Whether you’re super industrious or kind of a slacker, you’ve probably noticed that a few people do most of the work on a project or in an office. This is true for ants, too, and apparently this uneven division of labor is good—so much so that physicists studied the insects to program robots according to ant work patterns.
Physicists at the Georgia Institute of Technology report in Science (paywall) painted fire ants different colors to identify individuals and study how often each returned to a work site—to help dig a tunnel. They learned that most just loll and only some are highly industrious. The scientists say 30% of fire ants do 70% of the work.
And this division of labor is actually optimal: There aren’t too many ants crowded in a tunnel obstructing each other and busier insects can come and go as often as they want. Which is a lot, it turns out. ”We noticed that if you have 150 ants in a container, only 10 or 15 of them will actually be digging in the tunnels at any given time,” says Georgia Tech physics professor Daniel Goldman.
The scientists found a benefit to this seeming inequality. “Without it, digging just doesn’t get done,” he says.
Ant work patterns showed the physicists how to best program robots. Goldman posits that someday machines could be sent to excavate in crowded spaces. The robots, like ants, will bump into each other and end up wasting time and obstructing work if they’re not programmed sensibly. Ants taught the physicists just how many robots can get a job done in a tunnel-like space. Optimizing underground robots’ activity could prove useful in disaster recovery, mining, and more.
As robots have no boss, they have to be programmed to operate effectively independently, avoiding clogs and maximizing digging. ”While observing the ants, we were surprised to see that individuals would sometimes go to the tunnel and if they encountered even a small amount of clog, they’d just turn around and retreat,” Goldman explains. “When we put those rules into combinations with the robots, that created a good strategy for digging rapidly with low amounts of energy use per robot.”
The physicists programmed the robots, who were not ant size but about a foot tall, with three modes—eager, reversal, and lazy. If robots were “eager,” they worked quickly but got stopped often due to clogging. More eager workers didn’t get more done, in other words.
When programmed in “reversal” mode, robots turned back if there were delays at the work site. In the “lazy” mode, the machines dawdled.
“Eager is the best strategy if you only have three robots, but if you add a fourth, that behavior tanks because they get in each other’s way. Reversal produces relatively sane and sensible digging. It is not the fastest strategy, but there are no jams,” Goldman says.
What’s notable is that laziness proved strategic and smart. “If you look at energy consumed, lazy is the best course,” he contends.
We already know that too many cooks in the kitchen spoils the soup. So perhaps this lazy labor principle is also true for humans.