
side of this picture. One third of all adults over age fifteen in the
world smoke. That's 1.3 billion people. And smoking is known to kill
half of its long-term users, one quarter of those in middle age, between
twenty-five and sixty-nine years \-- years old. And although smoking
prevalence is stabler, or declining, in developed countries \-- in many
developed countries \-- that's not the case in developing countries, the
same countries where tuberculosis epidemics are often quite large. Okay.

Currently in the \-- in the US \-- in developed countries, the average
smoking prevalence among men is about 33 percent. That percentage \--
that prevalence in developed \-- in developing countries is higher. It's
50 percent, 54 percent in countries in transition. And it's much lower
in women currently in developing countries, but that is changing. Women,
in developing countries, comprise the segment of the population in which
smoking prevalence rates are increasing most dramatically, particularly
in countries in Asia and Africa. So truly the \-- the TB and tobacco
epidemics are on a collision course. These are important in the same
types of populations.

My motivation for this research is really threefold. First of all, the
impact of smoking on TB is not well understood, nor is it appreciated.
And this has changed over the past year or so. People are starting to
recognize smoking as an important risk factor for tuberculosis. But \--
but what they're hearing is that \-- is that the relative risk, and I'll
talk about this a little bit more, is on the order of two. So people who
smoke are twice \-- twice as likely to be infected with tuberculosis,
slightly more likely to develop disease. And it's easy to blow that off.
And so \-- so one motivation, one very strong motivation for this
research, is that I really think that it's a much stronger message,
rather than \-- rather than telling these relative risk values, this in
\-- the information about this individual level risk factor, I want to
talk about the population level implications. What does it mean at the
population level for smokers to have a risk of developing TB disease
that's two times higher than non-smokers?

Okay, so second, from the tobacco side, many developing countries are
not yet convinced that controlling tobacco is in their economic
interests. You hear this sort of thing all the time, that while smoking,
it's really just something that \-- that causes disease and death later
in life, and it's a small pleasure and it's just \-- it's just not a
priority. But tuberculosis is and so this association becomes important.

And lastly, current policies for TB did not yet address smoking as a
risk factor. And so a question is, you know, should they? Perhaps they
should. Smoking is a risk factor that we know how to change. We know how
to get people to quit. It's also identifiable \-- we know who these
people are who are at higher risk for tuberculosis. So let me talk a
little bit about traditional TB control. What's going on right now? In
1993 the World Health Organization declared tuberculosis a global
emergency, and so people got together and started thinking about how
they could make the biggest difference fast. And they came up with this
strategy called DOTS \-- that's the acronym and it's actually now pretty
much known as DOTS, but it stands for Directly Observed Treatment
Shortcourse. And the idea basically, the five principles are listed up
on the screen, but the idea basically is pass \-- so, passive diagnosis.
So someone comes into a clinic and they're coughing. You want to suspect
tuberculosis and test them, and if they indeed do have tuberculosis, you
want to treat them and treat them well. Okay, so the idea here is when
someone comes into the doctor, these are\-- these are easy \-- these are
easy people to reach, just make sure that you treat them as soon as you
can, and treat them well so that they are no longer infectious. Okay.

Tuberculosis is included in the millennium development goals for the
year 2015. The specific goals for tuberculosis are to reverse the
increasing trend in TB incidence rates and to have TB prevalence and
death rates between 1990 and 2015. A lot of modeling work was done at
the World Health Organization and elsewhere to establish process goals
to help reach these outcome goals. And these specific process goals,
sort of focusing on DOTS as the \-- as the main strategy, were to
diagnose 70 percent of new TB cases and to successfully treat 85 percent
of these diagnosed cases. And so the idea really was to expand DOTS
programs, so \-- so, in India, for instance, you \-- you want to try and
cover a significant percentage of the population with DOTS programs so
that people \-- people can be referred for \-- for good treatment. Okay.

Some current policy issues: so, first of all, the status in terms of
DOTS is that 83 percent of the world's population, estimated, lives in
an area that's covered by DOTS, so they have access to good, high
quality TB treatment. That \-- that's a success. Many countries are now
reaching, or are very close to reaching, these process goals that were
established, 70 percent diagnosis and 85 percent successful treatment.
But what's being \-- what's being realized is that \-- is that the
outcome goals are not \-- are not being seen. There's no evidence of
these outcome goals in many countries. So, so why is that? What was
wrong with the initial modeling?

And then, another \-- another sort of new policy debate is this idea of,
okay, well, we are starting to reach these process goals. What next? And
so there's now this new focus at the World Heath Organization where
they're \-- they're looking forward fifty years and they're thinking
about TB elimination. So what is it going to take to eliminate, to get
rid of tuberculosis, defined as \-- well, there's two definitions, but
the \-- the one that seems more reasonable is, to get TB incidence rates
to less than one per one hundred thousand people per year. And so, these
sorts of questions have also inspired and driven this research. Okay.

So my research \-- the research objectives in my dissertation, first I
want to \-- I want to try and understand this association between
smoking and TB. Second, I want to try to better understand the
mechanisms, the specific mechanisms underlying this association between
tobacco and TB, because these matter. They affect \-- they affect the
types of policy that \-- that we can dream up, and how successful it
might be. I'm going to talk very quickly about those first two, just
because I think that they're necessary for you to understand the
analysis that's built upon them, but also so you guys can get a sense of
what \-- what needs to come before the policy analysis. But what I want
to focus on is the third and fourth objectives. I wanted to estimate the
impact of smoking on population level tuberculosis outcomes. I want to
translate what is known about smoking as an individual risk factor to
the population level, and then set \-- and then lastly, in this \-- in
the initial stages of this policy analysis, I wanted to try and get a
sense of what \-- what impact we might expect tobacco control policies
to have on population level TB outcomes. Okay.

So, I began by digging into the epidemiological evidence, and I've
talked a bit about this. I talked about how there is this association,
that's been documented, between smoking and being infected with TB,
developing TB disease, and of dying of TB, and these are unconditional.
But also there are \-- there is an association between smoking and
developing more infectious forms of tuberculosis, defaulting from TB
treatments, so smokers are at a higher risk of not completing treatment
and remaining infectious. And also, there seems to be something
different, and we need more evidence here, but there seems to be
something a little bit different about how smokers respond to treatment.
So, given two months of treatment, smokers \-- smokers are less likely
to experience smear conversion. And what that means is essentially that
they seem to be \-- they seem to be infectious after \-- after two
months of treatment.

So, what I did after gathering all of this information and reviewing it
was I conducted a meta-analysis, and this is really just a statistical
technique to try and pool, or get a better sense of what these relative
risk's values are based on \-- based on the wider available body of
research, and this formed the foundation of part of this modeling work.
So just to give you guys a sense of the numbers, smokers \-- ever
smokers, and I define \-- so what I'm looking at here is focusing on the
difference between ever smokers and never smokers for two reasons. One,
this is how most of the epidemiological studies differentiated the risk
group. But second, in many developing countries, there's very little
quitting going on. So ever smokers are one point seven times \-- an
estimated one point seven times more likely to be infected with
tuberculosis. Two point seven times more likely to develop TB disease,
and two point four times more likely than never smokers to \-- to die of
tuberculosis. And there are some ac \-- some other numbers that start to
get at some of \-- some of the specific mechanisms in terms of increased
transmission and whatnot. But, what \-- okay, so.

What I did next was try to get a really good sense of how we understand
tuberculosis to spread through a population and to develop \-- to
develop thereafter. And Carl gave some examples of these types of
models, these are many names: compartmental models, stock and flow
models. But the idea is, basically, that you want to split the
population into the number of \-- the number of mutually exclusive
states that you care about. So for tuberculosis, there are those people
who are uninfected, people who are lately infected \-- and what that
means is people who have infection but are not infectious so they've
been infected but are not infectious and they are asymptomatic \-- and
people with tuberculosis disease. Okay.

So you start to think about the rates, the flows between these states.
What happens \-- what happens to someone who is uninfected from what can
happen to someone who's uninfected from one period of time to the next.
And the flows are that \-- so someone can either remain uninfected or
they can become infected and develop disease. So shortly after infection
\-- this is \-- in tuberculosis, this is known as primary progression to
disease, and this can happen anywhere from zero to five years after
being infected, most commonly between two and three years after. Or,
someone can \-- a person can \-- their body can control \-- their immune
system can control this infection. They essentially wall in the
infection, but they are never able to clear it so they enter this \--
this latent state where, at some point later in their life, something
can throw things off balance and a person can progress to disease.

So if you're infected, you may either be able \-- you may develop
disease or you may be able to wall in the infection, control it and move
into this latent state, or you can die from causes other than
tuberculosis. If you have latent infection, then in one period \-- in a
year, in a given year, you have a small probability of progressing to
disease. Either through endogenous reactivation, which means your body
loses control of that infection, or exogenous reinfection \-- you, you
encounter someone else with infectious tuberculosis and are reinfected
and progress to disease through this primary progression. And \-- let's
see \-- okay, so you have a much smaller late \-- rate \-- or
probability of progressing to disease from this latent state, but it
never goes away. So what is believed in tuberculosis is that once
infected, always infected. Okay.

And if you have tuberculosis disease, then the things that can happen to
you are that you can remain \-- remain with disease, or you can move
back to this latent state. This is how I said before that if you recover
naturally, so your body can sort of \-- can gain control of the
infection and you move back into this state where you have a small
chance of progressing to disease later in life, or you can be
successfully treated. And again, successful treatment does not confer
immunity. Or you can die from your tuberculosis or things other than
tuberculosis. Okay. So \-- okay. So, using the model that I just
presented to you as a framework for thinking about how smoking,
specifically, how smoking might impact TB dynamics, I dug into the
literature again, and I read about how smoking alters immune response,
how smoking alters lung function, thought about the social aspects of
smoking behavior. All the ways \-- all of the way in which smoking may
alter TB dynamics, using that model as a framework. And so, I want to
talk to you \-- whoop \-- talk you through \-- I'll talk you through all
of these individual level, sort of, impacts.

Okay, so the first is that smoking alters risk of infection. Okay.
Smokers \-- smoking alters lung function, making it more likely that if
someone is exposed to infection, that they will actually become
infected. Okay. Smoking also \-- so we've talked about this before \--
smoking is also social. So it doesn't matter \-- it doesn't matter how
this happens \-- so, we've talked about mixing patterns. Carl and others
have talked about mixing and how so often this assumption about random
mixing patterns happens, that anyone can contact anyone else. Well, what
I thought was that \-- that with smoking, it's not necessarily \-- it's
not necessarily the case and so \-- and things that aren't random about
contact patterns, right, really matter here.

So what I wanted to \-- to capture is the fact that if \-- so smokers
\-- so an individual may be more likely to hang out with other people
like themselves for several reasons; first of all, because \-- because
they frequent the same locations, they go to the same bars. Oh, sorry.
Let me start this part over again. The social \-- okay, so the mixing
being social. The idea is basically that an individual's own smoking
behavior may be a function of the smoking behavior of those around them,
so the fact that my parents smoke may influence my decision to smoke, or
it might be that the people around \-- the people around \-- ah. I'm so
sorry, I'm making a mess of this, but the idea \-- the basic idea here
is that \-- that the social \-- that the social aspects matter in terms
of tuberculosis, and so people who smoke are more likely to hang out
with other who smoke, so \-- so that sort \-- that sort of of makes
bigger this issue that smokers are more susceptible to infection.

Also, smokers are more likely to transmit their infection and this is
for two reasons. First of all, I mentioned before that smokers are more
likely to develop more infectious forms of tuberculosis, so smear
positive tuberculosis, cavitary tuberculosis, this is more severe forms
of tuberculosis that \-- that when \-- that when they become diseased
and infectious, that they'll spread more bacilli into the air. But also,
smokers are more likely to cough, and coughing is one surefire way to
get the bacteria out in the air.

Okay, so thinking about how smoking might affect risk of infection.
Well, okay, smoking has all sorts of impacts on immune response, making
it more likely that if someone is infected, they'll progress to disease.
Smoking \-- and for the same reason, smoking also makes it less likely
that an individual who has tuberculosis disease will recover naturally
\-- on their own, without treatment. But it also might affect
treatment-seeking behavior in ways that we don't fully understand.
Smokers \-- smoking \-- smokers are more likely to cough, as I said
before, and coughing is a major \-- is one of the most important
symptoms of tuberculosis, so it's possible that their smoking may mask
the symptom, delaying diagnosis. But also smokers are less likely to
comply with treatment, meaning that they'll remain infectious longer. So
these effects will \-- these impacts will affect this rate of flow from
\-- from the tuberculosis state into the latently infected state.

Okay, smoking also affects the TB death rate. Smoking kills, for reasons
other than tuberculosis. This is widely known and established, and in a
way this is good for tuberculosis. In a morbid way, this is good for
tuberculosis because if someone is infected, latently infected, they may
live less long, making it less likely that they'll progress to disease
later in their life. And you want to capture that effect. Okay, and
lastly, smoking may affect the TB death rate and \-- and the direction
is unclear just because we really don't have enough evidence about this.

So, what I did, was I tried to \-- just to summarize, I tried to come up
with a list \-- a comprehensive list of all of the ways in which I think
that smoking can specifically affect TB dynamics, and I came up with a
very large set of hypotheses about all of the different combinations.
And we have some information on some of these, and we were able \-- on
some of these specific impacts and so I was able to create smaller
bounds around them, and had less information about others, in which case
they got larger bounds. And what I did was, in my TB transmission \-- so
I came up with a set of \-- series of hypotheses, and then I reproduced
each hypothesis in my TB transmission model to try and see whether that
specific hypothesis could reproduce the relative risk values that were
estimated from the meta-analysis in the literature. Is it possible that
this specific hypothesis about the mechanisms can explain the relative
risk values that we're finding in the world. Oh my gosh, I'm sorry. I'm
really running out of time, so let me go quickly through. Okay.

So, this is one use of the model, which is \-- which is to try and \--
try and integrate various sources of information, to try and learn
lessons about risk factors. Okay, so let me talk about the impact
assessment very quickly. Traditionally, in epidemiology, if we're
interested in \-- in estimating the population attributable risk, the
percentage of a disease that would not happen if it weren't for \-- if
not for a risk factor, if a risk factor did not exist, this computation
takes into account the prevalence of that risk factor and the relative
risk values, that's all. Only the direct effects of \-- so in this case,
smoking on tuberculosis; the fact that if I smoke I'm more likely to
progress to disease. Okay, but in this model I'm able to capture \--
okay, and if you \-- if you estimate this population attributable risk
this way for smoking, given a two point seven five \-- a relative risk
of developing TB of two point seven five and a relative risk of dying of
TB of two point four, you would conclude that 34 percent of TB disease
and 30 percent, 29 percent of TB deaths, are attributable to smoking.

But in the model, you're actually capturing these indirect affects of
smoking, the effects of contagion, the fact that smoking affects TB
dynamics in ways that increase overall transmission. Smokers are \--
they develop more infectious forms of TB. You're capturing these
indirect affects. And what I found was that \-- that actually, 60
percent of TB disease \-- incident TB disease and 57 percent of TB
deaths don't happen if it weren't for smoking. So if I run the model
with smoking, and I should say that it's a 29 percent smoking prevalence
is used, which is the average in developing countries. If I run the
model with 29 percent smoking prevalence, and then I run the model with
no smoking, that \-- there are 60 percent more new cases of TB disease
and 57 percent more cases of TB death. And what I found absolutely
fascinating is, if you restrict this analysis and you focus separately
on people who smoke and people who don't smoke, that these indirect
effects of smoking are responsible for a substantial portion of TB
disease and deaths among the people who have never smoked, so \-- so you
know, we're all familiar with this \-- with the effects of second-hand
smoke, and the idea here is if I smoke and you're exposed to my smoke,
that affects your health. But what I'm talking about here is, if I
smoke, there's more TB in the population, and TB \-- and TB is
transmitted differently, and that affects \-- it affects your health.

Okay, policy implications, which I'm just going to show you some
pictures very, very quickly. I took a case study for India because we
had a lot of data to work with, and \-- let's see what I want to cover
here. So we know a lot about smoking prevalence in India. We know a lot
about this association between smoking and TB in India, so we \-- so we
are \-- feel very confident that these relative risk values are true in
India. And just to give you a quick background, smoking prevalence in
India is much lower. People don't start smoking until older, so between
20 and 25 years of age, and 39 percent of men currently smoke, and only
three percent of women. So all together, the smoking prevalence among
individuals over age 15 is only 16 percent. So this is lower than the
global average. So the pictures I'm going to show you, keep in mind that
smoking is a less important risk factor in this population. Okay.

So, the questions that I really wanted to focus on here are what will
happen if tobacco control is not \-- if tobacco is not paid attention to
in India, and second, what will happen to population level TB outcomes
if tobacco control is paid attention to? So, in India they've set up
some targets \-- some targets for \-- for what is possible, in terms of
controlling tuberculosis. And so I sort of \-- I programmed this into my
model. Okay, so very quickly, this is the TB incidence rate that my
model projects in India. If TB treatment rates remain at their current
level, and if smoking prevalence remains at its current level, this is
what can be expected. Okay. The next graph shows you what happens if
smoking initiation rates increase. So for instance, if women start to
take up smoking \-- and the takeaway message here is that this is bad.
It gets us on a completely different trajectory in which in the long
run, TB \-- the TB incidence rate is increasing rather than decreasing.
So we're sort of losing a lot of the good that we'd done through
previous dots expansion. Okay.

And then I wanted to try and get a sense of what feasible TB control
policies might do to long-term TB outcomes. And so in India, what I
found was that if we can reduce smoking prevalence by 15 percent between
now and 2020, then in the long run \-- which we care about for TB
elimination \-- we can pull down estimated TB incidence rates by 13
percent. If we reduce smoking prevalence by 20 percent, we pull \--
sorry, by 25 percent, we can pull TB \-- estimated TB incidence down by
20 percent. If we just continue to enhance dots programs as we have done
in the past, this is a small increase in dots expansion, the projected
outcome \-- I'm going very quickly through this, and I'm very sorry, but
the bottom \-- the point here is that we can begin to compare policies
that involve smoking as a modifiable risk factor to traditional TB
control policies to get a sense of what the potential impact is, and I
see this modeling work as a way \-- as a way of helping to generate
ideas about where we can intervene; what the potential of interventions
might be around \-- involving smoking.

So, thinking about controlling smoking, first of all, but also treating
smokers as a high risk group in contact investigations and whatnot. We
can get a sense of how much of an impact these types of policies might
have on long-term TB outcomes. And then I think that what is really
important to do is to go in \-- to go back with this information and try
and get a better sense of what the cost is, to instill \-- to take on
these policies and try and compare things. So the model I see as a way
to sort of bounce ideas \-- policy ideas \-- off and really try and
figure out the best way to move ahead. So I'm sorry this is rushed in
the end, but I hope that you've gotten some sense of this project.
And...

\[applause\]

Male Speaker:

Thank you, Kristin. Our next speaker is Mercedes Pascual, and she
received her degree from \-- joint degree from MIT and Woods Hole, and I
think is doing some of the most exciting work that can be found, which
couples complex systems and important public health problems. And I'm
sure you'll agree after you hear her.

