Hi, class.
Welcome back to modeling complex system.
I've said it before, what I like about modeling,
and I called it a romantic tool,
is that it allows you to do science
in a different way, right?
I think I said you can do it by the fire on a beach
of Lake Champlain or walking down a railroad track.
Doesn't matter where you are,
the lab benches are in your head,
and you can follow your curiosity,
and answer almost any questions
or try to answer almost any questions that you care about,
that you're interested in and follow your own curiosity.
So what I'd like to do in this video
is just give you a few examples of the kind of systems
we're gonna be looking at,
and what are the keywords you should be thinking about
when wondering what systems you can try
and study with the modeling tools we're gonna learn.
So the first keywords I'll use is collective.
And I'm using it as a first example,
because it's behind, this idea of collective behavior
is behind probably the most classic example
of complex systems.
(birds whooshing)
The sound of the birds, it felt...
So this collective behavior is amazing
and it's (indistinct) incredible that (indistinct)
emerge from birds (indistinct) individually,
(indistinct), right?
So it begs the question,
do we need to threshold with self-awareness or intelligent?
(birds whooshing drowns out speaker)
(birds whooshing)
(birds whooshing drowns out speaker)
(birds chirping)
(birds whooshing)
This was louder than I remembered
and I've used the word before.
But as you model these things,
you put a lot of these birds together,
the behavior you tend to care about is emergent, right?
It's something that is not forced top down by a leader
or by the modeler within the rules of the system.
But that emerges because of the many interacting parts,
interacting parts.
The example I'll use for this, I muted this video
because there's some pretty unsavory language being used,
but quickly what's going on here is that the band,
Lamb Of God is on stage on the left side of the screen
and the singer is using some colorful language
to ask the audience to start a circle pit.
And what a circle pit is essentially just
members of the audience running in circles, jumping around.
It's a lot of fun.
But then the singer is not telling them where to do it,
which direction to turn
and it's really through this collective behavior
is that a direction emerge, right?
So you have one circle that you're probably looking at,
in the middle, there's one in the far right,
one in the bottom right of the screen.
They don't have to go in the same direction.
These clusters can merge.
It's almost like fluid dynamics.
But here, it's mostly a human example
of what we saw in the birds just now, right?
So co-evolution is one of the theme of most of my research.
In the previous example, it could have been co-evolution
of different groups, different circle pits.
Often what I care about, what's fascinating to me
is the co-evolution of a dynamics on a system.
If you want, so the idea of a pandemic,
let's say, in a human society with the dynamics
of the system, which can be like because of a pandemic,
there's social distancing, physical distancing going on,
there's the creation of new institutions, right?
So the society is co-evolving with this pandemic
and this interaction of structure and dynamics
is something that I find fascinating.
I chose this random video.
The reference is there, I don't know where it's from.
I picked it because it's beautiful
and it claims to show the growth of a fungus and a bacteria.
I don't know if that's true. That doesn't really matter.
I just love the fact that often when you grow bacteria
or whatever it is,
you tend to get these beautifully complex structures.
Like doesn't this almost look like a city, right?
And then it begs the question of how, again,
something that locally is so simple,
cells and bacteria can grow in these complex ways,
can learn to trade within a colonies.
And those are just beautiful
or interesting questions in my mind.
And at the end of the video here, we saw, you know,
the structure start to decay.
And it brings us to this idea of instabilities.
A lot of the systems we're gonna care about
have multiple stable states.
Going back to the COVID-19 pandemic,
you could take the same novel Coronavirus,
the same virus behind COVID-19, put it in the same person,
in the same place at the same time.
And just because of random noise or you know,
or sensitivity to initial conditions, chaos.
9 out of 10 times would probably be okay now.
There's multiple different steady states
or outcomes that we can get
and it's interesting to know how likely one is
versus the other.
And when do you push the system beyond this unstable point
and towards a different state?
And I studied that a lot in a system
that I care a lot about, which is the Amazon rainforest.
So the Amazon rainforest exists
right next to savannah, right?
Dry grassland, and it's not just because of rainfalls,
essentially is that the rainforest
and these dry grasslands
are two stable states of the same system.
And once you understand that
from a dynamical system perspective
or a modeling perspective, it begs the question,
how much damage can this beautiful system,
the Amazon rainforest take before it collapses
to savannah dry grassland?
And as climate change increases,
as deforestation increase, as forest fire increase,
it is important to be able without burning the forest
to try and answer these questions,
how much damage can it take?
And modeling can help us do exactly that.
So it goes back to this idea now of feedback
that as we do damage either through deforestation
or forest fires, we reduce the forest area
and therefore, reduce its ability to grow back.
So there's what we call a positive feedback loop
in the loss of forest.
But in terms of, you know, biodiversity,
it's just a terrible, terrible negative things.
And this feedback show up in a lot of this of systems.
There's a beautiful example in the next video.
I'm just gonna shut up and let it play
because I love the sound of ice and snow.
(ice rumbling)
[Narrator 1] The only way that you can really try
to put it into scale with human reference is
if you imagine Manhattan, and all of a sudden,
all of those buildings just start to rumble and quake
and peel off and just fall over and fall over
and roll around.
This whole massive city just breaking apart
in front of your eyes.
