Researchers are beginning to understand how ant colonies can make complex decisions. It’s best understood, they say, as something like an optimization algorithm:

Scientists found that ants and other natural systems use optimization algorithms similar to those used by engineered systems, including the Internet. These algorithms invest incrementally more resources as long as signs are encouraging but pull back quickly at the first sign of trouble. The systems are designed to be robust, allowing for portions to fail without harming the entire system. Understanding how these algorithms work in the real world may help solve engineering problems, whereas engineered systems may offer clues to understanding the behavior of ants, cells, and other natural systems.

Cold Spring Harbor Laboratory, “Deciphering algorithms used by ants and the Internet” at ScienceDaily The paper is open access.

The researchers explain in more detail:

The same algorithm used by Internet engineers is used by ants when they forage for food. At first, the colony may send out a single ant. When the ant returns, it provides information about how much food it got and how long it took to get it. The colony would then send out two ants. If they return with food, the colony may send out three, then four, five, and so on. But if ten ants are sent out and most do not return, then the colony does not decrease the number it sends to nine. Instead, it cuts the number by a large amount, a multiple (say half) of what it sent before: only five ants. In other words, the number of ants slowly adds up when the signals are positive, but is cut dramatically lower when the information is negative. Navlakha and Suen note that the system works even if individual ants get lost and parallels a particular type of “additive-increase/multiplicative-decrease algorithm” used on the Internet.

Cold Spring Harbor Laboratory, “Deciphering algorithms used by ants and the Internet” at ScienceDaily The paper is open access.

Computer programmers learned a better solution to the Traveling Salesman Problem, in part, from ants:

Navigation expert Eric Cassell, author of Animal Algorithms (2021), has done a lot of work in this area, especially on the question of how life forms that do not engage in abstract “thinking,” as we know it, navigate with precision. In his view, similar to that of the Cold Spring Harbor authors, something like an algorithm kicks in. It may be possible to search for the means by which the algorithm is generated in the ant’s neurons or genes.

A larger conundrum is that the ant (along with many other insects) manages these complex patterns with brains of only 100,000 to one million neurons. (Humans have 3 billion neurons.) It does not appear that the original source of the intelligence that enables the algorithm is the ant itself. But that keeps nature interesting.

You may also wish to read: For ants, building a bridge is no “simple” task. Richard Stevens: There is nothing “simple” about designing neural systems and the computer systems to receive and interpret neural sensory inputs. The Quanta piece promotes a notion that software algorithms are “simple.” To the contrary, it would take an army of engineers to do what ants do instinctually.

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How do insects use their very small brains to think clearly? How do they engage in complex behaviour with only 100,000 to a million neurons? Researchers are finding that insects have a number of strategies for making the most of comparatively few neurons to enable complex behavior.