Mercedes Pascual:

Let me see if I know how to operate this.

\[low audio\]

Mercedes Pascual:

And this works?

Male Speaker:

Yeah.

Mercedes Pascual:

What is this?

Male Speaker:

Just do this.

\[low audio\]

Mercedes Pascual:

Salud. Okay. So, I think I have all the controls here. I'd like to start
by introducing my coauthors. They are here in the room. I'm \-- they
could give this talk, so Katya Cole \[spelled phonetically\], who was a
graduate student of mine. She's now a post-doc and she's moving to Duke
with a position pretty soon. Sarah Colby \[spelled phonetically\] and
Luis Fernando Chavez, two current graduate students. Both Katya and
Sarah have been very active in the complex system \-- complex systems
group here at the university.

Anyhow, I was given this title, Applications to Ecology, which I
interpreted freely to mean the population dynamics of infectious
diseases with an emphasis on the environment. And I thought I would
begin by showing you some of the patterns \-- the microscopic patterns
\-- that we would like to understand, and give you a flavor for the
questions. The first time series is for cholera in Bangladesh, where the
disease is endemic. This is a surveillance program in a rural area south
of Dakar. You see here a number of cholera cases for different strains.
Since 1966, over time monthly, and you have seasonal epidemics, but
clearly the size of outbreaks vary from year to year, and what we would
like to understand is this inter-annual variation, and can we predict
it? Can we, for example, develop early warning systems? What is the role
of climate variability?

The role of climate also arises in the context of climate change. In
some very contested discussions about what is driving the increase in
malaria \-- the size of malaria epidemics \-- in transition regions in
highlands and in desert fringes. This is from an African highland
showing the increase in the past few decades. And here, of course, we
have to understand the fast dynamics of outbreaks in the context of
long-term change. Long-term change also arises as a result of
evolutionary change of the pathogens, and that is illustrated by
inter-pandemic flu, where also we see variation in the size of epidemics
and we can ask, "Well, what is a way to understand this with
\[unintelligible\]? What are the limits to prediction?" So, I had to
have a slide that has my preferred list of properties of complex
systems. We have so many of these, so I'm not going to take a lot of
time. I just wanted to mention, of course, nonlinear interactions. We
heard about nonlinear \-- non-equilibrium behavior.

I added human variables because one of the problems is that we often
have to understand interactions that involve variables that we don't
measure, and sometimes we don't even know. So we don't know the
dimensionality of these systems. We heard a lot this morning about
distributed interactions and the large number of components in networks,
also, the processes of different organizational scales and adaptive
behavior. And what I thought I would do today is to show you very little
pieces of some case studies for cholera, which will emphasize the part
on nonlinear interactions and hidden variables. I will also very quickly
\-- there is no time in the time I have, so I will say two words about
malaria and then move to some recent work, primarily by Katya and Sarah,
on inter-pandemic flu that emphasizes these two last points and is more
truly an example of a complex adaptive system in the way we have been
discussing today. So, I have here my preferred quote from cholera. Some
of you have seen it already, but it comes from this famous book on
cholera.

What I like about it is this idea that you could sort of deal with the
epidemics by using cannons, and this had to do with the superstition
that gunpowder purified the atmosphere, so there was already an idea
that perhaps the atmos \-- something about the atmosphere mattered to
cholera. So that was, to me, a bit reminiscent of this hypothesis about
the role of the El Nino southern oscillation, which was formulated after
cholera returned to South America through Peru, in '91, '92, after being
absent from South America for over 100 years. This happened to be an El
Nino year, and this observation prompted Caldwell, Epstein, and others,
to hypothesize that El Nino matters for cholera. But this was a single
event, so you couldn't really test this hypothesis.

We therefore went to the place in the world where we have the longest
records, and this is the delta of the Ganges and the Brahmaputra in
Bangladesh and India, where we have many historical records. And
microbiologists have shown, repeatedly, that vibrio cholera survives
outside the human host in some of these aquatic environments. And this,
of course, is a disease \-- it's a water-born disease in an environment
where people interact continuously with water. This is a fecal-oral
transmitted disease we have here, some latrines, this is a fisherman,
and so on; it gets worse in cities such as Dhaka, where at least two
thirds of the population do not have proper access to clean water. So,
the hypothesis goes, if you have some environmental factor that drives
the population dynamics of the pathogen, the bacterium in the
environment, we have higher outbreaks. Why was this controversial?

Well, because people that deal with disease models very well know, and
we have seen an example \-- very clear examples of these yesterday \--
that diseases are natural oscillators. We saw the example of the 1918
flu, with two peaks, and the basic mechanism that drives these cycles is
the depletion of susceptibles, the epidemic turns around, it runs out of
susceptibles to infect, and it has to wait until we replenish the class
of susceptibles through birth or recruitment. So, here we have a natural
oscillator. I had to have a picture of the first such patented clock
that functioned with a force pendulum to remind myself that it is more
complicated than that, because in fact, most of these diseases have
seasonal transmission. So, in fact, we already have at least two cycles
that interact, the natural cycle of the disease, and seasonal
transmission, which anyone with a computer these days can show, will
generate an array of behavior depending on parameters. All the way from
simple annual cycles that repeat this seasonal forcing, to cycles of
longer period where larger outbreaks repeat, for example, every three to
four years, and chaos.

So what is the problem with this? Well, then if we see some data where
we have some interannual variability with characteristic periodicity, we
do not mean necessarily to invoke some forcing, something like El Nino,
to explain these patterns. And the problem is: how do we separate from
data these two alter \-- presumably alternative explanations? So what we
tried to do in our work was to start with data for infected cases and so
on, and see if we could separate, disentangle the effects of forcing
from those that have to do with intrinsic cycles of the disease. And we
developed different approaches to do this. The trick is to do this also
in the presence \-- well, in the absence of data. That is, when we have
these \[unintelligible\] variables, it would be very nice if we had
measurements on susceptibles, recovered and immune individuals. In the
case of cholera, we couldn't even come up with a good idea of how long
immunity lasts.

So, the trick is to work at this interface of some of these models and
some of the statistical techniques to deal with these data, to \-- to
infer what may be going on. So, we developed different approaches, and I
wanted to show you the result with one of them. I will not go over this
particular approach, which relies on a model like the one you saw before
for tuberculosis and so on. And, essentially, what you do is you have
your observed patterns for cases. This allows you to reconstruct the
variation in time for susceptibles, how immunity decays over time \-- of
course these two things are very highly related \-- and then, third, how
transmission rate varies over time. A different temporal scales, either
seasonally longer-term variation, and then also what the model does not
explain, that must come from some outside forcing.

So, over here, this variation in transmission rate can then be examined
for vari \-- for associations with climate, for example, or any sort of
possible factor of interest that is your exogenous factors. And we have
this simple representation of endogenous dynamics. We applied this to
cholera, of course with data \-- it always gets more complicated than
you initially wanted, so we had to deal with the different \-- in this
case these different strains are different biotypes. We essentially were
interested in the \-- the dominant one at the moment, but we had to use
both immunity and cross-immunity to another biotype. I will not, here,
describe these particular results, which are in a paper outside. I will
just mention that, in fact, we found evidence for an effect of El Nino,
even when you take into account these intrinsic dynamics, with, sort of,
delayed response of nine to twelve months. We found evidence for an
effect of rainfall and flooding, but more importantly we found that
there is an interaction between disease dynamics and climate forcing so
that, in fact \-- this is an interaction between immunity in the
population and climate forcing.

And I thought, "What I'll do today is I'll illustrate this point with,
sort of working progress on predictability and forecasting, since there
has been a lot of argument on whether we should consider this issue of
prediction." I will just use it here for illustrating the interaction
and make a few comments about prediction. So what I did was to take the
model at any given time, simulate it forward, and then with a, sort of,
prediction time of one year, in this particular case, and compare it
with data in the past \-- the observed data to assess predictability,
and also consider data that we had not used to feed the model. This is a
true test, it's about predicting the future, after all. So that will be
a bit harder.

So what happened? Well, this is a slide that shows you something about
interaction. In the bottom panel you see a measure \-- this is an El
Nino index where you have sea surface temperature anomaly in a region of
the pacific. When this goes positive you have an El Nino event, marked
here with the arrows. And you see cases now starting here in 1985 in
black, for El Tor. And in blue you have the prediction twelve months
ahead of the model without ENSOL. You see that it misses \-- it misses
some of the main peaks. Clearly it misses. And then, the red is the
model that then incorporates both disease dynamics and ENSOL. And the
point I wanted to make in this particular example is that two events of
similar size, in climate, have very different responses in terms of the
number of cases.

Interestingly, in '86, '87, the disease was in a refractory state to
climate because we had had previous outbreaks that had built immunity in
the population, including epidemics of classica. So that, here, there
are \-- you don't have enough susceptibles for a response, while when
immunity goes away, for enough people, we get this larger response. The
model is still obviously not perfect. Let me show you, how well do we
without the feed data. This is just showing, if you can see over here. I
don't \-- I'm not sure you can see very well, but there are two bla \--
the red is, again, with ENSOL. And without ENSOL, we haven't had big
events during that time, so the difference between the two is not very
important. What matters from a practical point of view is that we would
have predicted ahead the lack of an extreme event defined here as above
this percent \-- 90th percentile.

So, at least, we have to put this now, obviously in a stochastic
framework to put some uncertainty around these predictions, but I wanted
to make the point that even for these mean predictions we have to \-- we
can do relatively well, but we have to consider \-- we cannot do it
basically using climate. And we cannot do it just with an
epidemiological model. So there is this interplay that is highly
dynamic, and depends on the history of the population. So that is one
example \-- let me see. And I have this picture, here, to just make a
comment about the discussions yesterday about prediction. So we are
looking at the phenomenon of warming primarily in the pacific, although
everybody knows these days that it is that \-- and so is a driver of
global climate viability, and we are looking at cholera over here.

So, a number of times when we present this work people say, "Well, you
should be using \-- you should be finding out more about mechanisms,
regional mechanisms, that sort of mediate this interaction." And I agree
that it would be good to know; we are doing part of that, but the
question is: would it be better for prediction? My guess, my conjecture,
is that the answer is probably not, that you have \-- I showed you after
a lot of these talks about these very complicated models \-- may seem
like a fairly simple system, but it may be just because of the level at
which we are \-- the spatial scale and the level at which we are
describing it. The different events of El Nino act through very
different pathways, and the only reason we find some regularity is that
sometimes that what is regular is that some pathways always get
activated.

And if we wanted to model these or predict these at a sort of more
mechanistic level we may, in fact, lose our ability to predict. That's a
conjecture, but the trick is that I think part of the goal of this
modeling exercise should be defining the limits to prediction and seeing
how \-- whether we can identify variables that activate some of these
very complicated pathways that we saw yesterday in some of these public
health responses. So, I thought I would tell you about malaria, but in
the interest of time, I will just make a very brief comment about it.
This is another example of using dynamical models to assess response \--
potential response to change, and change in the environment. So what we
used here is a mosquito population; modeled the mosquito vector to ask
whether half a degree in temperature was, in fact, biologically
significant.

We can argue about statistical significance in these records; there is
evidence from the statistical point of view that in these highlands
there has been an increase in temperature, and the question is: does it
matter for the disease? The dynamical response to that is, I think,
quite interesting, because what we found was that the dynamics of the
mosquito vector could greatly amplify the difference in temperature that
you observe just in this environmental variable. And I will not go into
this \-- into details about that point; there is a paper outside. We did
it just with the abundance of the vector; I think the more interesting
question is about infection and levels of the disease, but that's a more
difficult question.

The last comment on malaria \-- we talked about very multi-factorial
problems; in fact, claiming that the changes we see in these transition
regions in highlands and desert fringes are purely due to climate is
simplistic. But so are these arguments that get stuck into: is it
drug-resistance versus is it climate change? Because perhaps we should
be asking how these drivers interact, and an interesting question that
models can help us address is, for example, whether under increased
transmission we should see the fast evolution of resistance. This is
something that can be done with theory and models and perhaps would be
an interesting exercise versus arguing whether it is one or the other.

So, I will now move to a more detailed description of this work, which
Sarah or

Katya should be presenting, so if you have hard questions, you can ask
them; I will just try to summarize. This is another example of the
interplay of pathogen evolution and disease dynamics, here in
inter-pandemic flu, due to the ability of the virus to evade the immune
system. So the immune system produces antibodies in response to two main
molecules on the surface. I cannot pronounce them well: Hemoglutinin and
neuraminidase. I think the literature calls them H and N for a reason.
You can classify these subtitles H3, N2, and so on. An interesting
pattern, one that has led to a very interesting question, is seen here
in the phylogenetic tree of the genes that have to do with the relevant
antigens on the H molecule.

In this tree, of course, you see the trunk with the sort of evolutionary
drift of the pathogen, but you see these very short branches. So the
question people have been asking \-- that's an ecological question,
perhaps, but that has consequences for, I think, vaccine development \--
is why is at any given time the diversity of the pathogen fairly
limited? So that if you look at a given time if the branches were
longer, you would have more coexistence, and you don't have that. So
that's a question at the micro-scale in this system. The different
colors correspond to different clusters defining the -- in a very
interesting paper by Smith and colleagues, where they show that with
time, you will see that the distance between \-- the genetic distance
between the strains will grow continuously, but that the antigenic
distance that matters for production of antibodies is in fact
discontinuous and clustered, so that many sequences basically give you a
very similar phenotype.

I have ten minutes. I will do this. So, let me try. So now, you have
this observation, and does it matter at the macroscopic level for the
population? Well it does; you see these stars correspond to new clusters
arising, and that's often when you see the largest epidemics. So the
question I like to ask is, "How are we supposed to model this?" One way
to do it in the literature is to use an SIRS series of compartments. Is
that good enough? So, that's a question. Ferguson and colleagues, in a
paper in *Nature*, used an agent-based model to couple these two scales
in fairly complex models that describe the evolution of the pathogen. To
get this \-- this restricted diversity they had to postulate this sort
of \-- how was the word \-- transcending generalized immunity so that
exposure to one strain gave you protection against other strains.

That \-- this model has properties that are not consistent with this
observation of the discontinuity here in the phenotype, so Katya had
this very interesting insight, which was to use neutral networks as a
way to map genotype to phenotype, so that the key problem was how were
we mapping the variation at the genetic level into variation that
mattered at the phenotype level. Traditionally this is done in all
models by looking at sequences, calculating some sort of distance, and
saying, "Well, the degree of cross-immunity is just a function of the
distance." But that is inconsistent with a number of observations; also
you can find that in detail in the paper.

What I decided to do was to show you a figure written on a commentary of
the paper because it's a very interesting representation of a
complicated model. This idea of neutral networks which come from complex
systems; they have been used in the evolution of protein shape; they
have been used in the evolution of RNA by Fontana, Schuster, and others.
I think what is new here is to couple that with some population dynamics
of disease. The picture is as follows: if you live in this space you are
a sequence, like a genetic sequence; a particular strain. If you are in
one neutral network you have a similar phenotype, so that the immune
system sees you as almost equivalent. Almost, I can say. It's not
perfectly neutral, but almost so that the sequences are mutating, they
are just drifting and looking for \-- in a sense, exploring this space
of sequences.

But occasionally you can have one change that connects, that finds
another network. When that happens you have a very distinct,
sufficiently distinct phenotype that is recognized as a different
antigen. At that point this \-- on this new network, this virus has many
more susceptibles available to it. And so, it can out-compete the
others, due to the dynamics happening up here, and it will lead to
extinction of the, sort of, its predecessors. After that there is more
exploration, and then the cycles of exploration and innovation. You can
see that there is a period of strong selection when this new type
arises, and then periods of, sort of, what could be, in terms of
phenotype, fairly static change. So, in these models, what is very
interesting is this combination of, in a sense, neutral drift and
selection, not as opposite explanations for evolution, but as part of
the same, in a sense, the same process, so that one enables the other.
And I think that's a fascinating idea that comes from these neutral
networks.

Another fascinating idea in these models, I think, is that we talk \--
we heard a lot about emergence and bottom up effects when you look at
these multiple organizational levels. But you can see that in this view
of the word, there is emergence in the sense that what is happening down
here at the micro scale has an effect on the population dynamics; for
example, how many susceptibles are available and so on. But there is
also feedback because selective pressures on the micro scale will very
much depend on the population state. So this is back and forth and it's
not uniquely \[unintelligible\]. So, there is this emergence modifying
the future of evolution so that is the interplay of what \-- you can see
the interplay of the dynamics of evolution and the dynamics of ecology
at another level.

I'll close by saying that this model produces, and it's the only model
\-- at least, I know \-- that produces the set of observations, at least
qualitatively, that we have for flu at those two levels. The character
of the phylogenetic trees with the short branches, without invoking any
\[unintelligible\] and immunity, the sort of larger peaks as you get to
new clusters. And it produced a prediction that was not the way we were
thinking about diversity, which is that there isn't just limited
diversity, there is a pattern of \[unintelligible\] when a new cluster
emerges, then diversity afterwards increases. But then, as another one
comes and gets everybody extinct, you get a decrease in diversity. So
there is this stasis, and this punctuated selection happening. And this
is supported by observations.

And this leads me to the, sort of, closing question. While we have this
complicated model that is a complex adaptive system, I don't know if
it's an agent-based model. Katya doesn't like to call it an agent-based
model, so I will not. But the question is, "Do we need to model
interpandemic flu like this?" I mean, it's nice to couple this level,
understand the mechanisms at some level, but if we wanted to model just
the population dynamics level, fit time series, try to do predictions
about the emergence of new clusters. Should we do it at this level of
detail, following these sequences and so on? And I think that one of the
roles of these models \-- not the only role, but one \-- should be when
\-- not when these details matter, they obviously matter \-- but given
that they matter, how can we simplify the models? Are there simple
models that are consistent \-- this the end \-- that are consistent with
these complicated dynamics? So, do the details matter to the big
picture?

And I think \-- it got stuck, it like these things \-- and I think,
again, that we have these great tools to ask about simplification, but
that should give us some idea about cases where we don't know the
details. We heard a lot about networks. What about when we don't know
the networks? Can we always measure them? I would say that for many of
these applications, no. And if I try to \-- and you can say, "Oh, the
model for cholera ignores all the spatial heterogeneity." No, it didn't.
In fact, it used a trick to barometerize, implicitly, the effect of
spatial heterogeneity. So the effect of heterogeneous random mixing,
implicitly, in a model that looks like an old type of model, but it's
not saying, "We have random mixing." It's just saying, "We can represent
it implicitly for the purposes at hand." And I think this is an
important question.

