This is a rich
comparison between the way ants communicate and organize, compared to that of
our own. Unsurprisingly, they are similar
and naturally suggestive as well. This item
suggests that it may even by possible to learn from this. In the meantime, it is an excellent teaching
analogy for computer science.
Read and enjoy.
What
Do Ants Know That We Don’t?
BY DEBORAH GORDON
07.06.13
Ever
notice how ant colonies so successfully explore and exploit resources in the
world … to find food at 4th of July picnics, for example? You may find it
annoying. But as an ecologist who studies ants and collective behavior, I think
it’s intriguing — especially the fact that it’s all done without any central
control.
What’s especially
remarkable: the close parallels between ant colonies’ networks and
human-engineered ones. One example is “Anternet”, where we, a group of
researchers at Stanford, found that
the algorithm desert ants use to regulate foraging is like the Traffic Control
Protocol (TCP) [updated with correct spelling] used to regulate data traffic on
the internet. Both ant and human networks use positive feedback: either from
acknowledgements that trigger the transmission of the next data packet, or from
food-laden returning foragers that trigger the exit of another outgoing
forager.
This research led some
to marvel at the ingenuity of ants, able to invent systems familiar to us: wow,ants
have been using internet algorithms for millions of years!
(Wired, too, flirted with the concept of “anternet” in its Jargon Watch column last
year.)
But insect behavior
mimicking human networks — another example are the ant-like solutions to the
traveling salesman problem provided by the ant
colony optimization algorithm — is actually not what’s most
interesting about ant networks. What’s far more interesting are the parallels
in the other direction: What have
the ants worked out that we humans haven’t thought of yet?
What Ant Colony Networks
Can Tell Us About What’s Next for Human-Engineered Ones
Deborah Gordon
Deborah M. Gordon is a Professor in the
Department of Biology at Stanford. She studies the evolution of collective
organization by investigating the ecology and behavior of ant colonies, and has
been awarded fellowships from Guggenheim and the Center for Advanced Study in
Behavioral Sciences. Gordon is the author of two books, Ants at Work and Ant
Encounters: Interaction Networks
and Colony Behavior.
During
the 130 million years or so that ants have been around, evolution has tuned ant
colony algorithms to deal with the variability and constraints set by specific
environments.
Ant colonies use
dynamic networks of brief interactions to adjust to changing conditions. No
individual ant knows what’s going on. Each ant just keeps track of its recent
experience meeting other ants, either in one-on-one encounters when ants touch
antennae, or when an ant
encounters a chemical deposited by another.
Such
networks have made possible the phenomenal diversity and abundance of more than
11,000 ant species in every conceivable habitat on Earth. So Anternet, and
other ant networks, have a lot to teach us. Ant protocols may suggest ways to
build our own information networks…
Dealing with High
Operating Costs
Harvester
ant colonies in the desert must spend water to get water. The ants lose water
when foraging in the hot sun, and get their water by metabolizing it out of the
seeds that they collect. Since colonies store seeds, their system of positive
feedback doesn’t waste foraging effort when water costs are high — even
if it means they leave some seeds “on the table” (or rather, ground) to be
obtained on another, more humid day.
In
this way, the Anternet allows the colony to deal with high operating costs. In
the internet, the TCP protocol also prevents the system from sending data
out on the internet when there’s no bandwidth available. Effort would be
wasted if the message is lost, so it’s not worth sending it out unless it’s
certain to reach its destination.
More recently,
I’ve shown how
natural selection is currently optimizing the Anternet algorithm. I’ve been
following a population of 300 harvester ant colonies for more than 25 years,
and by using genetic fingerprinting we figured out which colonies had more
offspring colonies.
Colonies store food
inside the nest as a survival tactic. On especially hot days, colonies that are
likely to lay low instead of collecting more food are the ones that have more
offspring colonies over their 25-year lifetimes. Restraint therefore emerges as
the best strategy at the colony level. Long-lived colonies in the desert
regulate their behavior not to maximize or optimize food intake, but
instead to keep going without wasting resources.
In
the face of scarcity, the algorithm that regulates the flow of ants is evolving
toward minimizing operating costs rather than immediate accumulation. This is a
sustainable strategy for any system, like a desert ant colony or the mobile
internet, where it’s essential to achieve long-term reliability while avoiding
wasted effort.
During the 130 million years or so that ants have been around,
evolution has tuned ant colony algorithms.
