Social insects such as termites, some bees and wasps in general, and ants in particular, have held a mysterious fascination for men since the beginning of earliest recorded time. No other organism of comparable size, unless it has been of outstanding economic benefit or harm to the human race, has ever engaged its attention so consistently.1 Ants, which are the fine and beautiful flowers of the tree of life, have excited the philosophical observation and speculation of thoughtful men of all times. Innumerable comparisons have been made between human civilization and the miniature civilization of ants; theories have been advanced and morals illustrated, utopian schemes encouraged and sometimes whole theories of the state built up for man on the basis of analogy with these little insects.2 But in most of the cases the morals have been false and the analogies were used misleadingly. In this article we try to explain the basic distinctive characteristic of ant colonies: Collective Intelligence.
The abundance of ants on earth is legendary. They live almost everywhere except very cold places such as Antarctica and Greenland. A worker is less than one-millionth the size of a human being, yet ants taken collectively rival people as dominant organisms on the land. Lean against a tree almost anywhere and the first creature that crawls on you will probably be an ant. Stroll down a suburban sidewalk with your eyes fixed on the ground, counting the different kinds of animals you see. The ants will win hands down. The British entomologist4 C. B. Williams once calculated that the number of insects alive on earth at a given moment is one million trillion, 1018. If, to take a conservative figure, one percent of this host is ants, their total population is ten thousand trillion. Individual workers weigh on average between one to five milligrams, according to the species. When combined, all ants in the world taken together weigh about as much as all human beings. But being so finely divided into tiny individuals, this biomass5 saturates the terrestrial environment.6
Ants absolutely dominate in rainforests, which are the most biologically diverse ecosystems on earth. Rainforests are so diverse that in a single leguminous tree (a relative to beans and peas) in Peru, 43 species of ants belonging to 26 genera7 were found, about equal to the ant fauna8 of the British Isles. In a single square mile of tropical forest in Peru or Brazil, there may be 1,500 or more species of butterflies-twice the total number found in the United States and Canada combined.9 In Amazon rainforests ants and termites together compose nearly a third of the animal biomass. In other words, when all kinds of animals, large and small, from jaguars to monkeys down to roundworms and mites, are weighed, nearly a third of the weight consists of the flesh of ants and termites.
All of the ants, composing in formal taxonomic classification the family Formicidae of the order Hymenoptera, contain about 9,500 species known to science and at least twice that number of species remaining to be discovered, most of which are confined to the tropics. The total number of species of social insects is about 13,500 out of a grand total of 750,000 insect species that have been recognized to date by biologists. These numbers show that social insects seem to constitute 2 percent of all insects yet, in terms of biomass, social insects are half or more of all insects. Why are ants and other social insects so successful in the terrestrial environment? Their strength comes from their social organization.10 In addition to the question of why ants and other highly social insect species have been so successful, it is also important to understand how such a large collection of individuals maintains order and collectively accomplishes tasks without producing chaos. With potentially thousands of individual ants to coordinate, how do they make decisions regarding who does what and when, especially critical decisions regarding reproduction?11 These questions become even more intriguing when you realize that ants have quite limited sensory devices to experience the world. They also have relatively simple nervous systems that process only a limited number of stimuli and are aware of only a few minutes to a few hours into the past.12
Intelligence can be defined simply as the ability to solve problems. One system is more intelligent than another system if in a given time interval it can solve more problems, or find better solutions to the same problems. A group can then be said to exhibit collective intelligence if it can find more or better solutions than the whole of all solutions that would be found by its members working individually.
All organizations, whether they are firms, institutions or sporting teams, are created on the assumption that their members can do more together than they could do alone. Yet, most organizations have a hierarchical structure, with one individual at the top directing the activities of the other individuals at the levels below. Although no president, chief executive or general can oversee or control all the tasks performed by different individuals in a complex organization, one might still suspect that the intelligence of the organization is somehow merely a reflection or extension of the intelligence of its hierarchical head. This is no longer the case in small, closely interacting groups such as soccer or football teams, where the “captain” rarely gives orders to the other team members. The movements and tactics that emerge during a soccer match are not controlled by a single individual, but result from complex sequences of interactions. Still, they are simple enough for an individual to comprehend, and since soccer players are intrinsically intelligent individuals, it may appear that the team is not really more intelligent than its members.