So, let me just thank again my coauthors, some collaborators, and then
some sources of funding.

Male Speaker:

Thank you, Mercedes. We next move to Jim Koopman. If in epidemiology
there were an early adopter of this kind of thinking, it's definitely
Jim, who has been arguing for the importance of systems thinking in
infectious disease epidemiology for a number of years, and has certainly
led many people into thinking that way. Jim?

Jim Koopman

Thank you. I'm going to talk about complex system analysis in infectious
disease. I've dedicated myself, for some time, to developing theory that
serves public health, and specifically about systems theory that serves
public health, and especially complex systems theory. And I'm really
pleased to be working in the Center for Advancing Microbial Risk
Assessment, a highly multidisciplinary center with \-- across multiple
institutions with many different disciplines, especially environmental
and microbiology.

The broad themes that I'm going to deal with are: the analysis of
infection transmission systems history \-- and it's really under rapid
change now and moving much more to complex systems approaches and
purposes; I'm going to deal with the philosophy in causal system
analysis methods of infectious disease epidemiology that could serve
other areas of epidemiology \-- I'm afraid my talk is fairly oriented
toward epidemiologists, but I think it should be of interest to broad
complex systems scientists. And then I want to talk about something that
guides a lot of my research focus; that is, inference robustness
assessment and how that provides a cornerstone to developing theory that
serves public health.

With regard to the history \-- indeed, within epidemiology there was
very early on a systems focus, and the consequences of feedbacks,
interactions between individuals, things that most epidemiologic
analysts still today assume away. They assume away feedbacks; they
assume away interactions between individuals. The initial thrust, when
\-- before the computer age, and actually when computers were simple,
when the field was being largely developed by mathematicians \-- was to
find the essence of the dynamics through simplicity. And, of course,
when we look at system dynamics, there's always virtue in the
simplicity. But, we never focused on informing public decisions. It
clearly requires a more complex systems approach.

So, here we have epidemiologists generally focus on, you know \-- they
have \-- they follow a bunch of individuals. They characterize those
individuals by outcome variables and by exposure variables. And they do
look for risk factors in the individuals. And, for the most part, they
assume that this is the relevant plane of analysis, even if they're
gathering data from the other important planes of analysis, the network
plane that connects individuals, of course, which Mark talked about. So,
the basic parameters, a lot of the basic parameters of transmission
system analysis, lie in this interaction between individuals. And the
effects of those may be observed by looking at this relationship between
exposure and outcome in individuals. But that doesn't give you a good
idea of what's happening within the system, even though these network
relationships are determining what's going on here. Assuming the network
effects away as a traditional risk factor, epidemiology does \-- can
lead to a lot of serious error.

So what are the \-- so, systems epidemiology didn't do that. But systems
epidemiology made a lot of simple approaches. They assumed simple
homogeneous states, like the FCIR model: the mass action, above all,
that assumes instantaneous contact, and instantaneously thorough mixing
after every contact; a highly unrealistic assumption. Abstract contact
that's mediated by transmission and is not specifying modes, and CAMRA
\-- our big focus is on specifying how infection gets from one person to
another, and how that can really dramatically change how you think of
contact networks when you start taking that into account. If you go
ahead and just define contact as somebody talking to somebody else,
that's not going to get you to a very realistic infection-transmission
network.

So then look at the purposes. And I orient my purposes very much, very
strongly toward advancing the science. I want to advance the science of
infection transmission analysis. So the very first purpose in a lot of
complex systems analyses are just aimed to getting insights at \-- but
really we want to carry that on to developing theory expression, so that
we build theory and create base for others to build new theory. You
build theory for old theory for the most part. And then predicting
consequences of action or inaction, and we have to get to the point \--
and I'm really happy to say that we're really moving down in the
direction here in epidemiology of using models to design informative
studies, and using models of dynamic systems of complex adaptive systems
even, for analyzing data to estimate system parameters and predict the
actions of action-consequences. So really, only purpose one can be
pursued when we make these really radical assumptions of homogeneity and
mass-action assumptions.

There was a big move \-- when I started working with Carl was in the
time when there was a big move to relaxing all of these homogeneity
assumptions. There was \-- began to realize, people began to realize, in
the 1980s, early 1980s, that we had to take into account something more
than mass-action, and then begin to change those. And then Mark talked
about fixed network models, and he mentioned that, in fact, for a lot of
situations, those fixed-network models \-- even though they may change
the kind of assumptions that you have when you have mass-action
assumptions \-- the fixed-network models are often as unrealistic and
can be as problematic as mass-action assumptions. The idea that people
are continuously in contact and they continuously have the same risk is
just unrealistic for a lot of things.

So we have to go to individual- and agent-based models. And thankfully
\-- Mark talked about Eubank models \-- we have a big project with Chris
Barrett and Eubank, in fact, modeling the Wasatch Front in front of Salt
Lake City, at the level of 1.5 meters, 6 million people, tremendous
computing power able to do that sort of thing. But then you add all that
detail; if you don't have the fundamental elements right, if you don't
know \-- why have all that computer power for so much detail when it
doesn't advance your scientific purposes that much? Especially given
limited analyzability of such massive models.

So you have to think about simplifying assumptions about contact
processes and modes of transmission that invalidate, for the most part,
the other purposes that I talked about before; predicting the
consequences of action or inaction, designing studies, and using models
to analyze data. So a variety of new model forms that can work together
in a new statistical estimation purposes, make these purposes 3-5 more
feasible. So we work with deterministic compartmental models that make
the mass-action assumption; they assume infinite population, the
continuous models. Stochastic compartmental models \-- at least they
relax the infinite population assumption. The network models that assume
fixed ongoing linkages, what they do is they, as I said, make a lot of
other assumptions. Our big focus in CAMRA is on these environmental
transmissions system models, and above all these relax the assumption
that people don't even concentrate on very much, of instantaneous
contact.

In fact, contact can occur over time and over distance, because one
person contaminates the environment, and not every person picks up that
contamination from the environment, and then, moving onto
individual-based models that can relax a lot of assumptions, but
thinking of how to do that in an organized way. So, what we focus on is
trying to integrate these kinds of models into an analytic framework
that enables us to focus on public health purposes and focuses on the
inferences that we want to make. So, we call \-- we try to make our
focus on inference robustness assessment, ensuring that realistic
relaxation of simplifying models assumptions will not change inferences
in order to focus on things that are going to serve science, or serve
public health, advance theory; you have to think that the theory has to
have something to do with realistic situations if you're going beyond
just getting the insight into dynamics phase.

So, the way you should proceed is using the simpler, more unrealistic
models to make needed inferences, and then assess the robustness of the
inference to the specific assumptions, not only due to the form of your
model in one model type or another, but to the \-- that are intrinsic to
the type of modeling that you may be doing. So that \-- you need to be
able to handle these different kinds of models in order to make the
inferences.

So in order to make epidemiology serve public health, we say, "Choose
inferences about important actions." If you want to make epidemiology a
sort of science, choose inferences about important theory. Clarify the
causal assumptions in models used to analyze data and to make
inferences. Then theoretically explore the potential assumptions to
alter those inferences, and see how they \-- the potential of the
assumptions to alter the inferences.

And then when inferences from causal-model based data analysis is
sensitive to assumptions, define the data that you need in order to
select \-- in order to overcome that sensitivity. And of course, at the
same time, you have to develop the estimation methods that
\[unintelligible\] take place. I really think that we're entering an era
where this kind of thing is much more feasible than it used to be. Part
of it \--and I'd like to say epidemiology can move in this direction,
partly because epidemiology is thinking more causally \-- Tom, the other
day, made a comment about most epidemiology being immunized by Judea
Pearl. But I think that, in fact, there are enough advances in directed
acyclic graph theory so that people start thinking about causal analysis
in a new way, but they just don't do the system part of it. In all of
these different model forms, part of it is advances in computing, part
of it is advances in software, part of it is advances in algorithms.

All of these different model forms are advancing greatly, and fantastic
new potential for gathering different kinds of data under different
situations. So if we want to think about moving areas, or how other
areas can proceed, begin just thinking about the dynamics of processes,
of causal processes. And a lot of different areas of epidemiology are
beginning to do that, outside of infectious disease epidemiology. Then
model the causal systems with feedbacks. You know, there's a
misconception amongst many of those who are promoting what they call
"causal analysis in epidemiology" that directed acyclic graphs within
nested structural models can handle this situation's feedback of
realistic systems. That 's not the case, and you really do need a
systems analysis to get a total systems approach.

The next thing is really, really central. Epidemiologists use data
models, for the most part, but they use data models like the things that
they take off the shelf for \[unintelligible\]. They use those data
models, ultimately, to make causal conjections, to make policy
decisions, to make theory inferences, and if you're going to do that,
you have to think of all the assumptions that are affecting the
inference that you are making, that you're \-- out of a particular
causal analysis. So you have to clarify the causal assumptions of those
data analysis models. And then I \-- then, you can adopt the inference
robustness assessment, and I think nowadays we have these really great
new statistical estimation methods that make a real difference.

In standard epidemiology, we really have to start relaxing the
assumption that one subject is independent of other subjects; there's no
interactions between individuals, there are no system feedbacks, or that
the system generating the data is at equilibrium. A lot of social
epidemiology, for example, gathers variables like social support, social
stress, that have to do with interactions between individuals. You have
to realize you have to analyze that data in the network plane between
individuals and not in the plane that's just relating subjects to
outcome. So, some of the very key progress made from doing this sort of
thing is well recognized now that\-- in most infection-transmission
systems.

[]{.mark}

The indirect effects can, and as you begin \-- as you understand and
gain expertise, you see where indirect effects are largely greater than
the direct effects \-- that is, the effects directly on individuals.
But, above all \-- and we saw this in a lot of the \-- in Mark Newman's
presentation of the \-- of the TB \-- you saw it in Mark Newman's
presentation of SARS, in the talk that we just heard about smoking and
TB. Some of the biggest effects are on contagiousness. Above all, that's
what we have to focus on; contagiousness. Epidemiologists, in their
classic study designs, don't pick up that at all. We need complex
systems models and complex systems approaches that take the whole
system, can use much more complex data, in order to detect
contagiousness effects.

The other is that the strongest individual level risk factors for
infectious disease are, are \-- okay, so we have to take those across
individuals, as well as the issue of contacts between individuals,
\[unintelligible\] contacts. So here we are, you know, transmission
systems science has been down in the shadows. We'd like to get to the
bright, shining light of the strong science, but we're down in weak
science dimensions, both on the scale of how much validated,
quantitative theory we have about the \-- the system, and also, our
ability to observe repeated units. So we've got to get somehow from here
to here. Well, we're \-- we are making progress; advancing a lot of
theory. But how can we get better data? One reality that we're dealing
with when we're looking at epidemics, we're looking at one realization
of a stochastic process. So we need to find ways that we can get the
most informative information when we recognize that we're looking at one
realization of a stochastic process.

There are two tremendous new data sources in CAMRA work focusing on
environmental measurements. These \-- we're going to get volumes of data
that'd be far more informative about transmission systems than anything
we've had before. And then I'm going to talk in a minute about the
genetic relationships. So, in order to get to this \-- our validated
thing \-- we have to find \-- identify informative and repeatable
observations, as well as developing the theory. So, HIV. A long time
ago, Carl Simon and I started looking at HIV transmission. We realized
the importance \-- John \[unintelligible\] was the strong force working
with us. Looking at the role of early HIV, we showed that it's very
important in the early stages of the epidemic, and that's all \-- well,
except that there is high transmissibility during the early stage, but
for a long time, the \-- the system role of early infection was not
accepted. We had very poor measurement of system characteristics.

HIV has a high peak of transmission early on. The issue now is, when
we're more in the endemic state, what is the role of primary HIV
infection transmission? And what we're \-- what we can say is that the
system effects of the HI \-- transmission are strong and interact
strongly with systems confirmation. So we have to take a complex systems
approach to answering this question. I was thrilled, recently, in April
of this year, in a period in JID; a tremendous study from Quebec \-- in
Quebec they're getting \-- approximately 70 percent of cases are being
diagnosed early in infection. Quebec long resisted any HIV reporting,
and this may be part of the reason why they're having such success at
it. But the \-- the key thing is there was a virologist collecting all
\-- you know, all these specimens, looking at all the sequences,
aligning them by hand, looking at them by hand with a simple Excel file,
looking at all the risk factors, thinking about everything that goes on.

So, you know, the \-- about 4,000 cases that she had sequences on \--
she knew each one of them individually, in terms of getting the data and
put out the pattern of sequences. So we built a simple little model \--
this is all work in the last couple weeks \-- of higher risk population
and lower risk population. One of the big realities of risk behavior
over time is that people change their risk behavior. I really realized
this when I did HIV counseling and testing. Everybody has some period of
craziness. Nobody has a really continually sustained period of craziness
in their life. Not nobody; some people do, but you know. And so we just
built this simple model; a simple individual-based model for infection
transmission. Jung Hun Kim \[spelled phonetically\], my doctoral
student, did the construction.

And then we \-- we divided the high-risk areas into four different
levels of different risk. So, at the first level, it's like we have one
big homogeneous population. At the second level there is \-- you know,
we're increasing the new partnership formation rate as we go down the
different levels, so that we're increasing the differences between the
high-risk stage and the low-risk stage. And then we take the Waver
transmission probabilities. Now if \-- the Waver transmission
probabilities are about \-- in this relationship \-- a little less than
one per \-- one percent in the first six months. But a lot of that, by
the way, is concentrated in a very \-- narrower period of about three
weeks, and during that three weeks there are times \-- there's probably
times that you're close to a hundred percent transmission, so that when
you spread it out over three months it goes down.

So \-- so, what have we done here? We're getting \-- we're setting these
different parameters, so that at the different settings of these four
different cases here, we're getting 15 percent prevalence and 40 percent
primary HIV infection transmissions. Then we look at patterns of
clusters, we look at the genetic trees, and we look \-- how many \-- how
extensive the genetic trees \-- or the transmission trees, where
transmission is from one person with primary infection to another person
with primary infection. And you can see that even though all of these
have 15 percent and 40 percent, the pattern of clusters in our
simulations differs quite a bit. Now, we collected data for this over
\-- out of our simulations over very long periods of time.

The data that they're collecting in Montreal is really \-- so it's
collected over \-- you know, they've been doing this over a shorter
period of time. And you can see very clearly this pattern of clustering
where you have this decreasing \-- and then you have these few clusters
with fairly big numbers, starting out there. So, this is real complex
systems data. How are you going to analyze this kind of data with a
standard statistical tool? But this data could be really informative as
to where you are on these \-- you know, on the scale of this sort of
phenomena. If we have two sorts of phenomena taking \-- increasing the
\-- the role of primary infection, that is, we have increased
transmission probability from early, and we have this clustering of risk
over time \-- at the two phenomena. We see that what each of those
contributes can be really important to the control of infection.

If in fact we're at this stage, if we look at the fraction of infection
that we have to eliminate, the \-- that people \-- infected people to
make them noncontagious. So these are the different fractions that we
make noncontagious, that \-- we have a much easier time eradicating
infection. If we get up to this top level, we've eradicated the
infection. Depending on how much of the role of HI \-- of the early
infection is being affected by that behavior change component, and it
becomes increasingly difficult as we move up that scale.

And it sort of \-- summarizing that, the level of transmissions from
primary HIV infection is raised by two things, and we already said that
the \-- these two different sources are affected by different patterns
of genetic clustering. The more that behavior swings raise the fraction
of transmissions during primary HIV infection, the harder it is to
control the infection, the harder it is to control transmission. And we
look at the Brenner data and we're \-- we're just at a preliminary
stage, we'll have to do this \-- you know, better analysis, but it
really looks like primary HIV infection transmission is much more
important than just reflected by its transmission probability.

So, overall, summary \-- advances in diverse infection transmission
systems models facilitate adopting an inference robustness assessment
framework. We'd like to get \-- move epidemiology in the direction of
\-- of adopting that. We think that if epidemiology moves to an
inference robustness assessment framework, it has to move to a complex
systems approach. There's no alternative when you want to relax
simplifying assumptions. And the complex systems mo \-- models allow us
to use data in ways that standard analysis doesn't, such as the
clustering analysis that I illustrated. Thanks.

\[applause\]

Male Speaker:

Thank you, Jim. We're going to have a short break now. We'll have a
15-minute break and reconvene at three o'clock for two more \-- two more
presentations, and then the final discussion. See you then.

\[break in audio\]

Male Speaker:

We'll come in quickly, so as not to miss what I think will be an
extremely interesting talk. Our next speaker is Ary Goldberger, and he's
the Director of the Rey Laboratory, and Associate Professor of Medicine
at the Harvard Medical School, and Program Director of the Research
Resource for Complex Physiological Signals. Another early adopter, Ary
has been doing work on complex analyses of physiological data for \--
for many years, has made many contributions, and if you go to his Web
site, you also see that the characters \-- the characters on the screen
are \-- represent, I guess, who the center, the Rey Center, was named
after. So if you remember Curious George and the Man in the Yellow Hat,
you will see all those characters represented on his Web site, which is
not important for today's topic, but \-- but fun anyway \[laughs\]. Ary.

Ary Goldberger:

Thanks so much. It's a \-- it's a great pleasure to be here. Let's see
if I \-- so what do \-- do I have to do something? So for those of you
who \-- actually, the \-- our laboratory \--

Male Speaker:

You know what \-- excuse me \--

Ary Goldberger:

The Rey Lab does have to do, in a metaphorical sense, with \-- with
getting into places that we don't belong as \-- because I came out of
cardiology, and therefore am eminently unqualified to talk about the
title of this talk, which has to do with neuroscience and physiology in
a sense, but we \--

Male Speaker:

Which \-- which presentation is it?

Ary Goldberger:

So \-- it says here, University of Michigan.

Male Speaker:

Oh, that one? Oh, it's there? Okay, sorry. Oops. Got it.

Ary Goldberger:

So we just get a slide show up here.

So \-- so the invitation was to talk about two areas where I have no
guild membership; physiology and neuroscience, but this is very much
connected, through the principles of universality and non-linear
dynamics to cardiology, in a way that I hope I can demonstrate to you.
So, what I'd like to \-- this will have sort of a musical format here,
in a sense, that there will be a musician whose \-- who will appear
briefly, but there'll be an overture and brief coda.

So, the themes that sort of guide our work are threefold. One is that
physiologic signals and structures \-- so the topology of physiology is
anatomy \-- are the most complex in nature. So if you're a physicist and
you think you've got the most complex signals, come see us. In fact, the
people in our group are primarily physicists and engineers. That
important basic clinical and toxicologic information is actually
encoded, or hidden, in the way things change; physiologic things which
are signals change with time. And that \-- in these non-linear
fluctuations there are mechanisms. And that from a practical, from a
clinical point of view, this is a \-- the topic here is public health,
that there's a degradation, there's a loss of complexity in both time
and space that's indicative of pathology, of aging, and of toxicity of
various agents, so that we can actually use non-linear dynamics in a
practical way, in a bedside way.