(ice rumbling)
We're just observers.
These two little dots on the side of the mountain.
And we watched and recorded the largest witness
calving event ever caught on tape.
So this calving event that they're talking about
is essentially just separation of the glacier.
It's a beautiful, beautiful event.
But similar feedbacks happen when you just look
at an iceberg melting or whatever it is,
there's a loss of snow cover
and you get less reflection of sun rays
and an increase in climate change.
And then you have these events,
like this incredible calving event was 75 minutes
or so, right?
But that interacts with climate change,
which occurs on a much longer time scale.
And so, you have different systems, some local, global
or systems of different nature that are interdependent.
And the video that I use to illustrate interdependence
or interconnectedness of systems is a video of bats
in Sierra Leone.
So a lot of my work deals with the Ebola outbreak
on 2014 to 2016 in Western Africa.
I chose this video to talk about it in part
because the video shows that Sierra Leone
is a beautiful country.
And also, it shows, you know, this behavior of bats
and it's an interesting system
because as as these countries grow
and push back against the forest for economic growth
and urban development,
you increase the frequency of context
between the human population and the bats population.
And here, the bats are the vector for a lot of diseases,
including Ebola.
So economic growth is increasing this risk
of Ebola coming back in the human population.
And anytime there's an Ebola outbreak,
it comes as great cost of human lives
and economic development.
In economic modeling, that's a classic poverty trap.
The idea that you're either sick and poor
or rich and healthy.
It's a cartoon but it helps us think about
how positive things like economic development
can have terrible outcomes
because of the interdependence of systems,
in this case through animals.
So the last example of behavior that I have
is just this idea that
a lot of complex systems are adaptive.
The parts of complex systems is adaptive.
The models we're gonna develop are not easy to control.
If you try and intervene, the parts,
the elements of your system might change their behavior.
And I chose a ridiculous video to show that.
So this is a video of a man, a lawyer in Florida
dressed as the grim reaper protesting the reopening
of beaches in Florida.
And I chose the video because I love most protests,
but also because it was a good representation
of behavior that I'm really fascinated with right now.
This idea that if you have different states,
different counties, different towns
with different regulation,
so one closes their beach, the other doesn't.
Well then, resident of the town that did close their beach
can move over to the other town,
and then you end up with even more people than you expected
on the beach, right?
So this is a negative consequence of your control measure
brought on by the adaptiveness of the system.
It's a classic, you know, unexpected
or unintended consequence in public health intervention.
This picture, I've been using for a few year.
When I started studying Ebola outbreak way back in 2014.
It was sad to see, you know,
this was one of the first measure implemented.
So Ebola is transmitted through bodily fluids,
and that can be, you know, blood, sweat, anything.
And the infectious person doesn't have to be alive
to still be infectious through their fluid.
So even after a death has been reported due to Ebola,
health officials would come in and burn the belongings,
sheets, mattress, anything that could contain bodily fluids.
Well, in a very naive mathematical model,
that makes a lot of sense, right?
You're burning the belongings, you're removing a vector,
you're reducing probability of transmission.
With adaptive behavior, what's likely to happen is that,
you know, these are poor communities,
burning everything that someone owned is a terrible thing.
So it's likely that they just stopped reporting cases.
And that's one of these unexpected consequences,
again, brought on by adaptive behavior.
But we sit all over right now, very locally,
even around COVID-19.
So I'll finish with one last example of a system,
something a little less sad, a little more majestic.
And really, the idea is that in a lot of systems,
we're gonna be studying, it's not one or one property,
or those properties are not even easy to define.
It's really all together that they create this idea
of complex system.
So the last system I'll show and I'll let the narrator
describe the system.
Really brings them all together in a majestic way, I think.
(tense music)
[Narrator 2] At sometimes of the year,
seasonal changes make the currents
especially rich in nutrients.
And then the ocean around the sea mount
becomes a virtual soup of plankton.
(tense music)
At such times, hunters gather in astonishing numbers.
(water sloshing)
Bonito, smaller relatives of the tuna,
they're searching for still smaller plankton feeders
that have been attracted by the bloom.
So are these jacks and their prey is nearby.
A school of anchoveta has strayed up near the surface,
even though it's broad daylight
and hunters are on the prowl.
(water sloshing)
These small fish can already feel the vibrations
of the approaching predators.
Swimming at speed, they formed into a ball
and now, they must wait for whatever comes.
I just love this example.
There's everything that I just mentioned is in there.
The seasonal changes that bring us interconnected,
bring interconnectedness with different systems,
collective behavior, nonlinear dynamics and instabilities.
We're actually gonna go back to this example
throughout the semester.
In two weeks or so, we're gonna be looking at why
during World War I, there was more sharks
and less fish than you'd expect in Italian water,
despite the fact that that fishermen were at war.
Much later, we're gonna look at what kind of rules
do you need to be able to simulate these
like collective behavior for fish balls, right?
It's just a beautiful system.
So I don't want to take too much time right now.
I think it's time that we get to work.
So soon, we're gonna be starting to see the different rules
for how we should even start modeling such systems
in a way that embrace their complexity and their richness.
So it's gonna be a really exciting semester.
I'll see you in the next one.
Thank you.