Our guest spot
is Professor David Krakauer. David is a
theoretical biologist and he directs the Wisconsin Institute for
Discovery. He’s also professor of genetics at the University of Wisconsin
and external professor at the Santa Fe Institute.
Before moving to Wisconsin, he was on the resident faculty of SFI and
served as chair of the faculty. So, welcome David.
I’m very pleased to be with you. Great. Earlier
in the class we talked about emergence -- the concept of emergence and the notion
that you could have these emergent properties that have to be
explained at the system level instead of the individual level and
some people have said that emergence is kind of a subjective notion
that maybe it’s only emergent because we’re not smart enough
to understand it at the individual level. Do you think
that’s true? Or is there something fundamental about emergence?
I have to confess I struggle with this concept, and I
am sort of on the fence. Part of me thinks
that remark is true, that you could
always, if you had infinite computational capacity, describe
a system in terms of its most elementary constituents
and that the reason why we look at what we call effective variables
or aggregations of the data is because it’s easier.
So thermodynamics, for example, heat, temperature,
entropy, and so forth. But another part of me says
that that’s insufficient. That if you think
about sophisticated intellectual capabilities
like reasoning about music
it’s not obvious to me that a microscopic description of the dynamics
of the proteins in our neurons will ever
sufficiently capture what we mean by an appreciation of music, so
I have to say I struggle. I don’t have a good answer to that.
Let’s see -- so I asked
you to define complex system, a different
version of that question is to talk about definitions of complexity
itself. Do you think that’s been
hard for people when you ask what is complexity, people often
give very different answers. Do you think there’s a notion that
there’s a single definition of complexity
that we might find that might be useful across systems, or is it really dependent
on the particular system itself?
So I’m quite optimistic about this. I think it that has to
do with the level of predictive accuracy you’re going to demand.
So I think there is a sense in which there could be very general definitions
along the lines that people like Murray Gell-Mann
and Seth Lloyd, and many others, have suggested having to do
with description length, and so forth, in other words
how many pages of algorithms or mathematical
equations are required to capture the variability you care about?
The problem with those measures is they don’t tell you a great deal
about the mechanisms that in some sense are the ones you want to
intervene into or control or understand.
So I think this is where the difficulties arise, because at that point,
these complexity measures start bifurcation and
proliferating and I think that’s okay, actually.
I think that as long as we’re careful and
any one of us states our operational definition
we state clearly our assumptions, our axioms,
I think it’s acceptable for the definitions
to not necessarily be unified. But
I suspect, by the way, if you were to look carefully
all of those slightly different definitions would all have a little bit of the same
flavor, that they would be capturing
something about the
number of components
required to effectively predict and control the environment
I think that’s probably a common denominator, even if on the surface they
appear very different. Okay, maybe
you could tell us a little bit about what you’re working on these days.
What you’re excited about. Yeah, so my very modest
research program is to
in one sense understand the evolution of
you can put it two ways -- the grand way of saying it is
the evolution of intelligence on our planet, in the solar system
and the universe. Which sounds
at one level as ambiguous as the evolution of complexity, right? It’s substituting one ambiguous
term for another. Another way to put it, slightly more modestly,
but not much more, is the evolution of mechanisms
of information processing.
A slight slight of hand, because it’s not really that much clearer
than the first, but people seem to find it more acceptable.
And so, for example,
simple organisms, single-celled organisms,
really can respond to the environments in which they live. They can
pursue food sources, they can travel along gradients
they can aggregate into collectives, and then in the multicellular
state, those cells take on differentiated roles.
Cells that just do metabolism, or cells
that just sense their environment, and so forth, or structural.