So, the \-- the complex variability, actually in a sense, is the signal,
and this variability represents the signaling mechanisms. So we don't
usually think about that in traditional physiology, and I will make a
distinction here that not all variability is complex. So there's a
certain type of \-- of set of ingredients that make the type of
variability that one sees under healthy conditions extremely complex,
and there's a degradation of that in various pathologic contexts.

So here's a quiz for you. Can we do a quiz here? Is it okay? Okay. So
\-- there was a quiz yesterday so I guess we can have \-- so here's \--
here's \-- this is a friendly quiz, it's \-- I think it's ungraded. And
so the question is, how good a physiologist, how good a dynamacist are
you, and can you tell? So, the proof of that non-linear pudding is, here
are \-- here are what are called time series. They're instantaneous
records of heart rate, on the vertical axis, versus time, on the \-- on
the X axis, a half hour of time, and four different subjects recorded,
say, with someone wearing a cardiac monitor, a Holter monitor, where you
can pick off the interbeat interval, and the heart rate is the inverse
of that, in a very accurate way. So you can measure the interspike
interval, convert that to heart rate, and then you have four different
recordings here from these four subjects, who are pretty much at rest,
and \-- and so their activity level is not different, and it was pretty
much basal.

So, but here \-- here's the \-- so here's the deal. You pick out the one
you think is the healthiest, and only one here represents sort of
optimal health, in a sense, what we would all aspire to from a \-- the
point of view of cardiac control, which is much more than that. And if
you get it wrong, the problem is that the other three subjects are at
extremely high risk of sudden death or of stroke. So to make this a more
\-- most tests are just tests, you know, but to make this really
compelling, imagine that the time series that you pick becomes your
physiology instantaneously.

\[laughter\]

So, you'll either have a really good day and a really good night, or
something untoward will happen. So, you want to think about it. This is
\-- this is sort of the test of your life.

So \-- so that should be, you know, health versus disease, it should be
pretty obvious, right? So how many want pattern A? What are all the \--
pattern A \-- where are all the steady-state equilibrium people here?
Okay.

\[laughter\]

So, how about pattern C? Anyone want pattern C? I got one over here, one
over there. So, C is sort of oscillatory. And how about \-- we'll go
around in clockwise \-- how about D? So we've got a bunch of people \--
and how many want B? So it's look like it's \-- D and B are probably the
most \-- most popular. A few votes for C and I don't think \-- does
anyone want A?

\[laughter\]

Yeah, aren't there any people who are into homeostasis here?

So what are the answers? Let \-- I'll tell you what. Since a lot of you
got this deadly wrong, or dead wrong \--

\[laughter\]

I want to \-- I want to help you out. So here \-- let's redo this, all
right? So \-- answer \-- here's the \-- forget about \-- forget about
dynamics because that's not working. Pick out \-- imagine that you were
mapping these four time series onto music \-- a musical \-- some type of
\-- you had a mapping so you could translate the variability or the lack
thereof into some output that was notes. Which one would make the most
interesting music? How many would pick A? You don't want to hear one
note played over and over again? How many would be C? You \-- you're
into, like, scales and stuff?

\[laughter\]

That's a \-- that's a whole different psychopathology. How many would
pick \-- would pick D? D \-- D is some \-- someone \-- someone just
playing random notes. And how many would like to listen to B? So you're
doing better. All right. So, A is heart failure.

\[laughter\]

C is heart failure.

\[laughter\]

So, are there any clinicians in the au \-- in the audience, people who
see patients? Yeah, so \-- so what occurs in heart failure that repeats
itself about every minute, you can see the bed side, and it has to do
with their breathing, and they're two British names separated by a
hyphen, and they're \[laughs\] called \-- it's called Cheyne-Stokes
breathing, yeah, very good. And how about \[laughs\] \-- and, so \-- so
the choice here is, do you think healthy dynamics are extremely
patterned and periodic, so we've rejected A and C, which one has \-- is
sort of a flatline, the other is varia \-- is variable, but in a very
periodic fashion.

But then the question is, do you want to be \-- which type of
variability do you want, because one \-- D versus B \-- one is perfectly
healthy, you know, the fountain of youth. The other is actually a very
serious cardiac condition, so not all variability is healthy \-- that's
\-- there's sort of a misconception in dynamics that variability and \--
is the same as complexity. No way.

So which \-- what is D here? Well, D, that's like random notes, that's
atrial fibrillation, very close but not identical to white noise, no
correlations. And B, if we could bottle, if we could package B, we'd
have the answer. Because, if you can model what's going on in B, which
is complex variability, extraordinarily complex variability, more
complex in this healthy heartbeat than anything that \-- that people in
physics know currently had a model, we would have solved the D problem.
So that's very, very interesting and that becomes the focus of a lot of
activity that I'll just allude to. So there's something about the
pattern in B, or the complex pattern in B, that reflects healthy
dynamics, and that's \-- also has to do with something called fractals
that we'll come back to.

So, here's another question, which relates to why there's a problem with
A, which is that very flat output. The question is, is the body a
machine? And if you go back to a historic paper by Walter Cannon, who
was at my institution at Harvard \-- or I'm at his, because there's a
whole society named after him, quite appropriately. He was a giant
figure in the field of physiology. He \-- he proposed the notion of
homeostasis, which actually goes back years, decades, before, to Claude
Bernard's notion of the "milieu interieur," the constant sea of the body
as being the wisdom of the body.

And there were two components to what Cannon talked about when talks \--
talked about homeostasis, which is probably, if you \-- if you do a
MedLine search of homeostasis, there are tens and thousands of articles
there, and that's sort of a \-- that's a buzzword. People talk about
homeostatic regulation. They have at this conference. But embedded in
that are really two notions, and the key overall notion is that the body
is a type of servomechanistic machine. It's a machine that wants to keep
things both inbounds and to reduce variability. The first part is
clearly true. If you look at the \-- at the cartoon below, the idea of
homeostasis is you have some flat steady state, you perturb it, and you
come back to it. And so, the corrective notion there clearly is correct
because your body doesn't want its pH to be 7.8 or 6.9. You'd dissolve
in acid or in alkali.

But the question \-- the deeper question here, is, is the underlying
pattern \-- what the body aspires to, in a sense, really a constant
single steady state? Is it an equilibrium-type state, as Cannon noted?
Or is it something quite different? So there's a \-- there's an "or"
here, and the "or" is homeostasis revisited, which is, in fact, the type
of variability we saw in the healthy heartbeat \-- is complex
spatiotemporal variability, actually a mechanism of healthy stability.
And you have to be very careful here, because what mathematicians mean
by stability, and what is actually stability in a physiologic or eco \--
ecologic context are very, very different.

So how can that be? How can variability be a mechanism of something?
Because if that's the case, then the body isn't a machine in any sense
that an engineer would think about. It's a complex evolved system, which
is totally different from anything machine-like. And, indeed, all
notions of machine-like entities that guide anyone's thinking about
pharmacology, or about medicine, are going to lead to very dangerous end
results in many cases. If this statement is true or has any connection
with verisimilitude, than we would have to, in fact, fundamentally
rethink all notions of mechanisms in causality and physiology. So that
would be a good thing because it would \-- would invi \-- yeah, it would
be a good thing, right, because then you could fire all the people that
are doing the wrong stuff and hire a new generation of people who know
what's going on.

So \-- so what \[laughs\] \-- what are the hallmarks \-- if so, what is
\-- "What is health?" was a question posed yesterday. And so, health and
adaptability are very, very closely linked, but if you \-- if we take
\-- just take the data, if we look at the healthy heartbeat, and use
that as sort of a template for \-- for healthy dynamics, and there's a
reason we can do that, there are a number of statistical features of the
healthy heartbeat that actually are extremely important and are \-- are
not part of the everyday vocabulary of people in biostatistics, or in
\-- where I come from, in physiology, certainly not in cardiology.

And they have to do with nonstationarity, the fact that the statistics
actually change with time, the different moments \-- the mean, the
variants, and higher moments are \-- don't sit still, so there's a \--
there's a, sort of a \-- bit of a problem area about whether you're
talking about a process being stationary or the output of the process.
Here we're talking about the stationarity of the signal, which is all we
can \-- can measure. There's nonlinearity, which is a major theme of
this conference, and what that means is crosstalk for biologists. It's
the \-- the components talk to each other in nonadditive or unexpected
ways.

A third component is multiscale organization, which is to stay that
there are fluctuations in structures that go across scale, and that they
may have fractal organization, or so-called self-similarity, which we'll
come back to. And a fourth very interesting feature is \-- is called
time irreversibility, that the signal doesn't read the same forward and
backward. You can't just time reverse it and have the same statistical
properties. And that so-called arrow of time, which we'll come back to,
is actually a marker of nonequilibrium dynamics \-- that energy is being
pumped in, you get dissipation, and things can't be just replayed back
and forth. If the system is at an equilibrium, the flatline or the sine
wave, it doesn't matter, it's back and forth, doesn't make a difference.

So it's interesting though, that in our \-- in terms of the linguistic
\-- sort of the semantics of health, that three of the essential
ingredients, nonstationarity, nonlinearity, and nonequilibrium dynamics,
are introduced by a negative. That's sort of a problem because it
creates a bias that, I mean \-- you don't want to be \-- you don't want
to have, you know \-- be a non-something or other, it's like a
non-sequitur or a non-person, that's not good. So, if \-- so, do you
want to be equilibrium or nonequilibrium? You know, do you want to be
stationary or nonstation \-- the people \-- so the language actually
creates a bias against what's actually the \-- the physiology. So,
there's another bias in the field, which is \-- so it's illustrated by
\-- this is the way cardiologists represent the heart. So it's a
beautiful picture by Frank Netter, with his glorious heart filling up
the screen and a metronomically periodic, or regular heartbeat, the
electrocardiogram underneath, is beating like a clock. So that's a \--
the problem there is what? It's sort of a little bit reminiscent of the
New Yorker's view of \-- of the United States of the world \--

\[laughter\]

\-- which doesn't \-- doesn't even exist here, I guess. So it's like
mainly Manhattan and just a few blocks there of the nearest \-- and
beyond that there's something called the Midwest, which I guess doesn't
really exist except for here, and then \-- and then \--

\[laughter\]

So that's \-- where I come from, that's where people \-- so they see the
world. So there's \-- there's a certain solipsistic view of things, and
so \-- probably a more \-- a more correct view is a more systemic or
network view, which is what \-- the theme of this conference, which is
actually \-- the heart becomes slightly \-- it gets rescaled down to,
you know, its proper dimensionality, which is part of a network of \--
of interacting components, which go from the central nervous system to
the autonomic nervous system, and they all sort of compete, there's this
competitive interaction.

And so you have coupled feedback networks that operate over a wide range
of temporal and spatial scales from the molecular, submolecular, on up
to the systemic. And indeed, on up beyond just organs to the individual,
then, to the society, and they all \-- so this is an enormous amount of
complexity in terms of feedback. So it's not bottom u \-- there's also,
I think, a misconception that things are bottom up or top down. The
physiology is both simultaneously, which creates enormous problems in
modeling. So here's \-- here's another quiz, which I'm sure you'll all
get right, is whether \-- is your world linear or nonlinear? And how do
you \-- you know, if you're trying to discuss this with a colleague who
still insists on linearity, then their world view would be dominated by
the ideas that simple rules lead to simple behaviors, that things add
up, that there's proportionality of input and output, and that there's
high predictability and no surprises. So that would be, sort of, the
world of dominoes, you know, and that's the linear causal chain. So
that's one worldview.

The nonlinear view is that simple rules can, in fact, lead to
extraordinarily complex behaviors, that small changes can have huge
effects, so-called butterfly phenomenon, that there's
low-predictability, and that anomalies are the system, that's what you
expect, and that the whole is not really greater or less than the sum of
the parts, but it can be qualitatively different from. And that's one
definition of emergence, and as we heard yesterday, that people differ
in how they define emergence. But if you get a group of components
together and they, you know, there's like, you know, puff, and then
something new happens, you know, a bunch of neurons get together and
make the Jupiter Symphony, that's emergence.

So, what's wrong with \-- this is the sort of way people represent
signaling. This has \-- this is not in any way a talk about this
cascade, and this \-- this was likely years and years of extraordinarily
elegant molecular work. But if someone shows you this, if you're \-- if
you're a student at a lecture in this hall and this is what's being
shown, without any additional comment about what's going on, what would
be your objection? What's \-- what's wrong, not knowing anything about
the details here, but what would be wrong with this, just in sort of a
systems biologic context? Physiology is what we used to call systems
biology.

Yeah, so there's a problem. All the arrows are going in one direction.
That's not so great. There's no feedback, there's no nonlinearity
indicated. There should be like a caution that the components are not
interacting in an additive fashion. So this is complicated, this diagram
\-- the system is complex, but the diagram is complicated, but the
complexity is somehow missing. So, complexity and complicated are not
the same, and so I want to cite a \-- someone who was, in fact, an
expert on that. So, there's a quote by Maurice Ravel where he said,
"Complex, never complicated," to describe his work. So \-- because
complex structures, like the heartbeat, have long-range correlations,
they have \-- they're organized, but in an extraordinary fashion. They
have multiscale organization. They're not random. They're not simply
additive. They're something very, very special. And deconstructing them,
which is a bad term, is \-- is a very delicate operation.

So, so the other question is, if you're interested in interacting with a
complex system, if you're into pharmacology, for example, or
therapeutics, which everyone in medicine is, what's the target here? How
\-- what, what \-- everyone \-- if you go into discussions of drug
effects and signaling meetings, you know, it's all about targets, right?
It's very militaristic; "Let's hit that target," you know, but you've
got to be a little bit careful, because in this type of network, what's
the target? So be careful.

With nonlinear network systems, targeted interventions may backfire. You
can hit the "target," you can block something, but because of the
feedback and feedforward, you may end up affecting something not even on
the screen here. So, does that happen? Yeah, you \-- so, it's actually
\-- it happens all the time and \-- and it creates pharmacologic
disaster. So here's a \-- I don't know if this is a new concept \-- but
I think we should be thinking about the system as target, at the end of
whatever you do, which may be informed, and should be informed by the
\-- by the \-- by the details. But there's also the system, which is
what you're trying to interact with.

So, here's the \-- the danger is that there's a linear fallacy, which is
the widely held assumption that biologic systems can be largely
understood by dissecting out the micro components, or the modules, and
analyzing them in isolation. This is called "Rube Goldberg" physiology,
in a \-- in a sense. Not Goldberger, but it's \-- so, so you \-- that
you can take out the components, put them back, and reconstruct the
system. So, another sort of misnomer, is the idea that healthy dynamics
are an equilibrium state, and that there's this sense that health \-- we
were at an equilibrium, disease sort of torques us out of balance, and
then you go, you know, in some chiropractic way \-- not necessarily in
\-- not exactly like that \-- but you get, you know, sort of retorqued,
you know, back to recovery. So that's a problem because health is
actually far from equilibrium, as we'll talk about in a second. But
there is an equilibrium state in physiology, which is called what? Yeah,
it's called death \--

\[laughter\]

\-- which is a state of, sort of \-- the maximum probability where
there's no \-- there's no thermal gradients between you and the
environment, and that's not what we're looking at. So, linear models \--
so, you say, "Well, you know, you have to do something." But linear
models have these huge unintended consequences, and there are probably
no better examples than in pharmacology where people create drugs like
inhibitors of vascular endothelial growth factor to inhibit cancer
growth, but \-- but that may cause increase in heart attacks and stroke.
And you can think of a lot of others, and the papers have been filled
with these. Avandia, just last week, Torcetrapib was \-- increased HDL,
that's great, it gets \-- you know, because HDL, low HDL, is a risk
factor for heart attacks, so why not increase it?

You can do that pharmacologically with this drug, Torcetrapib, but that
caused all sorts of other types of mischief, including increased heart
attacks. Vioxx story is \-- is well known. So, if you hit the target,
you may get \-- you know, that may be the quote-unquote "bull's-eye,"
but you have to be very careful about what's going on at the system
level. Same in looking for this magic bullet of treating, say, sepsis,
which has a well-known set of mediators, and people sort of know the \--
or part of the local mechanisms. But the problem is, quoting my
colleague Bill Aird, that over the past decade, more than 10 thousand
patients have been enrolled in greater than 20 placebo-controlled,
randomized phase three clinical trials, and almost nothing works,
because there's something wrong \-- there's something wrong with our
understanding of the mechanisms. An example of out of box, sort of
nonlinear pharmacology, would be the treatment of chronic heart failure,
where the linear approach is to increase the heart's pumping with drugs
like Milrinone and Vesnarinone, and the problem with that is that you
get excess mortality.

A systems approach, which didn't come anywhere from the \-- I know being
we're Web cast at the NIH \-- but the systems approach, which everyone
now uses, is to use beta blockers, which were considered malpractice
when I was in training, because they would reduce cardiac output, but
they actually also block a vicious cycle of neurohormonal activation and
enhance survival in \-- in heart failure. So the bad news is that
physiology is complex, that \-- there's good news, which is that there
are certain general mechanisms that do not depend on the details of the
system that are called universalities. And there's this \-- what I would
propose is this wonderful world of hidden mechanisms in physiology. And
the terms are here: bifurcations, nonlinear oscillations, deterministic
chaos, time asymmetry, fractals, and so forth. We don't use these terms
much in conventional physiology, or at all, but they're all \-- they are
probably in the \-- in the data, and the world of nonlinear dynamics
opens up a whole new realm of quote-unquote "mechanistic thinking."

One of the most important aspects of complex systems is their multiscale
nature. And one of the defining features in multiscale networks is \--
is this so-called fractal type of structure or function. Fractals are
tree-like objects. They have self-similarity. They're built of the large
scale, which is reminiscent of what you see when you magnify the
objects. So branches give rise to branches give rise to branches, and
this is self-similarity or scale invariance. But you can also see it, as
you do on the right, in processes, like the heartbeat, that have no
characteristic scale of time, that have fluctuations are long-range,
shorter-range, and over minutes or seconds, that they all play together
like a symphony orchestra. So a fractal is an object that doesn't have a
characteristic scale of time or space. A tree doesn't have a preferred
branch size. The heartbeat doesn't have a preferred frequency. So that's
actually a defining feature of healthy complexity, one of them.

The most famous fractal object is the mathematical one called the
Mandelbrot Set. Remarkably, it's \-- it can be created \-- is created
\-- out of this very simple recipe of taking a number in the complex
plain, squaring it and adding a constant, iterating that, and then
color-coding the output. And you get this solar bursting type of \-- of
pattern, of patterns within patterns within patterns; it's remarkable.
Fractals have been around for a long time, before people put the neur
\-- the tame \-- the name of fractal on them, as Mandlebrot did. The
fractal nature of turbulence is beautifully illustrated in this woodcut
from about 1813 by who? \--1831, excuse me. Yeah, Hokusai, "The Great
Wave Off of Kanagawa." So, the \-- the claw-like waves giving off these
wavelets and so forth. So turbulence has this fractal natures. Mount
Fuji in the background is also fractal, as are the clouds.

