“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 behavior, 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” — an 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.
- More Here
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 behavior, 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” — an 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.
- More Here
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