And so what I’m interested in is why that
happens, I mean, it’s a funny thing, we know more about
the universe that the smallest scales and the largest scales
than we do than at the scale at which we live. So we understand
extraordinary depth, essentially the origin of our solar system, the origin
of all the elements, the periodic table, you know we understand
nuclear fusion, but we don’t really understand
an ant, and that’s because
it’s a complex system, in other words it doesn’t yield to these
elegant mathematical formalisms. And the question is, how
did a complex system emerge out of a simple system? So that's
in some sense the big question. How did life, some people would say,
emerge from non-life? And
that’s what I do, and I do it in the context, I guess, of
natural science, I’m not a mathematician, I’m not a computer scientist,
I use tools from those fields, and I apply them to natural phenomena, for which we
have reasonably good data, and increasingly better data.
Can you give an example?
Yes, of course. So for example, over the course of the last few decades,
the extraordinary increase in the number of complete genomes
we have from a very large range of organisms. It used to be that we
only had that data for one or two, because it was so expensive to
collect. And now it’s not so if you’re asking questions about
evolution you clearly need data from very diverse
species, not all from one group, not all from humans or mice
or yeast, or flies, and
so that’s made a big difference, so we can now ask about patterns
of change, infer patterns of change from
very very diverse tips of the tree of life, and I think that’s made
a big difference, and along with that data is data
about things like, all of the proteins we find on nerve cells
so we can ask questions about changes in the way nerve cells
express genes across diverse lineages, and when
so that’s the kind of data, I think largely genetic,
that’s been made available via these high frequent mechanisms
that I think really complements this sort of more basic theoretical
research program. So just a simple question
when you look at these genomes and
look at changes in the genomes and so on, how
does information processing come in? You mentioned that as fundamental -- That’s a very good question.
In fact, that is the great challenge. So
what you have to do, yes, is commit to
a model of computation and then ask to what extent
the elements that you observe fulfill
the requirements of that model, so let me give you an example, a simple example.
After the discovery of
the structure of DNA, there was a lot of interest in how genes are turned on
and off, and this is often, this work is often associated with Jacob and Monod
who are interested in what is now called gene regulation
how genes turn on and off at different times. We can take one organism that is living
like an E. coli bacterium, and we can study in great detail
how these genes are turned on and off, so we know
what the structure to function mapping is, because we have a living species.
And now we can go back and look at a whole range of
genomes, for which we do not have laboratory experiments
generalize and say, since they share those structures,
it’s probably fair to assume that they have similar mechanisms, so that’s what we do
we try to write down simple mathematical models for systems
that we know, and if we find analogous structure
in species for which we do not have the capability to
perform experiments, ask whether the mathematical models
have all the necessary ingredients, and if not, try to make some
conjectures about what’s missing or what they've added.
Okay, interesting. Let me ask one last question, which is
you’ve talked about all these different areas, like information processing
computation, mathematics, dynamics
and so on. So if someone wanted to become a complex
systems scientist, how do they master all of these different fields?
How can you do it? Yeah, that is a good question.
I’m not sure that we do.
Well, so the first answer is that we don't.
Collaboration is key. And in fact that’s one of the
really fun things about working in this field, because it really requires
deep collaborations, and that’s
part of what makes it so much fun.
The other thing, of course, is just to
throughout your career, not to lose track of other fields. I think
that the human brain is remarkably flexible, and remarkably
capacious and the way
that our universities are structured sort of denies that fact, and says
you have to specialize because you can’t make a contribution unless you
only know about this group of proteins, and
I defy that convention. I think that
it’s not true. I think that people
when interested can learn huge amounts, and there’s really an issue of time
and structure, and I think part of the
huge benefits of your course, of something like this
making this information available online, is that people will have access to
this information. So I think
as a director of an institute, I go out of my way to
try to promote seminar series, and courses, that have this kind of
transdisciplinary quality. I think we have
to find ways of rewarding people for pursuing this kind of work. I think
we have to tenure people who work across disciplines. Yeah, that’s the hard
challenge. There are some significant challenges ahead, but
they’re just practical challenges, they’re not logical impossibilities.
Well great. Thank you so much. This has been great.
And I hope you don’t freeze too much in Wisconsin.
I won’t. I have many, many layers.
Alright. Thanks a lot. Thank you.