We like to see fractals, and one reason we like to see them is because
they're also inside of us, I believe. That's \-- that's a \-- we \-- we
sort of experience the fractal essence of nature because we are
ourselves are \-- have this fractal property. Our nerve cells are
fractal. You can also get fractal growth of bacteria. The lung tree,
which we've heard about indirectly with TB susceptibility is, under
healthy conditions, a fractal ‑‑ fractals guide the creation of blood
vessels. And the healthy heartbeat ‑‑ you can be fractal in time, and in
the case of the healthy heartbeat, you have fluctuations over long time
scales ‑‑ you don\'t feel these \-- I want to emphasize that this is not
the source of palpitations ‑‑ if you feel your heart dancing around, or
it feels irregular, that\'s not ‑‑ that\'s not what the healthy
heartbeat is. This is a subliminal sense ‑‑ it\'s like being an athlete
in the zone ‑‑ the fluctuation, sort of the jazzy variability of the
healthy heartbeat is below the resolution of any sensation. You don\'t
feel that.

And that\'s why it\'s counterintuitive ‑‑ but if you look ‑‑ everything
fractal is to expand the window and look on smaller and smaller scales
and then to see if you get statistically, or in some way, self-similar
types of subcomponents, and in this case the fluctuations are there on
orders of hours, to minutes, to seconds. The system doesn\'t smooth out
or flatten out until you get between heartbeats. So fractals produce
something mathematically called power laws which are ‑‑ inverse power
laws have a negative slope on double log clots, they're not exponential
decays, they\'re long-range correlated processes and that is the basis
of some of the tests for fractality and the changes that occur with \--
with various pathologies.

One of the points I wanted to emphasize was that people say, "Well
fractals \-- everything is fractals, so nothing \-- nothing is
meaningfully so." But that\'s not the case. The healthy heartbeat, like
other processes in physiology, is fractal, and when pathophysiology
supervenes due to disease, or severe aging, or to toxicity, the system
literally breaks down, it loses its multiscale variability and there are
only two places it can go, dynamically. If you\'re very, very sick, or
you\'re taking care of a patient who\'s very, very sick, the system is
either going to lose its multiscale variability and just get randomized,
in which case it looks like atrial fibrillation, down the right lower
graph. That\'s variable, but that\'s not nonlinear chaos. Or, it looks
like it does in the left lower part, which is one scale ‑‑ the opposite
of be \-- of having many scales is having one scale.

That makes it very easy to recognize, because if you have just a single
time scale frequency, you're periodic, and then you\'re very transparent
because you repeat yourself again and again ‑‑ you repeat yourself again
and again, and that becomes very boring. But it also becomes clinically
necessary because that\'s how ‑‑ clinicians are not subtle ‑‑ they are
subtle, I don\'t mean to disparage my -- but they can't be \--
clinicians are not looking to differentiate the, you know, Jupiter from
the Haffner Symphony, they\'re looking to \-- to make much more extreme
types of diagnoses.

So, why is it physiologic to be fractal? And this gets at the question
posed yesterday by David about, "What is health, and why don\'t you pick
up a textbook of medicine and find a definition of health?" And the
answer is, in part, because health is really a state of capability, or
states of capability, and it\'s sort of the capability to cope with the
unpredictable. So what fractal systems do, is they buy you a broad range
of responses and these have long‑range correlations, they organize the
system over seconds and minutes and hours, and longer and shorter time
scales, and that leads to adaptability, because you can recruit
different frequencies, literally. It\'s like an athlete responding to
either a curve ball, or a change of pace, or a fastball, and what
fractals do is they prevent the system from getting mode locked.

The worst place you can be in physiology is to be locked into a
characteristic or single mode, then be \-- you become highly periodic
and you lose your adaptability, you lose your flexibility. That\'s what
we\'ve termed the "Tacoma Narrows Bridge" syndrome, so, if you remember
this disaster of engineering \-- this happened because this bridge had a
characteristic frequency, which was knocked over by a gust of wind and
the bridge literally collapsed. It oscillated, and then it collapsed.
And, so that\'s what happens in \-- in adverse physiology. If you have a
characteristic frequency that\'s very maladaptive \-- the loss of
complexity, the lo \-- which is very much synonymous with the loss of
information, is actually a defining feature of disease, and what happens
is the output of physiologic systems often becomes more regular and
predictable with disease. 

And that prac \-- so here\'s ‑‑ the practice of medicine, in fact, is
impossible without such predictable behaviors. Clinicians are very
subtle in their diagnostic ability, but what they\'re looking for with
\-- mostly, is the loss of complex variability, the appearance of
stereotypy, because that allows them to make diagnoses. Healthy
function, which is multiscale information rich, is infinitely harder to
characterize because it\'s a state of being adaptive. And so if you \--
if you look at various types of pathologies, from Cheyne‑Stokes
breathing, to Parkinsonian tremors, to obsessive-compulsive disease,
down the list, what you see are diseases of emergent periodicity. Things
get strikingly regular and metronomic, very boring, but very much
accessible to immediate, or prompt, clinical diagnosis.

So, the question I have is, do memes correspond to one of these
pathologic periodicities, where you get a loss of complex variability
and the system gets \-- gets wrapped up in a characteristic frequency?
It sounded that way from the description. So what\'s the cure? Well, the
cure is to fight ‑‑ is to fight sameness as what was put in this ad for,
interestingly enough ‑‑ this is sort of the anti-meme, so this was like
a Reverend Moon wedding, where everyone was dressed the same, everyone
was doing the same thing. That\'s not a good thing to have, socially or
physiologically, and the antidote, according to the Metropolitan Museum,
is art. And art is therapeutic, which is very interesting. So, what I
\-- if \-- can I run over by ‑‑ by just three \-- yeah, whatever ‑‑
three units of something.

\[laughter\]

So, what I want \-- \[laughs\] what I wanted \-- I, and \-- so, this
will be brief, but I wanted to just \-- to show you what \-- the
dynamics of death and life are very different. So, the normal heart has
this property which is very fundamental, which is time asymmetry. The
electrocardiogram doesn\'t look the same going forward and backward. And
it\'d be very obvious if one reversed that, to anyone who had any
inkling of clinical medicine. The dying heart, on the other hand ‑‑ if
you take the recording below of someone whose literally dying, you can
flip it around, you can put it upside down, no one\'s going to know the
difference. No one would know. I don't even know if I got it right here,
because it\'s time sometric, it reads forward and backward the same, and
because you\'ve gone from far from equilibrium, to close to equilibrium,
and the next step would be death.

So this property of multitime irreversibility, which my colleague,
Madalena Costa, and our group have been developing, has to do with the
notion that time irreversibility is actually greatest for healthy
systems, which are the most adaptive, they\'re furthest from
equilibrium, they take in energy, they dissipate it, they take it in,
and they read differently in a signal analysis point, if you left and
right and right and left. But as with aging and disease, you actually
get a loss of this fundamental property which \-- which Madalena
developed a way to actually measure. So, the warning, then, is that
excessive regularity is bad for your health, and so examples like photic
stimulation ‑‑ those things that are too periodic, so \--

\[laughter\]

\-- that's bad. Loss of complexity; repetitive stress injury ‑‑ if
you\'re doing something that\'s too repetitive, that\'s bad for you.
This may also apply to musicians, as Tony is. And so the question is,
and the final thing I just wanted to mention is that there are many
different ways of measuring complexity. Obviously, you can\'t use ‑‑
there\'s no preferred single metric that will allow you to pull out the
key features of something that\'s beyond our current mathematical tools,
and that what\'s listed here are a number of different techniques. If
you want to read about these and have access to a syllabus in a course
we put on you can go to this website on PhysioNet. PhysioNet is an open
access, open source \-- sof \-- source of software tools and data bases
that \-- that's sponsored by the NIH, previously by the NCR, and surely
to be taken over by NIBIB and NIGMS.

And you can get data software tutorials, you can get the code for
multiscale entropy, which is another measure of complexity that Madalena
developed, and that people have used in other articles, in this case
from the neurosciences. And just as the last note here, I just wanted to
mention that \-- that the idea of using dynamics to actually probe
system function is something we refer to as dynamical assays ‑‑ that if
you enhance system complexity, are you actually doing something good for
the system ‑‑ we think so, because you\'re adding information. On the
other hand, if you remove information ‑‑ make the system less complex,
that\'s probably an adverse outcome.

One test of this is something we just published in a paper by Madalena,
and the group with Lew Lipsitz, and C.K. Peng is \-- is testing a ‑‑ an
intervention called ‑‑ it\'s based on stochastic resonance that Jim
Collins and his colleagues at BU, Boston University, devised, where you
stimulate the \-- underneath of the feet with subsensory stimulation and
that, through mechanisms that are not fully understood, actually noises
up the system and enhances balance. And, so Madalena looked at this
using complexity measures that are multiscale. And what\'s of interest
is that as you add noise to this system, what you actually do in this
\-- in this final graph here is, you can actually enhance the \-- the
complexity of these sway movements. You have people on a balance
platform, you can measure their sway back and forth, and if you put this
noise based therapy on, you can actually make old people more complex
and more stable, in a sense ‑‑ physiologic sense, so that\'s a dynamical
assay.

So, there are other uses in terms of looking at predicting drug toxicity
with cardiac drugs that I won\'t get into, and in forecasting seizures.
Loss of complexity may antedate the onset of seizures, which is very
preliminary data that Madalena has with a group of colleagues at
Harvard. So, what I want to do is, we started with an overture, just the
final 30 seconds is just the \-- sort of the package insert to trainees,
which is just to remind you that you\'re dealing with the most complex
systems in nature. You should look for anomalies in nonlinear
input-output relations, because that\'s what's in the data, and if
you\'re coming up with straight lines on linear graphs, that\'s only a
small part of the system.

Structure and function are inexorably linked in physiology and anatomy,
but it\'s nonlinear structure and function. That you should think in
terms of multiscale networks, phase transitions, emergent and collective
mechanisms, and to paraphrase Sir Arthur Eddington, the astrophysicist,
at the beginning of the 20th century, physiologic mechanisms are not
only stranger than we imagine, but they\'re likely stranger and more
complex than we can imagine. So, I apologize for running over, and I
thank you for your attention.

\[applause\]

Male Speaker:

Thank you, Ary, and I guess this belies the quote from Einstein about,
things should only be as simple as necessary \[laughs\]. And in this
case that\'s not very simple. We next move to our final presentation
before our panel, and that \-- our speaker is David Levy. He\'s an
economist at the University of Baltimore, and also at PIRE, and among
other things, has contributed substantially to the modeling and
simulation of various smoking policy interventions. David.

David Levy:

Okay. Okay, I\'ll be talking about a model of tobacco control policies,
and I\'m going to use a quote that\'s already been used, but maybe I\'ll
use it a little bit differently. And it\'s a quote by George Box, which
says that, "all models are wrong; some models are useful." And, you
know, one of the themes that I think has run through the last two days
is, to go out on a limb, you know. You\'ve got to take chances, try new
methodologies. Don\'t stick to the tried and true. My work is more
methodologically incremental, but I think, you know, what I\'ve tried to
do is kind of go out on a limb, in terms of getting out there in the
policy world and developing a model that people can use. And that\'s
always been the goal from the start rather than to \--

So anyway, been working on this model for about seven or eight years,
with funding from many different organizations, and \-- started out with
funding from SAMHSA, to look at tobacco control policies for the United
States. Since then, we\'ve developed models for a number of states and
countries throughout the world. Our newest one is Algeria ‑‑ if somebody
knows where that is, please tell me. But, what we try to do is ‑‑ you
know, again, we\'re kind of going out on a limb here because the
evidence is usually for developed countries. So, one of the things we've
had to do is adapt the models based on ideas, discussion with people in
those countries, to other countries.

Okay, what is SimSmoke? As I said, it\'s a \-- it's a model of tobacco
control policies and it\'s set up as a dynamic model, starting with
tobacco use and projecting from that tobacco related deaths, then
looking at the effects of policies, most directly on smoking rates, and
then ultimately on deaths. I call it a systems model. It\'s a systems
model in that we try to take into account how the effects vary by the
way in which the policy is implemented, and how it varies with
demographics, and how those demographic effects trace throughout the
system. But then, most importantly, it\'s dynamic. It does have
nonlinear equations in it, and the effects of policies are interactive.
It is set up as a deterministic model, but we do sensitivity analysis
based on reasonable ranges of parameters from the literature. But again,
it is unidirectional, and the key focus is on the relationship between
\-- oops \-- policy changes ‑‑ policy changes and, hmm ‑‑

Male Speaker:

\[Inaudible\] forward, that is \[inaudible\].

David Levy:

Okay. Looking at the relationship between policy changes and cigarette
use. And taking into account, not always explicitly but, you know, in a
theoretical sense, the role of norms, attitudes, and opportunities. As
an economist, you know, I focus on ‑‑ originally was focusing mostly on
opportunities, but we\'re working with other people getting into this
area. I quickly realized the importance of norms and attitudes. Okay.
So, we started off, you know, summarizing literature, and we tried to
look not only at the tobacco literature ‑‑ originally I worked with
Harold Holder on this model, who's from the alcohol field, but we looked
at other literatures in prevention.

In developing the model, we quickly realized that, you know, there were
gaps, so we needed theory. And we developed some theories on our own,
but I think one of the very most important parts of this project was,
we\'ve always had expert panels. What we\'ve done is bring together
people from many different fields, not only economists, sociologists,
psychologists, epidemiologists, and so on. But, anyway, you know, we\'ve
tried to employ that at all stages of the model.

The model begins very simply, as most macro models do, with a population
model. It begins in an initial year and then moves through time with
births and deaths. Then, transposed onto that is the smoking model,
where we divide up the population into never smokers, smokers, and
ex-smokers and the process evolves through time through initiation,
cessation, and relapse. The death model, which I\'m going to talk very
little about, is a very standard model of smoking attributable deaths,
and what I\'m going to focus on are the policy modules.

Okay, so again, here\'s our basic population model, in some of the
models we do incorporate migration and immigration, but it's simple.
Year by year, people age, move through time ‑‑ we do distinguish not
only by age, but also by gender, in the models. What happened, there \--
What we have here, the arrows ‑‑ oops, it got knocked out \-- oh \--
there it is, amazing. Talking about \[laughs\] emergent properties ‑‑ we
start off \-- people, you know, are born \-- oops \-- as never smokers.
I\'ve got to get these two straight \-- see, this is why I don\'t do
truly complex models. We start off \-- you know, people are born as
never smokers, although one of the extensions we\'re looking at is how
effects transmit over generations. But in this model people start off as
never smokers, some proportion initiate each year between the ages of
about \-- actually starts at age 10, through about age 24. Then, if they
stay a smoker for a year, they\'re considered a current smoker, which is
someone who smokes every day or some days.

And some proportion of those smokers quit each year, but unfortunately,
another portion relapses. So basic model is our stocks are the never
smokers, smokers, and ex-smokers, which by the way, we do distinguish by
year, and the flows in are through initiation and relapse, and the flow
out of smoking is through cessation. We have extended the model, and
I\'ll talk a little bit \-- by including smoking intensity variables,
that is, per day quantity smoke \-- average quantity smoked per day, and
number of days per month. We\'re also looking now at incorporating
duration, types of cigarettes, and other tobacco use. But that\'s where
things get really kind of, complex and messy, especially when you try to
determine how policies affect these kinds of variables, and ultimately
affect death rates.

We\'ve also developed some models distinguishing by education, income
\-- actually, we\'re starting to develop those, and thinking about
racial, ethnic, and geographic variations. Again, here\'s the simple
model of deaths, where we examine ‑‑ we have a model that gives total
deaths, total mor \-- based on total mortality risk, and then we
distinguish by types. And I mentioned we are \-- we also have a separate
model that looks at quantity and duration, but again, the problem is,
how the policies really affect that and what affect does that have. In
other words, if a policy gets people to reduce the quantity smoked,
there\'s a finding that they compensate by inhaling more deeply. So, is
that kind of change going to have the same effect as somebody who\'s
been smoking fewer cigarettes over \-- you know, since they started?

Okay, so now the policy modules. And here\'s the five that we focus on
in the US model, in other countries we focus also on warning labels and
advertising, which have kind of been ruled out, at least for now, in the
United States, as potential policies. The way we modeled this is \--
most of the effects are through percentage reductions. And, what we
do ‑‑ and there\'s some support for this in the literature, although one
thing I want to emphasize is, the literature, at least in tobacco
control, and from what I\'ve seen in public health generally, does not
do a good job at examining dynamic effects over time. In other words,
how things unfold or don\'t unfold. And that\'s ‑‑ I\'m sure there\'s
much more complexity than we have, but we have large effects in the
first couple of years, which is what most studies look at, and then the
effects are maintained through time and \-- and sometimes increase in
time, through their effects on initiation and cessation effects.

We also allow the effects to differ by age and gender, and allow those
to translate through the model, which is what I call one of the systems
effects, which statistical studies generally don\'t do. Another thing
that we\'ve tried to do is look at how the effects vary depending on the
way in which the policy is implemented. You know, oftentimes people do
meta-analyses of policies, but the way they\'re implemented is very
different. What we've focused on, in lots of our models, is trying to
explain the variation in results. Okay, starting off with taxes, here we
do have fairly uniform effects, and \-- but what we see is the effects
do vary by age. And \-- actually these elasticities were original
elasticities. We\'ve changed it, so for seniors we now have a higher
elasticity.

What we found out is that the model predicted really poorly for seniors,
and so we looked at ‑‑ you know \-- and the major policy change in the
model was the price change, so we did a study and we found that seniors,
in fact, do have more elastic, as economists would call it, more
sensitive demands to price. And what happens in the model is, just
through the age effects, the effects of a price increase do increase
over time because the bigger effects are on those who are younger and so
if you trace it dynamically through the system, as those people age the
effects of an increased price spreads out, and there are bigger effects
if you look at the overall adult prevalence. There are some dynamic
issues that we\'re looking at. And again, you know, even in \-- on taxes
and prices where we have the most studies ‑‑ I mean, literally there\'s
hundreds and hundreds of studies, we really don\'t have ‑‑ we have good
knowledge on the relationship between prices and smoking prevalence. We
do not have good knowledge of the effects on initiation, cessation, and
relapse rates themselves.