With the growing interest in complex adaptive systems, artificial life, swarms, and simulated societies, the concept of “collective intelligence” is coming more and more to the fore. The basic idea is that a group of individuals (e.g. people, insects, robots etc.) can be smart in a way that none of its members is. Complex, apparently intelligent behavior may emerge from the synergy created by simple interactions between individuals that follow simple rules.
Now we have lots of questions to ask about the success of ants as a group. How do they govern? Who is the ruler? How do they foresee the future? How do they elaborate plans and preserve equilibrium? These, indeed, are puzzling questions. Every single ant in a colony seems to have its own agenda, and yet an insect colony looks so organized. The seamless integration of all individual activities does not seem to require a supervisor. For example, leaf-cutter ants cut leaves from plants and trees to grow fungi. Workers forage for leaves hundreds of meters away from the nest, literally organizing highways to and from their foraging sites. Weaver ant workers form chains of their own bodies, allowing them to cross wide gaps and pull stiff leaf edges together to form a nest. Several chains can join to form a bigger one over which workers run back and forth. In their moving phase, army ants organize impressive hunting raids, involving up to 200,000 workers, during which they collect thousands of prey.14
A harvester ant colony performs many tasks: It must collect and distribute food, build a nest, and care for the eggs, larvae, and pupae. It lives in a changing world to which it must respond. When there is a windfall of food, more foragers are needed. When the nest is damaged, extra effort is required for quick repairs. Task allocation is the process that results in certain workers engaged in specific tasks, in numbers appropriate to the current situation. Task allocation is a solution to a dynamic problem and thus it is a process of continual adjustment. It operates without any central or hierarchical control to direct individual ants into particular tasks. Although “queen” is a term that reminds us of human political systems, the queen is not an authority figure. She lays eggs and is fed and cared for by the workers. She does not decide which worker does what. In a harvester ant colony, many feet of intricate tunnels and chambers and thousands of ants separate the queen, surrounded by interior workers, from the ants working outside the nest and using only the chambers near the surface. It would be physically impossible for the queen to direct every worker’s decision about which task to perform and when. Consider the commercially available ant farms being sold. Since it’s forbidden to transfer ant queens, in the US ant farms are sold with only worker ants. Still they work in harmony. They build their nest, they build bridges, they collect food and they defend their colony. They do all these things without a queen. The absence of central control may seem counterintuitive, because we are accustomed to hierarchically organized social groups in many aspects of human societies, including universities, businesses, governments, orchestras and armies. This mystery underlies the ancient and pervading fascination of social insect colonies.
No ant is able to assess the global needs of the colony, or to count how many workers are engaged in each task and decide how many should be allocated differently. The capacity of an individual is limited. It cannot make complicated assessments. It probably cannot remember anything for very long. Its behavior is based on what it perceives in its immediate environment. Each worker needs to make only fairly simple decisions. There is abundant evidence, throughout physics, the social sciences and biology that such simple behavior by individuals can lead to predictable patterns in the behavior of the group. It should be possible to explain task allocation in a similar way, as the consequence of simple decisions by individuals.
Though ant colonies must respond to changing conditions, the response does not have to be perfect. It is not like clockwork, or an army, each unit snapping into place so the whole system ticks on without a hitch. There must be enough ants to collect food, often enough for the colony to survive and grow. The appropriate range of numbers should be allocated over a set of similar occasions. If the colony did not get enough food today, perhaps it will tomorrow. The process results in more or less the right number of ants engaged in the appropriate task, often enough for the colony to carry on.
Maximizing the number of ants that perform each task may not always be best for the colony. A task allocation problem for a human city is how to get the right number of firefighters to the scene of a fire. It may be a waste to have too many firefighters on the city payroll. Too many ants allocated to each task may be expensive for a colony if the excess ants could be doing something more useful than waiting around when they are not needed.
The most difficult thing to grasp about task allocation is that it is not a deterministic process even at the individual level. An ant does not respond the same way every time to the same stimulus; nor do colonies. Some events influence the probabilities that certain ants will perform certain tasks, and this regularity leads to predictable tendencies rather than perfectly deterministic outcomes. The ant is jostled in a stream of events that send it sometimes into one task, sometimes another. Task allocation is not a system in which each ant awaits the crucial event that defines its status forever. Like a twig in a turbulent river, an ant may tend to go in one direction, but there are many places it could get washed ashore, to be picked up and then swept in another direction altogether.
