Ants are renowned for their industriousness. Ask the grasshopper in the story by Aesop. He had to come begging the hard-working and for food when winter came because he had frittered away his summer. But that is a fable, the ultimate in what scientists call anecdotal evidence.
And new research at Georgia Tech suggests that although ant colonies are very efficient, that may be because 70 per cent of them are doing very little at least when it comes to tunnel digging. Daniel I. Goldman, a physicist at the Georgia Institute of Technology, and his colleagues found that the secret to efficient tunnel digging by fire ants was that 30 per cent of the ants did 70 per cent of the work. They reported their fable-shaking finding in the journal Science. The reason, it seems, is that the ants were working in narrow tunnels where traffic jams could easily clog up the entire effort to build nests. So it helped if some of them took a pileup in the tunnel as a signal to suggest that they take a break. To come to this conclusion, they set up material for the ants in containers in the lab. After painting identifying codes on the ants, videotaping them and analyzing who was doing what, the team found several things. The ants were easily discouraged by traffic jams and were flexible enough to turn around and go back out the tunnel. It was the hardworking few who kept the job going.“Some of them worked for five hours at a time just going up and down and up and down and up and down. And most of the other ants never appeared at the tunnel,” Dr Goldman said. This didn’t have to do with some ants being lazier than others. His team could remove the hard workers and another group would take over and do just as well, and the same 70/30 rule would hold.
After running various computer models of the behaviour, he found out that this was the ideal distribution of work. And that the individual virtual ants had to have idleness built-in as a potential response to a crowded tunnel. To get the digging done efficiently, he said, “there’s only one good strategy” unequal distribution of tunnel digging work and a willingness to turn away from work. If you start out in a computer model with eager diggers, he said, you have to add some programming that says, for any ant, “I’m going to get down there and then if it’s taking too long, I’ll turn around.”He said, “You have to add a lot of this kind of giving up in the eager ants to make it actually work.”His team also tested this out with small robots and came up with the same conclusion. And this could matter quite a bit, he pointed out. The formula does not apply only to tunnel digging, but to any situation in which a traffic jam could stop progress, such as a swarm of robots entering a disaster site to search for survivors or hazards. Or imagine a lot of nanobots deployed into the bloodstream to deliver drugs to some site in the body. If this distribution of labour operates this way in your office, however, (the 30 per cent may laugh knowingly now), there’s no real solace to be taken from the ant experiment, unless you are digging a tunnel perhaps. Assuming everyone has their own computer, phone and cubicle, they could all be working all the time. It does, however, apply to kitchens with limited space. Too many chefs? But then we knew that.