You know, one of the things we also found is, there is \-- there does
seem to be relationship between quantity and prevalence, which also is
not really developed in the literature. There\'s these separate models
that are only very indirectly connected. Next, we look at clean indoor
air laws, where we have many, many studies of firms, or groups of firms,
to look at the effects of various restrictions. And we find somewhat
consistent results over those studies. Now, those are policies that are
enforced at the firm level. When we talk about clean air laws, it\'s at
the broader local, state, or national level and \-- and here the studies
are not so good. And, in particular, we do have a couple of good studies
that look at worksite laws, but as far as the other kinds of clean air
laws, the results are, at best, very tentative.

So, what we tried to do is develop a framework for looking at these
different laws where we took into account \-- looking at the worksite
laws in particular, things like, you know, the restrictiveness of the ‑‑
how restrictive was the smoking ban in a firm and things like that. What
we also did was, in trying to actually look at the effect of clean air
laws \-- and this is something that none of the empirical studies do \--
is take into account, when you pass a law there are already many firms
that have these clean‑air restrictions. Then, another factor that we
recognize, and again, this is a lot through the panel and discussions,
is, even though these laws seem to be self-enforced, and this became
very important when you look at other countries, they\'re not so well
public-enforced and \-- or not so well enforced where they\'ve been
tried, especially in some of the less-developed nations.

I\'m now doing a model for France, and it will be interesting to see how
well their new clean air law is actually ‑‑ how much compliance we
actually see in France. So, public support seems to be important, which
suggests the importance of the role of other policies, like for example,
you know, when I ‑‑ in the work with Malaysia and Thailand, you know,
even though clean air laws is considered one of the big three policies,
you know, I\'m very cautious in terms of recommendations and say, "You
may want to first have comprehensive mass media campaigns, make sure you
raise the taxes, you know, start doing things to sensitize smokers, and
also start changing norms so that when these laws are passed they are ‑‑
there is some compliance." So the model tries to take into account all
these various factors in trying to look at how clean air laws will
actually translate into effects on smoking prevalence.

Next was mass media policy, and most of these studies, when you look at
them, look at, you know, kind of a before and after picture. And
there\'s many different studies, but the effects are fairly mixed. And
most of those policies are implemented with other policies. And, in
particular, the ones where you see the strongest effects of media
campaigns, such as in California and \-- and Arizona and Massachusetts,
you know, there are many other policies in effect. So, these models are
basically before and after kinds of pictures with, you know, some of
them look at the pattern of effects over time and usually don\'t explain
those too well.

So we tried to, you know, think more about it, bring in some theory, and
in particular bring in quantitative dimensions, and the effects of other
policies. What we did stay away from is content, with the recognition
that content is very difficult to address, and probably, good content in
one type of environment would not work in another. In other words, what
works in California may not work in Kentucky. We also do not bring into
account tobacco advertising. Granted, an important feedback loop, but we
don't have information. And in fact, the studies that have been done,
you know, don't control for tobacco advertising, so implicitly it's a
net effect of tobacco advertising. But, you know, we've thought about
these issues.

Okay, so what we did is bring in, you know, the kind of curve that
economists are very familiar with, the old S-shaped curve. And \-- which
gets across the idea that you have to get to the point where you're
going way up the slope, you know. And this is \-- this is the type of
curve that's used by people in advertising, who have the motto, "Got to
hit them three times before pier \-- before the ads pierce the army \--
armor." What we also brought in, was to take into account other
policies, recognizing that much of the information that people get is
not from the \-- the ads themselves, but also the media coverage that
comes with it. So, this is why in states like California, where there
does seem to have big effects, we would attribute that to the tax
increase and some of the other laws that came into effect and also
worked to help change norms and attitudes.

Let's see \-- oh, next is the youth access model, and here again \--
here's a literature where the results are all over the board. And in the
panel we had, we had people that thought that youth access policies are,
you know, bad. They're \-- they're bad because they detract from good
policies, and we had people that said that they were very good, and
people in between. And the literature, if you looked at the literature,
some of the studies found that they were effective, some not.

So my goal was to develop a model, which explained, you know, where, in
fact, youth access policies \-- those are laws that try to stop the sale
of cigarettes to youth, where they're effective. And we considered two
important factors that weren't really considered in any of the studies,
and that is the role of non-retail sources, because as you can see here,
youth get over half of their cigarettes \-- and this is actually older
data, my guess is it's even more \-- more skewed now, it's probably \--
a larger percent is even from non-retail sources. But, you know, even
back when we developed the model, less than 50 percent was from retail
sources. The other was from non-resale, other sources, and theft, I
don't know if you'd consider that retail or not, and vending machine
sales. So the idea was to think about how, when you do youth policies,
how that might affect non-retail sales, or non-retail youths.

So we developed a model that incorporated that, and also incorporated
\-- looked at how the different types of youth access policies \-- most
of the attention focused on compliance checks, but also the role of
penalties and merchant concerns. And we developed a model that started
off with the policies themselves, brought it together in a
multiplicative function. Then in terms of retail compliance, we again
have, you know \-- as an economist I love these S-shaped curves, and
they particularly make sense here because you have to get to a very high
level of retail compliance, particularly in a city like New York,
because, you know, if there's four or five people selling in Manhattan,
the kids will probably know those sellers right away.

So, we have an S-shaped curve relating retail compliance to purchasers,
and then we also have substitution into non-retail sources. So, and this
led to effects which allowed for substitution. I'm running low on time,
so I won't get into the effects. But we also distinguished effects for
older versus younger youth, who had more ability to substitute because
they knew \-- they were closer to 18-, 19-year-old friends.

Next, with cessation treatment policies, here again we have very good
knowledge on the effectiveness of treatments, though the evidence on the
actual policies, how they affect treatment use, and also, even more, how
they affect quit attempts, was less clear. So we started off with a
decision theoretic model, and then we tried to trace the effects looking
at random controlled trials, bringing in real world effects. Now, to get
into some of the results of the model. There's two important parts of
the model. There's the tracking model, these \-- what we try to do is
start the model at a year before major policies go into effect and
hopefully we have good data going back that far. And we track it, then,
up to the present, and that gives us the ability to calibrate and
validate the model, and something else which I'll talk about is look at
the role of past policies. Then, to the extent that the model works
well, we can use it for future projection.

So, again, you know, we want to justify the model. And we justify it
based on, you know, how it's a reflection of past studies and so on and
through publications of reviews and all that. But another important
thing is benchmarking, that is, trying to explain the different studies
in an area. Try to, you know, explain the different studies of youth
access and within our model of how policies work. And finally,
validating the model; here are the results for \-- for smoking
prevalence. And I think the thing that's impressive about these results
is that we do explain the turning points. Our timing is a little bit
off, and that's one of the things that got us into quantity, because the
big change that occurred right here was a large price change.

We also found \-- and, you know, one of the things I also want to
emphasize \-- I think others have said this, but, you know, what you
learn from modeling, as much as, you know, what you can explain in the
real world is what you don't explain. I mentioned before how, you know,
for seniors, we didn't explain well. And this had us go back and
actually do some empirical studies. We're also finding that for 18- to
24-year-olds, the model doesn't explain well. And this is an area where
we need more research. But anyway, so, I mentioned we want to look at
quantity. We have a quantity component in the model.

Here's the results where you bring the quantity and the prevalence
component together to look at overall consumption per capita. And you
know, quite frank \-- to be honest with you here, the first couple of
years we used to calibrate the model, we validated over this period. In
other words, this was the period we used to try to see \-- get better
parameters of quantities, since the estimates in the literature were
somewhat weak. And what you see here is, I think we predict almost
embarrassingly well. You know, when I see things predict this well
sometimes I'm suspect. But the other thing, again, is to notice \-- and
I think it's important in validating models, is again, we're explaining
turning points. That is, our model does well compared to a linear trend
line.

Next is the time model, and I bring this in because Thailand has been a
really amazing country in terms of tobacco control, so it's one I really
wanted to look at. Notice, they've had some really effective policies.
And, ag \-- and so we did the model, and over the, you know, period of
time, we looked at \-- the model did, I would say, quite well. You know,
the beginning point, the end point, even the last couple of points are
right on. And also, the trend over this period is, appears to be \--
well, it is very close. Where we failed is actually \-- or where we
didn't predict so well is very interesting: in the mid '90s, and then at
2000, 2001. So I went back and looked at descriptions of policies in
Thailand.

What I found, and you know, this came out very clearly, was that the
tobacco industry was really doing all they can to avoid the advertising
ban, which was one of the major policies implemented there. What also
occurred here was \-- which was somewhat a reflection of the \-- the
actions of the tobacco industry was \-- at this point, a very important
NGO, Thai Health was created, and they've been very effective at getting
the policies implemented and enforced. So these are feedback loops,
which, you know, I eventually want to incorporate into the model. And
again, what's important here is what we learn from where the model does
not predict so well.

Okay, now \-- there's three \-- you know, we originally did the model to
look at policies, justify the use of policies, and so on. In other
words, you know, from the literature, project forward what kind of
impact the policies will have. What we've also done is use the model to
distinguish the effects of policies in the past. And I'll use the
Thailand model as an example, because I think we do predict quite well
there. And compared to statistical studies, where you have many
different policies going on, it's very difficult in most case \-- I
haven't seen one study which distinguishes more than two tobacco control
policies at a time, even though usually there's many different policies
going into effect. So, what we do is, we look at the effect that
policies have, and use this as a justification for future policies. And
we've done this in California and Arizona, but in particular, we've done
this for Thailand. And so what we do is we set the model to the initial
year policy variables, run it, then compare it to the policies actually
implemented. And from that, we can look at \-- oops \-- the effect of
policies.

And you see that, you know, according to our model, which predicts quite
well the effect with policies, if the policies weren't in effect, you'd
have a much slower decline in smoking rates, for males. For females, it
would probably be going up. For males, you know, it would \-- it would
probably have gone down at a very slow rate. With the policies, you see
a 25 percent reduction compared to what it would be. Now, we can't
actually distinguish the effect of particular policies, but \-- except
insofar as what the model attributes. But useful to \-- to policy
planners is to say, "Look, you know, according to the model, you know,
based on the parameters on the best estimate, the effects are such that,
you know, 44 percent is due to price, 43 percent to advertising bans,
and so on." So again, it provides an important justification for future
policies based on the effect of past policies.

Two more purposes of the model: one is as a heuristic device. Again,
this is something that many of the speakers have \-- have emphasized
today. And \-- you know, heuristic not only to academics, hopefully \--
and not only in bringing together the different research, but also again
in finding out what we don't know. But we've tried to make the model so
it's heuristic to those who implement policy. We're working now \-- we
have a grant to work with Kentucky. We've been working very closely with
Thailand and Malaysia. And they've been using the model and what we've
tried to do is, we give a workshop and we go through the model. And what
I've done for countries is developed Excel models so they can actually
see the guts of the model. They can actually use the model. Being on an
Excel platform is something where they're familiar with the programming
language, if you will. In other words, they could work with the model.

Finally, you know, as \-- in working with the states, we've tried to
make this part of their planning system, not only in terms of, you know,
planning the effects of policies, but also as part of their broader
surveillance and evaluation system, to help them determine what data
they need to collect, how finely they need to collect the data \-- in
other words, by demographics and so on, help them with evaluation
methodology, when they do studies are they within the ballpark or not,
and so on. So, in a sense, the model is dynamic in two ways. It's a
dynamic model in that it predicts models over time. But it's also a
dynamic modeling process. And I think Dave Abrams mentioned before, one
of the things I think that is an important focus is to think about not
only how to develop these models, but how to adapt the models themselves
over time as we get new information. Thank you.

\[applause\]

Male Speaker:

Okay, can I ask the speakers from this session to come to the table?
We'll have a slightly truncated Q and A period so we can get to the
panel discussion. And I'm going to ask Stephen Marcus to \-- to lead the
Q and A session. Stephen is the senior editor of Tobacco Control
Monograph at NCI \-- has been very helpful to us in making this meeting
happen, and is one of the great enthusiasts for complex and systems
modeling at NCI. Stephen?

Stephen Marcus:

About 20 minutes? Okay. So, I actually just want to be one of the first
to thank George and Carl and the whole planning committee for, what I
think, has been a really terrific symposium. So, thank you.

\[applause\]

Just sort of listening to the hallway conversation, I think I've been
transported to another plane where everything is so interesting. It's
like I go here and then I go there. I want to be on this conversation
and that conversation. It's really been terrific, so thank you. And I
wanted to make one brief comment about the \-- the session, which I
really loved. I really thought it was terrific, because I think that
systems thinking and systems modeling are different. You can use
modeling to help you think about how you should do this sort of thing,
but I do see some distinctions, and I think the session did a really
good job of showing how modeling can be used to help us think, but there
were also, I think, some terrific presentations by people that didn't
show any modeling, but yet really showed how systems thinking is a way
of thinking about asking the right kinds of questions and, how you go
about asking those questions. So, I think it was terrific. So why don't
we start with the questions. This side?

Rob Steiner:

Rob Steiner, University of Louisville. I was impressed with Mercedes'
presentation, by the fact that it was followed, you know, by Ary's. And
you spoke about El Nino and the change in periodicity of a disease, and
Ary spoke about body parts and systems in an incredible fashion. Help me
with defining the scope for modeling?

Male Speaker:

Mercedes is going to help you out with that question.

Rob Steiner:

And it's a serious question, really \[laughs\].

Mercedes Pascual:

No, no. I have no doubt, but perhaps too serious. Let's see. So, you
would like to \-- were you looking for some similarities in the process
of modeling from the two perspectives, or looking at the population
level, physiological levels? Can you be a little bit more specific?

Stephen Marcus:

Let's \-- let's say that we were looking at cardiovascular disease, you
know, from a complexity, physiologic perspective. It seems to me that
there could be confounding factors that might have to do with things
that we might never consider, like El Nino, and you know \--

Mercedes Pascual:

I \-- I would \--

Stephen Marcus:

I'm really trying to get a handle on how to define the scope of research
for a real scientific investigation, from the perspective of systems
thinking and mod \-- and modeling complex systems.

Mercedes Pascual:

Yes, I see there are some analogies, obviously, between the two types of
talks and even the studies in those areas. You were asking \-- I think
you mentioned El Nino, but in this case, in the physiological studies,
you may think of something driving the system that may be very well be,
even some signal that \-- that these parts of the \-- the physiological
part of the \-- well of the individual, but doesn't cover strong
feedback to the \-- to the variable you are interested in. So in that
\-- in that case, I'm not sure, but you can have some examples of
drivers that are part of the system that just function as an \--
essentially as an input to your \-- to the variable you are measuring.
In that sense, you could use similar approaches, very similar
approaches. I think the \-- in terms of some of the \-- help to model
it.

And for example, we have modeled the system, in some cases, with some
time series models that have \-- that are inspired from neural networks,
not neutral networks, but neural networks, so that we were making no
assumptions initially about the dimensionality or which variables were
important and so on, and \-- types of analysis that have been used to
very different types of nonlinear signals. The strong difference there,
I think, is mainly that, well, from my perspective \-- I have a strong
envy for what they do, because they have types of data that we will
never have at population levels. That is unfortunate, because I think if
we had those types of data, we could do much better at all these goals,
like early warning systems and so on. So it's fantastic to \-- I think
it's one of the areas in biology where you can actually compete with
some of the things physicists measure in the lab.

But in terms of looking at what you call the system, and what you call
the drivers or these sort of external variables, I think it's only a
function of to what degree there are feedbacks. But from what you are
defining as your system, and something like El Nino. In the case of
cholera, it's obvious that there is no feedback hopefully, in that \--
in that direction, and I think there will be some \-- perhaps you can
\--

So the definition of the system will have to do with the relevant
variables among which there are \-- there are feedbacks and others will
be considered drivers or variables that you \-- that you may want to
analyze in the same way you analyze climate. I'm not sure I'm getting an
answer to your question.

Ary Goldberger:

Yeah, man, I think your question is so \-- goes so far beyond the
capabilities of modeling at any level that it \-- you \-- it's \-- the
challenge very daunting. The only \-- there \-- whether El Nino, and to
what extent it affects an individual's health, you know, is \-- it's not
at all clear on \-- you know, in any daily circumstance or
cardiovascular health, as you say. But what I can tell you is that there
are certain major external events, which are major stressors, like
earthquakes and 9/11, which were followed by a huge spike in sudden
cardiac death through stress effects. So where you'd have some external
event that clearly caused a major change in outcome, that would not have
been anticipated, but from an individual point of view -- we're looking
at with time series, or what goes on, in \-- it's sort of
intra-individual, and in the physiology, say, of heart failure or of
aging and so forth. And so the connection to the very elegant work that
Mercedes does, where they're looking at incidence of an \-- of an
infection, which may be cyclic, and may be driven by some periodic
phenomenon going on \-- on a planetary basis is different.

The one thing that would connect them, and it wou \-- it's not going to
be particularly satisfying, I think, in terms of the real gist of your
question, is that the nonlinear dynamical modeling, which would involve
\-- goes back to the Huygen's \-- the oscillatory model, of coupled
oscillators \-- that there may be commonalities to this sort of thing
that would be of interest mathematically on the different scales. But
beyond that, I don't think that there's any ready answer to what you \--
the challenge you pose.

Mercedes Pascual:

But if you measure two different measurements in the body, something
that could be part of the \-- perhaps the nervous system or something
that could be, some other, \-- let's say other system, I don't know the
right terminology. And you had measurements on both, you could measure
both, and one was responding dynamically to the other, or you wanted to
know, and there were no strong feedbacks in one direction, you could use
very similar approaches. So it wouldn't be El Nino, but it \-- it would
all be within the individual, but it would still be a question of trying
to determine to what \-- to what degree it's a response of the intrinsic
dynamics of the system, or whether you will benefit from so \--
measuring some driver, also. So I only see an analogy at that level.

Ary Goldberger:

Yeah, and one of the great challenges is because physiology is, I mean
the heartbeat is one signal. It happens to have multi-factorial inputs
and affect other signals. But what you \-- it's really very much like a
symphonic score, where you have all these different instruments playing
and talking to each other, and the crosstalk, and how one signal
influences another, and what \-- once again, causality is a very murky
notion in these multi-signal types of networks. You know, what causes
what, is \-- and what you get are emergent processes and then feedback
where things interact, they cause some larger scale change. And the
larger scale of change feeds down, so it's both bottom up and top down
as I mentioned. The mathematical tools, actually, for that, probably
have not been developed. So that's a huge challenge. The mathematical
\--

Stephen Marcus:

That's what I needed to hear. Thanks.

Ary Goldberger:

\-- tools are not there yet. So I \-- so I think that's -- so you throw
down a major gauntlet, but unfortunately, we don't have the heft to pick
it up yet, but it's a good, this is the right place to pose it.

Male Speaker:

It's a great discussion. Let's go to another question.

Diane Finegood:

Diane Finegood, Canadian Institutes of Health Research and Simon Fraser
University.

I guess this is more of a comment, and maybe an extension of this last
discussion that was just taking place. Dr. Goldberger, you argue that
disease is the loss of complexity, but it's also possible, if you take
that up to the public health level, that disease is when the system's
complexity exceeds the complexity, and therefore capacity, of the
individual to respond to that complexity.