Stories about totalitarian societies, inexorable armies, and voracious monsters are often told as stories about ants. But ants have no dictators, no generals and no evil masterminds. In fact, there are no leaders at all.
In short, the basic mystery about ant colonies is that there is no management. A functioning organization with no one in charge is so unlike the way humans operate as to be virtually inconceivable. There is no central control. No insect issues commands to another or instructs it to do things in a certain way. No individual is aware of what must be done to complete any colony task. Each ant scratches and prods its way through the tiny world of its immediate surroundings. Ants meet each other, separate, go about their business. Somehow these small events create a pattern that drives the coordinated behavior of colonies.15
More is different. This old slogan of complexity theory actually has two meanings that are relevant to our ant colonies. First, the statistical nature of ant interaction demands that there is a critical mass of ants for the colony to make intelligent assessments of its global state. Ten ants roaming across the desert floor will not be able to accurately judge the overall need for foragers or nest-builders, but two thousand will do the job admirably. Individual ants do not know that they are prioritizing pathways between different food sources when they lay down a pheromone17 gradient near a pile of nutritious seeds. In fact, if we only studied individual ants in isolation, we’d have no way of knowing that those chemical secretions were part of an overall effort to create a mass distribution line, carrying comparatively huge quantities of food back to the nest. It is only by observing the entire system at work that the global behavior becomes apparent.
Ignorance is usually useful for ants. The simplicity of the ant language-and the relative stupidity of the individual ants-is, as the computer programmers say, a feature but not a bug. Emergent systems can grow unwieldy when their component parts become excessively complicated. Better to build a densely interconnected system with simple elements, and let the more sophisticated behavior trickle up. That is why an ant does not respond to all stimuli around her, namely she ignores until she decides that the stimulus is strong enough to be responded to.
Encourage random encounters. Decentralized systems such as ant colonies rely heavily on the random interactions of ants exploring a given space without any predefined orders. Their encounters with other ants are individually arbitrary, but because there are so many individuals in the system, those encounters eventually allow individuals to gauge and alter the state of the colony itself. Without those haphazard encounters, the colony would not be capable of stumbling across new food sources or of adapting to new environmental conditions.
Look for patterns in the signs. While the ants do not need an extensive vocabulary and are capable of syntactical formulations, they do rely heavily on patterns in the semiochemicals they detect. A gradient in a pheromone trail leads them toward a food source, while encountering a high ratio of nest-builders to foragers encourages them to switch tasks. This knack for pattern detection allows meta-information to circulate through the colony mind: signs about signs. Smelling the pheromones of a single forager ant means little, but smelling the pheromones of fifty foragers imparts information about the global state of the colony.
Pay attention to your neighbors. This may well be the most important lesson that the ants have to give us, and the one with the most far-reaching consequences. You can restate it as “Local information can lead to global wisdom.” The primary mechanism of swarm logic is the interaction between neighboring ants in the field: ants stumbling across each other, or each other’s pheromone trails, while patrolling the area around the nest. Adding ants to the overall system will generate more interactions between neighbors and will consequently enable the colony to solve problems and regulate itself more effectively. Without neighboring ants stumbling across one another, colonies would be just a senseless assemblage of individual organisms-a swarm without logic.
Ants, first of all, have something to teach us about how nature works. Any system whose behavior arises from the interactions of its components has something in common with ant colonies. Using ants and other social insects as models, computer scientists have developed software agents that cooperate to solve complex problems, such as the rerouting of traffic in a busy telecom network or internet. Another example, the famous traveling salesman problem, in which a salesman tries to find the shortest and fastest route between many cities, is almost impossible to solve definitively. But with the methods inspired by ants the problem can be solved at least approximately, because ants are very good at finding the shortest path between the food and the nest collectively. Collective robotics borrowed from collective intelligence in ant colonies is being used to manage systems composed of lots of robots in synchronization.
Nature is a book to be read by the people who approach it to live in harmony, not to dominate. We are not the owners of the beautiful things around us, but observers searching for signs which reveal the wisdom behind them.