Scaling Up from Small to
Large Systems
What
happens when a system scales up? Like human-engineered systems, ant systems
must be robust to scale up as the colony grows, and they have to be able to
tolerate the failure of individual components.
Since
large systems allow for some messiness, the ideal solutions utilize the
contributions of each additional ant in such a way that the benefit of an extra
worker outweighs the cost of producing and feeding one.
The
tools that serve large colonies well, therefore, are redundancy and minimal
information. Enormous ant colonies function using very simple interactions
among nameless ants without any address.
In engineered systems
we too are searching for ways to ensure reliable outcomes, as our networks
scale, by using cheap operations that make use of randomness. Elegant
top-down designs are appealing, but the robustness of ant algorithms shows
that tolerating imperfection sometimes leads to better
solutions.
Optimizing for
First-Mover Advantage
The
diversity of ant algorithms shows how evolution has responded to different
environmental constraints. When operating costs are low and colonies seek an
ephemeral delicacy — like flower nectar or watermelon rinds — searching speed
is essential if the colony is to capture the prize before it dries up or is
taken away.
Since
ant colonies compete with each other and many are out looking for the same
food, the first colony to arrive might have the best chance of holding on to
the food and keeping the other ants away.
How
does a colony achieve this first-mover advantage without any central control?
The challenge in this situation is for the colony to manage the flow of ants
so it has an ant almost everywhere almost all the time. The goal is to
increase the likelihood that some ant will be close enough to encounter
whatever happens to show up.
One
strategy ants use (familiar from our own data networks) is to set up a circuit of permanent highways
— like a network of cell phone towers —
from which ants search locally. The invasive Argentine ants are experts at
this; they’ll find any crumb that lands on your kitchen counter.
The Argentine ants
also adjust their paths, shifting from a close to random walk
when there are lots of ants around, leading each ant to search thoroughly in a
small area, to a straighter path when there are few ants around, thus allowing
the whole group to cover more ground.
Like
a distributed demand-response network, the aggregated responses of each ant to
local conditions generates the outcome for the whole system, without any
centralized direction or control.
In the face of scarcity, the algorithm that regulates the flow of
ants is evolving toward minimizing operating costs rather than immediate
accumulation.
Addressing Security
Breaches and Disasters
In
the tropics, where hundreds of ant species are packed close together and
competing for resources, colonies must deal with security problems. This has
led to the evolution of security protocols that use local information for
intrusion detection and for response.
One
colony might use (“borrow” or “steal”, as humans would say) information from
another, such as chemical trails or the density of ants, to find and use
resources.
Rather than attempting
to prevent incursions completely, however, ants create loose,
stochastic identity systems in which one species regulates its
behavior in response to the level of incursion from another.
There are obvious
parallels with computer security. It’s becoming clear (consider recent events!)
that we too will need to implement local evaluation and repair of
intrusions, tolerating some level of imperfection. The ants have found ways to
let their systems respond to each others’ incursions, without attempting to set
up a central authority that regulates hacks.
Ants have evolved security protocols that use local information
for intrusion detection and response.
Some
of our networks seem to be moving toward using methods deployed by the ants.
Take
the disaster recovery protocols of ants that forage in trees where branches can
break, so the threat of rupture is high. A ring network, with signals or ants
flowing in both directions, allows for rapid recovery here; after a break in
the flow in one direction, the flow in the other direction can re-establish a
link.
Similarly, early
fiber-optic cable networks were often disrupted by farm machinery and other
digging: one break could bring down the system because it would isolate every
load. Engineers soon discovered, as ants have already done, that ring
networks would create networks that are easier to repair.
***
Our
networks will continue to change and evolve. By examining and comparing the
algorithms used by ants in the desert, in the tropical forest, and the invasive
species that visit our kitchens, it’s already obvious that the ants have come
up with new solutions that can teach us something about how we should engineer
our systems.
Using
simple interactions like the brief touch of antennae — not unlike our fleeting
status updates in ephemeral social networks — colonies make networks that
respond to a world that constantly changes, with resources that show up in
patches and then disappear. These networks are easy to repair and can grow or
shrink.
Ant
colonies have been used throughout history as models of industry, obedience,
and wisdom. Although the ants themselves can be indolent, inconsiderate of
others, and downright stupid, we have much to learn from ant colony protocols.
The ants have evolved ways of working together that we haven’t yet dreamed of.
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