So, and here I like to think about again \-- I'll bring it up, the
situation of obesity. Where, I'm not sure, maybe the public has lost
their capacity to deal with the complexity of their environment, which
is incredibly complex when we think about all the different factors that
affect food and physical activity related behavior, right? But it also
speaks to the notion that if your capacity to deal with that complexity
is exceeded by the complexity of the environment, you may fail in that
kind of a systems thinking approach. But it also suggests to me that
when we start to think about solutions, it really speaks to the nature
of the solutions, because in public health, we talk about, well, we need
to make the healthy choice the easy choice, so that people make the
healthy choice more often. And I think that is probably true, but that's
challenging. It may also suggest that we have to find interventions and
solutions that increase the capacity of the individual to deal with the
complexity of their environment \-- therefore increase the complexity of
the individual themselves, and therefore, increase their capacity.

Male Speaker:

Thank you for that comment. Going to this side.

Female Speaker:

Lily \[unintelligible\], UCLA. I have a question for Dr. Goldberger. It
appears that stochastic resonance is possible in systems that have some
sort of a natural oscillation frequency. So, if you have a healthy
system, where you have complex signals, you should not be able to
observe stochastic resonance, whereas you should be able to observe it
in an aging or diseased organism. I would be curious to know if there
are actually any studies that observe stochastic resonance in a \-- in
an organism.

Ary Goldberger:

So, what you're referring to these \-- in Jim Collins' work of having
subsensory noise, which \-- as an input in enhancing balance control,
where there was a connection to quote-unquote "stochastic resonance," or
that was at least the motivating, one of the motivations for trying that
input, whether it \-- whether it, in fact, is to true stochastic
resonance where \-- noise \--what the actual mechanism there is, is not
entirely clear. But my recollection is that, even in younger healthy
people, that the noise did, in fact, have a positive effect on their \--
on their balance control. Madalena, did you recall?

Madalena Costa:

\[Inaudible\].

Ary Goldberger:

Yeah, so the interesting \-- that's Madalena Costa, who did the analysis
of the complexity of the dynamics. But \-- so, in younger people who
have, you know \-- it's not an optimally complex balance system or a
highly developed one \-- but intervention with noise still had some
subtle effect. It was \-- it was positive, but less so than in the \--
in the older individuals. But that's an interesting \-- that's an
interesting question. It's probably the \-- sort of the classic
stochastic resonance model where you have two wells or something. That
doesn't apply in physiology, where you might have an infinite nested
type of network of responses. So that's an open area. It's a very
interesting challenge, though.

Female Speaker:

Thank you.

Male Speaker:

Do we have time for one or two more?

Male Speaker:

Yeah, \[inaudible\]

Male Speaker:

Okay.

\[laughter\]

Male Speaker:

Well, I was going to give one, but I don't know about the two.

Male Speaker:

Two.

Male Speaker:

We'll get two more.

Male Speaker:

Two. It sounds fairer that way.

Male Speaker:

Yes.

Male Speaker:

So, I'm trying to draw the connections between agent-based modeling,
systems dynamics modeling, compartment modeling, and complexity
modeling, as Ally \-- as Ary presented it, or chaos-based modeling. And
\-- and I want to back into that by asking Ary \-- and perhaps some of
you can comment also. In your very interesting work, have you considered
developing agent-based models or other kinds of models, which generate
the loss of complexity? So for example, modeling the changes in the
communication feedback and feed-forward between all the systems that
contribute to balance, or that contribute to the patterns of \-- in the
EKG that you see.

Male Speaker:

So, one of the problems is terminology; what people mean by agent-based
modeling and if \-- if cellular automata are an example \-- or is a
subset of agent-based modeling, which according to some it is, then
there is a nice sort of toy model of \-- network model that Luis Amaral
and Albert Diaz Guilera and our colleagues developed. It was \-- so the
mathematics of it involves having these nodes around. It's a combination
of bullion and a cellular automata model. And so what happens is if you
have just the \-- if you have \-- within a range of the right amount of
noise, you can get the type of fractal scaling that you see, sort of, in
physiology, but not as subtle.

But if you break connections, you \-- if you disconnect some of the
components to a critical extent, you isolate them, and then you have a
loss of complexity in the output. Or if you have too little or too much
noise, you actually can degrade the complexity of the output. So that's
a very \-- that's a toy model that has nothing explicitly to do with
physiology except in a metaphorical way. But what's interesting, and
what is physiologic in a sense, and may have to do with aging, is \--
what we know what goes on with aging is that you degrade both the \--
there's a dropout of cells, say, in various parts of the nervous system
in cardiac cells, and also the connections are degraded, so \-- so
that's interesting to the extent that that may be a mechanism of sort of
the geriatricization of the senescence of the \-- of the system and a
loss of complexity, but that's a loss of information in the system.

Stephen Marcus:

Okay, and I think we have time for just one more. So, Mitch.

Mitch Waldrop:

Mitch Waldrop. It's a question for David Levy. You built this absolutely
awesome model for how to disregulate and discourage one kind of
addictive behavior, namely smoking. How about \-- to what extent can you
adapt it to look at regulating and discouraging other addictive
behaviors like gasoline use, or \-- pick your choice, or illicit drugs
for that matter. And as a follow up to that, have you ever encountered
any opposition, hostility, from elements who don't want to see
regulation be effective?

David Levy

Well, first I'd like to thank you. And yeah, these \-- there are policy
models like this in other fields. And in terms of my own work, I've been
working towards developing models in alcohol control policy, and
hopefully I'm going to have the time for that in the next year. But also
an area which I have gotten into is obesity policy, which in many ways
is just much, much more complex. What's nice about smoking is it's
well-defined, like gasoline use, so gasoline use would be a nice one to
maybe look at next. But when you get to obesity, because you have
exercise and you have eating and then the interrelationship between
exercise and eating, and then you have policies affecting usually
consumption, maybe just at the school, or some other place, you know, at
McDonalds or whatever. The relationships are going to be much more
complex and that's going to be a \-- I think a very, very heuristic
model.

Male Speaker:

That would have to be completely a new model. You couldn't adapt your
model to this.

David Levy:

I mean, there'd be some of the same elements. I would try to \-- you
know, in thinking about even the alcohol model, it's tough because, I
mean, with smoking, because it is an addictive behavior for at least the
vast majority, if not all smokers, you'd have a well-defined event. Once
you get to something like eating or alcohol, it's not \-- what's
excessive behavior. Where is it an addiction? It's very difficult to
define, and hence model. So, you know, when you get to those kinds of
behaviors, what's very important is how you define the outcomes.

Stephen Marcus:

And as a teaser, if you're going to be at the workshops tomorrow, there
is a systems dynamic modeling in which they will show you a first
generation model of the obesity problem. So. Thank you.

Male Speaker:

Thank you.

\[applause\]

Now I'm going to ask five people to come up and lead us through the next
exercise. You've met them all except for David Abrams, and David Abrams,
who I've given the responsibility for chairing this panel of what's
next, is the director of the Office of Behavioral and Social Science
Research at NIH. This is a very influential office within the Office of
the Director of NIH, which basically communicates with all 20-some
institutes at NIH. So if there's anyone who is used to dealing with
complexity and chaos, I think David would be an excellent choice. So
we're going to have David, Ana Diez Roux, Sandro Galea, Carl Simon, and
Mercedes Pascual. And their marching orders are to speak for a few
minutes \-- meaning a few \-- about next steps, and then open the
discussion up.

David Abrams:

Thanks. It's the end of a long day, an exciting two days. And I guess
I'm very interested in hearing about next steps and hopefully can take a
little bit of time to think about what have we learned from this, where
are we, and what are \-- where do we want to go with this in terms of
next steps? I just want to make a couple comments myself and then turn
it over to the folks here. A couple of things that struck me as I think
about these two days: firstly, I think this is an incredibly exciting
transdisciplinary sort of synthesis of new things that are coming
together, and if anything, I daresay that we're on the bleeding edge, or
the beginning edge of a true Kuhnian-type revolution in terms of moving
from compartmentalized, linear thinking, to in \-- to the sort of phrase
that the actions are in the interactions, and that we're moving from
linear causality to causal loop models and loops within loops in
different cyclic timeframes that can scale up literally from nanoseconds
of neurons being organized in brain circuitry all the way up to
macroeconomic policies.

And I see some of those implications here as we scale up and down.
Secondly, I think \-- I'm very struck by the fact that we've got new
technologies developing that provide incredible, rich sources of dense
data at multiple frequencies and levels, which is what I alluded to in
one of my questions. And yet they're not fully talking to or informing
the people on the other side who are doing the systems modeling, and
this is where the practical implications or the industrial strength
modeling that was mentioned earlier comes in, in the sense that, for
example, as David Levy tweaks his model and uses historical data to
fine-tune the various engines in the model, it becomes much more real
and much more relevant, and much more respected as hard science that
could truly inform policy even against counterintuitive and
policy-resistant decision-making of the kind that we see in politics,
where, for example, short, immediate impact around an 18-month to
three-year perspective in decision making in politics is, in fact,
aligned and reinforced by the current system \--

\[audio skipping starts here\]

\[skip\] adaption to the environment is a key factor here. If there is a
single grand challenge, it's to develop some better system \[skip\] to
schema that involve irrational and rational or intuitive thinking around
the limbic \-- how the limbic brain interacts with \[skip\] and how that
weights human decision making towards a central function, which is that
there's a reward mechanism in the brain \[skip\] genetically influenced,
but when you've got a reward pathway that drives a \[unintelligible\]
goal-directed behavior, I think we've really got to have \[skip\] and I
didn't see \-- very little here about those neurosciences whether it's
social neuroscience and mirror neurons that could bridge between biology
\[skip\] imitation works in that way, or whether it's reward pathways
that drive stress and perceived stress and other goal-directed
behaviors, whether it's \[skip\] enormous amount of bridging that could
be done by putting brain and behavior as a central bridge between
microbiology and cellular \[skip\] mechanisms on the other hand.

So I think there's just lots to learn here from a huge number of
different disciplines. \[Skip\] and I just want to close by saying that
as I represent some thinking about the importance of behavioral-social
science within the NIH, my \[skip\] systems thinking, systems dynamic
and other viewpoints here, as well as I think the integration of things
like agent-based \[skip\] to see a more joining linking of things like
agent-based models and system dynamic models, for example. But NIH does
have some \[skip\] across institutes for this kind of work. And in fact,
there are some programs in measures and methodology development; the
National Institute \[skip\] quite a bit of the research, some of which
was presented here by Josh Epstein and others. And our group is going to
want to try to promote more interest in this \[skip\].

And I guess in closing, I think the idea of real-time data collection
from multiple new technologies, whether they're Web-based or PDA
\[skip\] individuals over time can really be used in a feedback loop
with modelers, to get the practitioners and the modelers and the
real-time \[skip\]. \[Skip\] end of the day, modeling is great for
informing theory as well as for refining our thinking, but at the end of
the day, if it doesn't make \[skip\] a quality of the species within a
worldview, you know it's not really doing \-- it's got to be practical;
it's all Kurt Lewin's old issue, that, you know, there's nothing as
\[skip\] current modeling and practical implications that would make a
difference in population health improvement. So I think those are some
really exciting \[skip\] incredible, different approaches, and I'm
really excited, because I actually wasn't aware of three-quarters of
them.

So I think just the fact that we're all \[skip\] a new synthesis in a
new field. I guess the last thing that I want to say is I think
development of new measures and methodologies are important biomarkers
of \[skip\] because the current measures, biostatistics and
methodologies are inadequate, because we've got a new view of the world,
and therefore we need to create very \[skip\] transform the behavioral
and social sciences dramatically. To me this is like the discovery of
the telescope or the electron microscope for the behavioral and social
sciences \[skip\] time; social support, social stress, social phenomena
in ways that we just couldn't do ten years ago because of the
technologies and these approaches that \[skip\] and then we'll move on.
I don't know who's going \-- do you want to just start and go clockwise?

Female Speaker:

Okay, I know \-- can you hear me? I know everybody is \[skip\] and my
comments will be very much from the perspective of someone who does
population health and epidemiology, and is interested in the substantive
questions that we want to answer in population health. \[Skip\] the
second two are a little more practical, maybe not as practical as we'd
like, but a little more practical. So the first point I want to make is,
I think one of the main contributions to \[skip\] of systems thinking,
or complex systems thinking, or complex adaptive systems thinking is
precisely that it stimulates us, and maybe even \[skip\] than we often
do. It allows us to think about feedback and nonlinear facts; it allows
us to think about the possibility of unanticipated effects; effects
distant in space, time \[skip\] and robustness.

So in a sense, it allows us to recover a holistic approach which public
health and medicine had in its origins, but \[skip\]. And I think this
is especially important, just the thinking about it, because \--
especially in today's epidemiology, and in public health generally,
we've become \[skip\] certain extent. And because the methods have come
to constrain not only the types of answers that we get, but also the
types of questions that we formulate, and the ways in which we \[skip\]
thinking is incredibly valuable, and potentially very liberating to the
field, even if the models we come up with initially are very simple, and
actually very \[skip\] type of thinking. I think that will be useful.
And it may even be the case that experimenting with these models or with
these new approaches will allow us to pose new questions \[skip\]. That
may include experiments, that may include some of the traditional
observational methods, which by no means \-- I don't think we should
throw out the window at all.

And \[skip\] generally; so, a combination of methods. The second point
I'd like to make is, I think potentially another important contribution
of complex systems thinking to population \[skip\] David's comments
yesterday to try to develop a theory of this thing that we call
population health. And this \[skip\] biological, the individual, the
population, the individual, the group. I think people have been trying
to do that for a while, and I think we've accomplished it to some
\[skip\] although we haven't articulated it in as precise a way, with a
precise language and with the rigor that perhaps learning more about
this would allow us to do. And I think they could \[skip\] realize that
we don't really know what we mean. And I think it'll challenge us to
overcome some rather simplistic and vague thinking \[skip\] like the
causes of population health are different from the causes of individual
health which are common, I think, in a lot of the epidemiologic and
population health literature, \[skip\] clear enough terms to be able to
try to understand exactly what it is that we're saying.

So I think this complex systems thinking may help us do that. \[Skip\]
two more philosophical points; the third point, which is a little more
practical, is that I think \-- you know, we really need to think about
what specific, what specific population health \[skip\]. And I think
here we need to distinguish complicated from complex in the sense in
which we've been talking about it at this meeting, because everything is
complicated. And, you know, for complicated stuff we need a whole bunch
of different approaches \[skip\] here, which is a much more specific
definition. And I think we need to \-- in order to identify the types
\-- in \[skip\]. One option is to identify the fundamental types of
questions or the features of questions that complex systems approaches
may be \[skip\]. \[Skip\] very clear from infectious diseases, because
infectious diseases arise from people interacting with each other
because they get stuff from each other.

And you've seen this in many examples; virtually all of the \[skip\]
it's also, you know, pretty straightforward for things \-- for other
things which may involve contagion-like behaviors, although, you know,
\[skip\]. In any case, there are analogies. But what are some other
fundamental, you know, types of processes suitable for these approaches?
And clearly, you know, one obvious one are process \[skip\] there are
plenty. And, you know, other examples include situations where networks
or contact structures or interactions between individuals are \[skip\]
processes operating at multiple levels or scales. So, try to identify
specific problems which involve these types of things, but specific,
very specific to population health questions \[skip\]. \[Skip\] what new
insights we can learn applying these approaches to these particular
questions and contrast those to what we would get if we use a standard
approach, and see what the difference is, and show what the new
\[skip\]; I think that's, you know, that's really what we need to do if
we want to incorporate this.

And here I just want to emphasize the challenges of moving from
infectious diseases to chronic diseases; it's not going to be simple.
Chronic diseases are much more \[skip\] first of all are
multi-factorial, infectious diseases are also multi-factorial, but at
least there's that agent, that you have to have that agent plus other
stuff. In chronic diseases, you might be able to \[skip\] very much. The
lag times are very different for both types of diseases, although there
are infectious diseases with very long incubation \[skip\] to
infections, and therefore, you know, you can \-- things that we've
learned about applying these methods to infectious diseases can be
transferable to behaviors. Here is our \[skip\] and distant, and so
we're not going to get that much \-- well you know, I think it's
important to look at behaviors, but it's not going to be the whole
answer. \[Skip\] last point I think that I want to make is that although
we're \-- you know, we're interested as epidemiologists in understanding
processes and sort of the intellectual challenges \[skip\].

And I think this is a strength of epidemiology which we sometimes
forget, you know, sort of in our self-flagellation about how terrible
\[skip\]. The strength that we have is that \-- in that we are very
grounded in data, in empirical data, much more than many other
disciplines, because that's our purpose is to apply our knowledge to the
real world. \[Skip\] don't become just clever tools or clever games that
we can show to people, but really help us understand reality and act on
it. Thanks.

Male Speaker:

I'm also an epidemiologist and come at this experiment from a population
health perspective, and \[skip\] I knew I was going to start after Ana
and I suspected she would make some of these points lightly and
\[skip\].

\[laughter\]

It's sort of a \-- here are my three points. My first point is during
this meeting, which I have found immensely stimulating, I'm terrible
\[skip\] so I really, the fact that it held me was great, is why are we
here? And we articulated this meeting sort of as complex systems
approaches to population health, which is if you think \[skip\] achieve
that title, because what we're trying to do, of course, is we're trying
to forge a meeting out of two very distinct traditions. Now, both
traditions \[skip\]. Be that as it may, it emerged, I thought quite
clearly, that there are two very different traditions, and their
difference is rooted in a word \-- they're traditions \[skip\] a lot on
abstraction and elegant theoretic formulation, and the other one, which
is sort of the one that I dwell in, rests much more on substantive and
pragmatic and \[skip\] where they're at, but in fact, of course, both
have strength in terms of where they're at.

But I think it's important that we recognize it if we're going
articulate complex systems approaches to population health sciences we
really \[skip\] additions of abstraction and pragmatism that really are
strange bedfellows. And there were a couple of talks which influenced me
a lot, which sort of tried to bridge this gap, but if you think
\[skip\]. He actually sort of did the first half of his talk on
abstractionism and the second half of his talk on pragmatism rather than
sort of truly seeing \[unintelligible\] integrating them. So, clearly,
although I don't \[skip\] there are two \[unintelligible\] traditions
that we are trying to bring together and it's a very, very hard thing to
do, and although I think there were \-- that this room is filled with
very smart people, it's not my impression that we're achieving \[skip\].
What was the message that emerged from this; what did I get from this?

