Our first unit explores the question "what is complexity?"

As you'll see this turns out to be a hard question to answer precisely.

We'll start out very intuitively by giving a series of examples of the sorts of phenomena studied by complex systems scientists.

This will be a preview of the sorts of topics that we'll be covering in this course.

Then we'll make a list of some important properties that are common to most complex systems.

We'll briefly look at defining the notion of complexity, something that we'll come back to later in the course.

We'll also survey the core disciplines, goals and methodologies of the field of complex systems research.

At that point we'll be ready for the first in a series of what I call "guest spots", that is

in which I interview prominent complex systems scientists about their views on the field.

The last half of this unit will focus on Netlogo, the simulation and programming platform

that we'll be using to illustrate many of the ideas of complex systems in this course.

You're ready to get started? Let's go.

A great example to begin with is ants. Nigel Franks, a well known ant researcher once wrote

that the solitary army ant is behaviorally one of the least sophisticated animals imaginable.

In extremely high numbers however, it's a different story.

Here for example, is a colony of army ants building a tunnel.

Each ant on its own is very simple, but the colony as a whole can work together cooperatively

to accomplish very complex tasks without any central control,

that is without any ant or group of ants being in charge.

In other words, ant colonies can organize themselves to produce structures much more

complicated than any single ant can produce.

Here's an example of ants building a bridge with their bodies, so that other members of the

ant colony can cross the gap between the two leaves.

This video shows ants assembling this kind of bridge.

They start up here, come in here on a stick, all the way up to the top, and they're going to

eventually chain themselves to go all the way down to the ground.

You can see them gradually adding themselves to the structure.

Each ant is secreting chemicals to communmicate with the other ants, and the whole

bridge is built without any central control.

You might call this an example of a decentralized, self-organizing or self-assembling system.

Other social insects produce similar behavior.

For instance, here is an example of the kind of complex structure built by termites.

It serves as a nest. A major focus of complex systems research is to understand how individually

simple agents produce complex behavior without central control.

In these examples, the simple agents are insects, but we'll see many other kinds.

Another classic example of a complex system is the brain. Here, the individual simple agents are

neurons. The human brain consists of about 100 billion neurons, with a 100 trillion connections

between those neurons. Each neuron is relatively simple compared to the whole brain,

and again there's no central control. Somehow, the huge ensemble of neurons and

connections give rise to the complex behaviors that we call cognition, intelligence, or even

creativity. Brain imaging has shown that these neurons organize themselves into

different functional areas. Just like the ants or termites, neurons can self-organize into complex

structures that help the species function and survive. Yet another complex system is the immune system.

The immune system is distributed across the body,

involving many different organs as shown in this picture, and trillions of cells moving around

in the bloodstream or lymph stream protecting and healing the body from damage or disease.

For example, this is a picture of immune cells, these ones in blue here, attacking a cancer cell

here in the center. Like the ants we saw before, immune system cells communicate with

one another through chemical signals, and work together without any central control

to launch coordinated attacks on what they perceive as threats to the body.

In addition, the population of immune cells in the body is able to change, or adapt itself

in response to what that population of cells perceives in its environment.

This kind of adapatation is another key characteristic of complex systems.

Another familiar example of a complex system is the human genome. Here's an image of

a human genome. Each of these worm-like structures is a chromosome, and there're 23 pairs

of them. You can see that this is a male, because it has an X-Y pair.

Each of these chromosomes is made up of thousands of genes. Genes, of course, are

strings of DNA along the chromosome. It's currently thought that the human genome has about 25,000

genes which code for proteins. In complex systems terms, you could think of the genes as

simple components that interact with other genes in a decentralized way. And the way that they

interact is through genetic regulatory networks. They control one another's expression, where

expression means translation into proteins. Here is one small genetic regulatory network

that's been mapped out by researchers. Here, each of these rectangles or ovals represents

a gene, and an arrow from one gene to another means that the first gene controls the

expression of the second gene. It turns out that the human genome is made up of thousands of

networks like this one, in which genes interact with one another in complicated ways,

and it's these interactions largely responsible for our own complexity.

The idea of networks is central to the study of complexity in nature.

Here's another kind of network- a food web.

Here, each node, or entity in the network, is a particular group of species, and the arrows

represent who eats whom. If one species group points to another, that means that the first

is food for the second. For example, you can see that foxes here are at the top of this particular

Alaskan food web since they eat several kinds of animals but nothing eats them,

at least not on this chart. Here's an abstract diagram of an even more complicated food web

from the gulf of Alaska. When we talk about networks later in the course, we'll see some very

interesting examples of decentralized self-organization in food webs like this, and other kinds

of networks. Probably the kind of network you're most familiar with is a social network.

Here's part of my own social network with me here. These links represent friendship relationships.

My friends are linked to their friends and so on and so forth. Social networks turn out to have

some very interesting patterns, ones that also turn up in biological and technological networks.

Later in this course, we'll look in depth at what those patterns are, and how they form.

Complex systems scientists are very interested in studying large social networks such as

Faceook, to understand their structure, how they form, how they change over time, and perhaps

most interestingly, how information is transmitted in such networks, among other questions.

Economies are another type of complex system, in which networks of interaction are

fundamental. Here, we see a sample of the international financial network, where

nodes represent financial institutions, and links represent relations among them. For example,

if a bank owns shares of another bank, the two are linked. It turns out that the amount of

connectivity in such a network, as well as the kinds of links present, can have a big effect

on how stable the network is to changes, such as a bank going out of business.

The new interdisciplinary field of network science, which arose from the complex systems research

community, studies these kinds of phenomena in networks from many different disciplines.

As a final example, we look at the study of cities as complex systems.

It's often been said that a city is like a living organism in many ways.

But to what extent do cities actually resemble living organisms,in the way that they're structured,

grow, scale with size and operate? These and other questions form the basis of a rapidly growing

area of complex systems research, which we'll look at in detail later in the course.