As many of my students can attest, I’ve always loved emergent behavior where you have a whole bunch of “stupid” agents working with simple rules, but the end result is amazingly complex and wondrous. There are termites, for example, that by following simple rules (and with no “leader” in charge) manage to build large mounds with extraordinarily precise climate control in certain chambers where they farm special fungi. If you held a gun to my head and told me to use dirt, sticks, and other bits lying around to build a chamber that would be nearly constant temperature day and night, winter and summer, I’d have to tell you to pull the trigger. Yet these insects, with no blueprint or foreman, pull it off over and over again across thousands of years. (Doesn’t evolution just rock!)

No surprise, then, that I was smitten with this recent TED video (of a talk from four years ago) where Deborah Gordon talks about her research into how ants figure out how to allocate tasks (i.e., who does what when).

If you want to play with this sort of thing, I recommend both NetLogo and Breve, as well as this cool Breve-based screensaver, which evolves walking creatures while you’re momentarily distracted from your random web surfing. Both NetLogo and Breve can happily suck up way too much of your life (there are literally days to be spent playing with all the canned models in NetLogo), but it’s great fun, and working with tools like this can really help build your intuition for how complex systems of agents can work.

Below the fold I list some of my favorite NetLogo simulations that I often use in demos.

• The termite simulation. This is probably my favorite because it’s easy to explain and has a cool and non-obvious outcome. I would often set up a scenario (lots of little autonomous robots collecting ice or mineral deposits on Mars), and ask people to suggest a set of rules that they might use to get the set of robots to bring everything to a single location. Then I explain the two simple rules this system uses, and show them the behavior. They are usually interested and impressed. (The Painted Desert demo is a nice extension of the earlier Termites model.)
• Wolf-Sheep predation. This is probably my other favorite for demos. I’d start it up (with the grass turned “on”) when there was some dead time and no one was around, and let it run for a long time in its stable state. Then when someone would show up I’d explain the simulation while it’s still running and we’d talk about the fact that things were fairly stable. Then I’d ask them what they thought would happen if I turned the grass “off” (i.e., made it so there was an infinite amount of grass all the time). Then we’d turn it off and watch the system crash, which tended to get people’s attention. It’s also a nice example of how a small change can really throw off a dynamic system.
• Ants – People always like food foraging, and playing with the settings on the pheromones is a nice way of showing how small changes can really affect the behavior of the system.
• Bug Hunt Speed – A neat, simple demonstration of evolution at work. And you can let the kids do the bug catching for you, which helps them get involved.
• AIDS and Virus – Both suggest how these ideas could be used in “real world” settings (such as public health policy). You have to be careful how you interpret results from a model like that, but they can be great intuition builders.
• Fireflies – Simple rules leading to coordinated behavior without anyone “in charge”.
• Flocking is always fun, and one of the applications that people are the most familiar with (even if they don’t realize it). Flocking algorithms like this are widely used in the film industry to provide lifelike behavior to large groups of computer generated individuals (birds, soldiers, insects). Breve will also do some very cool flocking behavior in 3D.

We are what we eat, and (all too often) we eat what’s cheap, and what’s cheap is what we subsidize.

Or, turning the thing around…

We subsidize unhealthy, environmentally irresponsible foods, so it’s hardly a surprise that, as a nation, we eat tons of the stuff.

To cleanse your pallet, I’ll leave you with a delicious organic mushroom from River Nene Farms…

I just discovered The Gapminder, a very cool visualization tool created by Hans Rosling and others. It gives you the ability to visualize changes in a variety of socio-economic indicators across both space and time, with particularly effective use of simple animations across the time dimension.

It’s really cool to play with, and there are a lot of things you can change that aren’t immediately obvious (I found the short demo in the on-line help quite helpful). You can certainly learn a lot by goofing around with it (as well as generate a lot of meaningless visual noise). The screenshot above is from a plot of life expectancy vs. per capita income over time (the default plot you get when you start it up). I had to watch it a few times to sift out some trends, but after getting past the obvious “generally, more money means longer life” I realized that lots of blue bubbles (i.e., African countries) were “falling” out of the cloud at the end like particulates settling out of a liquid. This indicates that their life expectancy has plummeted in the last few years, which is obviously not the right direction. Why? Almost certainly the continuing AIDS crisis in large parts of Africa, and visualizations like this make it painfully (literally) obvious that this must be cause for serious concern.

In the graph above, I’ve highlighted Botswana,which shows a particularly depressing case. Both their life expectancy and per capita incomes were moving in a happy direction until the early 90’s when, despite continued gains in per capita income (which I’d like to know more about), their life expectancy flew the wrong direction.

Take a second to think about the enormous human suffering a change like that implies.

Thanks to John Hawks for the pointer. Hawks points out that the use of circles of different areas probably isn’t a great way to visualize quantitative differences (a general point Tufte raised years ago), but Rosling’s going for quick impact rather than quantitative precision:

So far, we have had a major hit with two target groups: children under 12 and heads of state. What they have in common is that you have only 5 to 10 seconds to impress them.

(You just have to love the reality that you need the same tools to communicate with little kids and world leaders. Maybe we should just put the little kids in charge? They might have more empathy.)

One thing that really annoyed me when I wrote this post was the fact that you can’t easily embed these graphs in a web page. I don’t think I’d really appreciated how used I’d grown to being able to embed images, video, maps, slideshows, and the like in blog posts. I think embedding has become absolutely key to the success and mass propagation on the web, and people designing new tools (or updating existing ones) better keep that in mind.

Those wild and crazy people at WalkingTimeBomb.com have a whole host of ads aimed at college students who blithely assume (despite all the evidence to the contrary) that they’ll quit smoking after they graduate. Some of them are pretty strange and don’t make a lot of sense, but some are real winners:

Hitting yourself in the face with a hammer is not harmful as long as you only do it socially.

When I’m at the bar I like to smack myself in the face a few times with a good claw hammer. So what? Who am I hurting? Or the other day I was walking along with Joe and he pulled out a shiny ball-peen model. Man, we just went to town with that baby. But what of it? I don’t plan on being one of those life-long hammerers. I’ll just quit after I graduate by tapering off with some of those small rubber mallets.

Generally I’m a pretty libertarian kind of guy, and I rarely give students grief about their smoking even though they bloody well ought to know better. Going through this cancer fight with Dad, though, makes it tough not to run around shouting at students that I see smoking.

The problem is that the claim that smoking (or not wearing a seatbelt or a bicycle helmet or whatever) only hurts the fool is based on a sad and ultimately unacceptable assumption that there is and will be no love in that person’s world when the odds come for their due. To watch my mother hold Dad while he’s vomiting again in the middle of the night, to hear her voice break on the phone after spending another all-nighter with him at the hospital, all this shatters any arguments that the impact of these choices is limited.

We are people, and that has implications.

When my father started smoking in the early 40’s, there was no broad understanding of the horrible risks involved, and as that data became clearer the tobacco industry spent millions to confuse and obfuscate the issues. At some point when I was a kid (late 60’s, early 70’s) Dad became convinced and stopped cold, an action I have always admired.

Today’s students can’t claim ignorance, and I sure as hell hope they don’t plan on lonely, loveless lives. I know that I have higher aspirations for them…