Well, I thought Stephen Marcus actually captured nicely the essence of
what I got from this, and that was the fact that there are two \[skip\]
complex systems methods and there's obviously argument about what the
methods are and what they're called and whether \-- what emergence is,
and what a complex system is, and that's all fine and good for graduate
classes \[skip\]. It's very clear that we need movement \-- just
wondering if Carl was frowning when I said something \[laughs\] \-- I
think it's very clear that we need to advance on \[skip\]. \[Skip\]
quite nicely said that part of what this complex systems thinking should
do for us is encourage us to think more creatively and to formulate our
theories more rigorously and \[skip\] to do and I think some of us in
our comments and \[unintelligible\] have complained, but I think that
the two are very distinct and some of the clearest, in my mind, historic
examples of complex systems thinking of course use nothing that today
\[skip\] methods.

Again, I thought Scott de Marchi made a very nice point; at least, I
think that is what he was trying to say, that complex systems methods
can become a constraint, and if they become a constraint \[skip\]. In
the context of this meeting we've bemoaned a lot of our traditional
descriptive, simple, linear regression approach and bemoaned how much
that's a constraint on us, but I think all methods fundamentally can
become a constraint \[skip\]. My last point is sort of focused on next
steps. Well, I thought the point of this meeting was to disturb the
comfortable equalibrium that we're in in population health science
\[skip\].

I think moving forward is going to require a not insignificant amount of
cleverness. I think there are a lot of clever people in this room
\[skip\] but I don't think this is going to be easy, and this is sort of
where I circle back to where I started from; it certainly was my sense
that the two \[skip\] which I thought laid out a very compelling view of
the set of questions that we should answer, and my estimation is \--
from what I've heard today \-- is that we're \[skip\] and that's not to
\-- for lack of effort or for lack of creativity, so clearly I think we
need discontinuous leaps of cleverness to incorporate complex systems
thinking with methods \[skip\] fundamentally all this is about to answer
your questions of interest, and I think that the mechanics of how we do
that \-- we can all envision those in terms of involving students across
disciplines and all that, and those are all sort of mechanics we can all
think about, but I think \[skip\] we need to \-- both are sides of the
tradition, of the fences of this tradition, shake what we do. And if we
truly think that this is a way forward, we need to sort of shake the
comfortable equilibrium of what we do and \[skip\].

Female Speaker:

Well, I'll be really brief because I see \-- I think that Professor
Simon has here at least a page \[skip\] many things have already been
said. I have one point, which essentially reiterates something that
David said even yesterday about \-- I think one of the big challenges
\[skip\], but the integration \-- better integration of different
organizational levels needs to happen, but also in view of asking how
relevant it is, not just to do it. I'm slightly concerned with the
terminology \[skip\] to me we have been doing this in ecology for a long
time and it's different from complex systems. And it seems to me that it
could be sometimes we label, just as reduction is at a new level, which
is just \[skip\] that was not complexity. So it seems like suddenly
molecular biology has discovered that you needed more than one
regulatory.

Now, what I mean by that is that it would be wonderful if we could sort
of do things \[skip\], well, how much do we need to integrate within
host and between host dynamics; that's just one example. Perhaps, you
know, evolutionary and population level issues; that's another example.
And at even \[skip\] economics behavior, and I think there is a great
opportunity to merge that with what we see as population dynamics of
disease. There is a bridge there that we use these types of techniques,
but needs to \[skip\]. And the final point is about data. You talk about
new technologies. I think that one of the greatest limitations, at least
in my field, is data, but not even new data.

\[Skip\] prediction, it would then weather prediction, for example. And
we are doing it with a number of time series I can count on my hands.
And this is for problems like malaria that are killing millions of
people \[skip\] some people have them. There are very few that are not
even good data sets. We don't even have these historical records, and
this is not even with new data. I think one of the challenges is sort of
continuing these surveillance programs \[skip\] so I agree with you the
data is terribly important, but also to sort of have ways to share it
and to develop something that \-- I think when the climate people have
\[skip\] much more data collection than we do to solve these difficult
problems. So, those are some of my points.

Male Speaker:

Thank you.

Female Speaker:

You have two hours.

Male Speaker:

Okay. You have five minutes.

\[laughter\]

Male Speaker:

\[Skip\] begin by agreeing with Anna and Sandra on one point and maybe
disagreeing on another. Mike \[unintelligible\] might frown. First of
all, if we've learned anything, we certainly did learn the difference
between complicated and \[skip\] and I'll bet half of us didn't quite
understand that difference, and I think hopefully it became sort of
clear. I like the Maurice Ravel quote that Ary \[skip\] agree.

\[laughter\].

The part I \-- I'm not sure about this dichotomy between abstract and
pragmatic or data organization and data driven. In fact, that scares me.
I hope that's not true \[skip\] data, but it's also critical to begin to
have a theory to organize it, to learn from it. I'm thinking of the work
Jim Coltman and I did on estimating the \[skip\] pragmatic, but if you
just use data, say, from in San Francisco, you know, you have great data
on disease and people answered every \[skip\] from or when they got it.

And so, you know, hanging it together with a theory and merging the two
just seems crucial, or Mercedes' work on the effect of El Nino \[skip\]
all these data, but putting \-- finding the hanger to put it on and sort
through to see the difference or \-- or Mercedes and Katya's work on
predicting influenza. We have incredible data about influenza \[skip\].
You know, how the data \-- what the patterns are. And how the micro and
the macro interact. Boy, I think there's \-- I think that's powerful
stuff that needs \[skip\] data analysis \-- you know, and theory.

I do agree very much with Sandro about \-- you know, there were probably
no talks directly on complex systems and population health. \[Skip\]
were locked in a room for a few days, maybe we'd throw in Jim and Diane
and a few others to round out. You know, some modelers, some data
people, data-driven; others who \[skip\] involved and people who are
more concerned about modeling. What could we take from this meeting to
help us? I think there was a lot. So for example, the social science
paradigms, micro \[skip\] transmission, the infection trans \-- you
know, contagiousness, infection, and behaviors and thought processes.
And then the networks that Mark Newman talked about would be a key piece
about how we'd study \[skip\] need to know who's connected to whom. And
actually, the talk I thought would least fit maybe fit the most, and
that's Jasmina's talk on economics. That game that she showed, you know,
\[skip\] capture, you know, the decisions one makes even implicitly
about what to eat and, you know, how \-- what behaviors would run your
life. I think that would be a great one \[skip\].

\[Skip\] those and we put in, so Axtell and Epstein told us about
agent-based modeling and de Marchi told us about computational social
science. And Kristin and David had compartmental \[skip\] data and put
it together. And then Dave Krakauer and Jim Koopman and Ary Goldberger
talked about sort of the philosophy that you'd think about as you
\[skip\] from \-- I think we've had a successful session just by \-- you
know, if we knew nothing but what we learned during the last couple
days. But I did miss the \-- you know, the talk that \[skip\] and that,
you know, we're beginning to have a day tomorrow, going to show us
systems models of obesity and diet. You know, it would've been great to
have that here because it's sort of would tie together, to see the
beginnings of it \[skip\]. \[Skip\] population health and the things we
think about meet the criteria for complex systems so completely; it's
such a magnificent match. So, what's next? You know, maybe locking some
of us together and \[skip\].

Female Speaker:

\[low audio\]

Male Speaker:

I \-- this does \-- maybe on an island, but yes, we're off to the \--
I'm off to the Galapagos in a few \-- in a couple weeks, so what I want
to say is the proof is in the pudding \[skip\] I'm really \-- I'm going
to close. A lot of us put a lot of work in. The totality of all the work
we've put in probably didn't come close to matching what George put in.
By the way, George designed that \[skip\].

Female Speaker:

What's the picture?

Male Speaker:

It's abstract, about all the things we care about.

\[laughter\]

If you understand that poster, you'll understand population health. And
I'm very proud, again, at the \[skip\] because the Population Health
Institute group that \-- the center, and the Center for Complex Systems
are somewhat unique, and \-- but we'd be glad for this meeting to be
transmitted around the world.

Male Speaker:

Well, thank you, that \[skip\] comments from anybody in the audience
who'd like to say something either mundane or profound?

Male Speaker:

No profound.

Male Speaker:

\[laughs\] Go ahead. Yeah, go ahead \[skip\].

\[low audio\]

Male Speaker:

\-- and there's lots of skills \[inaudible\] and so I recommend that
instead \[inaudible\] \[skip\]. I recommend that you look at the
Professional Science Masters degree programs \[inaudible\] \[skip\]
programs are easy to learn about; just go to
[www.sciencemasters.com](http://www.sciencemasters.com/).

\[end of audio skipping\]

A hundred and eight programs, masters degree programs, \[inaudible\] are
listed there, \[inaudible\] a broad range of sciences. And they include
about 15 programs in applied mathematics, financial mathematics,
industrial mathematics, \[inaudible\] and such, and these are programs
that train young people in becoming project-oriented modelers.
Leadership will have to come from your profession \[inaudible\], but the
nuts and bolts of modeling and getting that work done is best done by
modelers, and that's an excellent training ground from which to recruit.

Male Speaker:

Thank you.

Male Speaker:

\[inaudible\] particular kinds of programs \[inaudible\] Foundation
\[inaudible\] Professional Science Management \[inaudible\].

Male Speaker:

Thank you. Just keep the questions or comments short, so we can get as
many in as possible, thanks.

Barbara Cremgald \[spelled phonetically\]:

Okay. Barbara Cremgald. I wanted to thank Ary Goldberger for taking
complex systems thinking from the realms of science and economics to
music and art, and I had wanted to ask him, but I'd just like to ask
people here, when they get to work on designing a theory of health, to
keep that in mind, the sort of mysteries of art and music that we're
capable of, as well as sort of rules and systems from science and econ.

Male Speaker:

When David Krakauer came, I meant to ask him to give a talk on complex
systems \[unintelligible\] he has just done that at the Georgia O'Keefe
museum in Santa Fe, so where he led sort of a docent tour, sort of
connecting a lot of the things we've talked about. But there are natural
connections.

Male Speaker:

Okay, go ahead.

Male Speaker:

I'm \[unintelligible\] from the University of Massachusetts. I'm a bio
studies student, and I work on \[unintelligible\] data, so in our field,
well, we're most concerned with the quality of the data, and how did
\[inaudible\] connected, supporting the statistical tests in systems
modeling approaches. So, could you provide some guidance in terms of how
should we design a study to connect the data to support more
\[unintelligible\] the system models?

Male Speaker:

Thank you.

Male Speaker:

Well, I mean, that's sort of, I think, the whole thrust of this meeting,
was to give examples and to think about it, that's \-- so if I
understood the question, it was some guidance on taking systems models,
analyze data in special areas, but, you know, that's \-- it's not an
algorithm; it's sort of an art, a skill, a way of thinking about things,
and, I mean, one of the reasons that \-- to advance the topics we've
talked about here, I think, will take quite a while, because of the art
and skill piece. So I don't think there's a nice, short answer. It's
about, you know \-- it's taken me 40 years to learn the little I've
learned, and hopefully maybe they will \-- so, one example might be the
workshop tomorrow, that Stephen Marcus mentioned on systems thinking in
health.

Female Speaker:

I was glad to see the transition in one of the final presentations that
talked about the implications of modeling for public policy. One of the
things that we've been tackling is this recent phenomenon, over the last
15 years, of institutional abuse of teenagers in a new form of
residential treatment across the country, and despite the fact that
there's been some proposed federal legislation for this issue, it hasn't
yet passed, and one of the things we've recently developed, because I
think we need to start thinking not only about public health and
research differently from a complexity perspective, but also about what
this then means for policy, and I think it means that we don't
necessarily have the privilege of presuming that some of us can clearly
understand the phenomenon and design policy interventions and then get
stakeholder buy-ins so that we can roll them out with fidelity.

I think that it's really about a co-creative policy, and we've developed
an online method \-- developing an online methodology called Wikipolicy,
where people who've actually been experiencing a given phenomenon that
seems like it might benefit from a policy can share their perspectives
with each other, and can comment specifically on proposed policies, so
that we can truly co-create with the very folks who would be living in
the systems that the policies would be affecting. So \--

Male Speaker:

Thanks.

Female Speaker:

\-- I think that this is an important thing to keep in mind in addition
to the implications for research.

Male Speaker:

Okay, thank you. Let's just do a very quick \-- because we want a couple
minutes to wrap up at the end, so we'll do our best to \--

Diane Feingold \[spelled phonetically\]:

I'll be really quick. Diane Feingold, I \-- Carl, I'm not sure how I
feel about being locked in a room with you, but on the other hand, I am
\-- I do think this was a very good start. I've worked in multiple
areas, or across disciplines for a while, and as a first step, you know,
it was a good shot, and we weren't really always talking to each other;
sometimes we were talking past each other, but that's a good first step
to get to know each other and recognize each other and hand each other
cards and stuff like that. So I look forward to the next opportunity to
do this, where we can now begin to maybe put a solutions orientation to
the discussion. We can learn to talk to each other better, so that we're
not passing each other as ships in the night, and do that, and
certainly, I'd be happy to help in any way that I can to make that
happen.

Male Speaker:

Thanks.

Male Speaker:

One question, Diane, before you go. Do you think \-- a comment on my
statement, if you would, that I think \-- if we took only what we
learned at this meeting, it would be a good basis for such a working
group.

Diane Feingold:

Oh, absolutely. Absolutely. There are some missing \-- there are some
gaps, some definite gaps in topics that weren't talked about at all, and
I don't mean obesity, or \-- you know, but topics like organizational
\-- complex systems in organizations and how that relates to public
health and a few others, so I think there's a few other people we might
want to draw into the tent in that discussion.

Male Speaker:

Okay, thank you. Apparently there's a bus that's leaving at 5:30, so we
really have to \-- or close to 5:30, so let's do a quick wrap up, and
then George wants to have the last word.

Male Speaker:

So, I think we should take a multi-pronged step forward. I think we need
to have people who are really concentrating on advancing the area of
science; people who are really concentrating on it, the areas of policy,
and people who are really concentrating on advancing the methods. But if
you have those three separate, they don't work. We need a center for the
study of complex systems.

Male Speaker:

Thanks. I agree with that.

\[laughter\]

Let me just take a quick opportunity to thank \--

Male Speaker:

Thanks to your mouth \[inaudible\] later.

Male Speaker:

Let me thank everybody as well, both the audience that have been
wonderful at contributing to the ideas here, as well as the organizers,
particularly George and the group who put this together; that was a
lesson in complexity. And I think the first step in trans-disciplinary
integration is just learning from each other what the paradigms and
languages of the other groups are, and you can't really expect to go
much beyond what we learned here today. The next steps would be, now
that we have a basic lay of the land, what can we do to integrate and
synthesize the paradigms even better in the future? So I just want to
thank George and the organizers for a wonderful conference, and for
great presentations. Thank you.

Male Speaker:

Thank you, David.

\[applause\]

Male Speaker:

David?

David Krakauer:

I have only a few things to say, some of which you need to know, and the
rest will be short. For those of you who are staying \-- first is
housekeeping stuff \-- for those of you who are staying at the Marriott,
there will be a University of Michigan bus waiting outside to take you
back to the hotel for the evening. For those of you who are staying at
the Marriott but want to hang out in town for a while, there will be a
bus \-- there will be a continuous shuttle that will loop from the hotel
to the Michigan Theater, from 6:00 to 9:30 pm. How's that for service?
And if you don't know where the Michigan Theater is or you don't know
where restaurants are, etcetera, you'll find maps out on the tables
outside. For those staying on to attend the workshops tomorrow, if you
don't know where the workshop is located, please ask the staff out at
the table, and they'll tell you.

There will be two drop-off locations in the morning for a bus coming
from the hotel; one at the School of Public Health and one at the Center
for Social Epidemiology and Population Health. And also, you're asked to
wear your name badge to the workshops. Now I want to just take \--
really, just two minutes \-- first to thank the speakers, who I think
were extraordinary, to thank the audience \-- those of you who took time
in your busy schedules to come and participate in this; you are as much
a part of this as those who spoke, because you will be some of the
agents that help to propagate this information. And I want to thank my
colleagues, both in the Center for Social Epidemiology and Population
Health, and The Center for the Study of Complex Systems. This truly was
a joint effort. Anyone who referred to it as "George's meeting" is
absolutely wrong.

This was a joint effort; it was jointly conceived and jointly planned,
and it would \-- believe me, it really benefited from the multiple
heads, perspectives and energy of everybody that was involved. I also
want to thank our sponsors for \-- you know, to whom we can truly say,
"Without you, this would not have happened." And that includes the
National Institute for Child Health and Development, the National Cancer
Institute, the Robert Wood Johnson Foundation, the Office of Behavioral
and Social Science Research at NIH. And finally, thank the University of
Michigan, who has supported rather nicely the Center for The Study of
Complex Systems and the Center for Social Epidemiology and Population
Health. They're investments that we hope pay off, and I think that this
meeting is definitely one of the payoffs.

Finally, I just want to indicate that I think there are several things
that need to happen. One is, we need to learn. That's a two-way street.
Those who are doing complex systems work need \-- if they're interested;
this is all about voting with your feet \-- to learn about these
problems of population health that drive so many of us; those of us who
are driven by those need to learn more about complex systems. There are
techniques, there are languages, there are tools, and there are cultures
that we all need to learn \-- make a commitment to learn. There's mixing
that has to go. Without this room or some analog of it, I don't know
that we need to be locked into a room. I don't think we can get past
that \-- past the IRB, but \--

\[laughter\]

\-- but there's no question that propinquity breeds a lot of sharing. If
we don't occupy the same place, it's hard to do that. We can do that
electronically, but frankly \-- maybe this is just because of my age \--
there's really no substitute from sitting across the table, or \-- and
that table can be a dinner table as well as a conference table. We need
to do; we need to come together, to actually do this kind of
integration, and without \-- obviously, that is going to take building
funding sources for this kind of work. There are some indications that
that's starting to happen, but we have to be agents of change in the
sense of convincing study sections and foundations, etcetera, that this
is something that not only is a good thing to do, but something that has
to be done.

And finally, I think we need to translate. We need to be able to build a
science that integrates Population Health and complexity science in a
way that makes it useful, that makes it intellectually interesting, and
that makes it \-- and gives it the ability to truly have an impact on
improving peoples' lives. That is, indeed, what we're all about. There
is a race going on now to see who can build the tallest building in the
world. The current one is going to be somewhere in the Middle East \-- I
forget where, maybe Dubai; I'm not sure \-- and it's going to be 800 or
1,000 meters high, you know, 300 stories, 250 stories high.

What we need to do is take the effort that's going into that building
and turn it on its side and build bridges which are as long and as wide
and as deep; bridges between all of these disciplines that we represent,
and bridges that allow us to walk in all directions, back and forth, and
with a common destination in mind, and I think if we can do that or come
close, we have a chance at actually building a new approach to
population health which relies heavily on complex systems. So I thank
you all for coming. It's been very rewarding. Carl, do you want to say
something, too?

Carl Simon:

Yeah. As they say at the end of Casablanca, "This could be the beginning
of a beautiful friendship."

David Krakauer:

Right.

\[laughter\]

\[applause\]

\[end of transcript\]