Colonies of ants base decisions like where to establish a nest based on their population density. Scientists theorize that ants can estimate how many of their kind are around by randomly exploring the area and bumping into other ants. New research from MIT computer scientists not only supports this theory but could also be used to analyze social networks, improve robot swarms, and yield improve algorithms for networked communications in distributed computing applications. From MIT News:

“It’s intuitive that if a bunch of people are randomly walking around an area, the number of times they bump into each other will be a surrogate of the population density,” says Cameron Musco, an MIT graduate student in electrical engineering and computer science and a co-author on the new paper. “What we’re doing is giving a rigorous analysis behind that intuition, and also saying that the estimate is a very good estimate, rather than some coarse estimate. As a function of time, it gets more and more accurate, and it goes nearly as fast as you would expect you could ever do.”

Musco and his coauthors — his advisor, NEC Professor of Software Science and Engineering Nancy Lynch, and Hsin-Hao Su, a postdoc in Lynch’s group — characterize an ant’s environment as a grid, with some number of other ants scattered randomly across it. The ant of interest — call it the explorer — starts at some cell of the grid and, with equal probability, moves to one of the adjacent cells. Then, with equal probability, it moves to one of the cells adjacent to that one, and so on. In statistics, this is referred to as a “random walk.” The explorer counts the number of other ants inhabiting every cell it visits.

In their paper, the researchers compare the random walk to random sampling, in which cells are selected from the grid at random and the number of ants counted. The accuracy of both approaches improves with each additional sample, but remarkably, the random walk converges on the true population density virtually as quickly as random sampling does.

“Exploring networks efficiently” *(MIT)*