Deborah M Gordon:
"Regardless of size, as ant workers get older, they move from one task to another, switching tasks as circumstances require. But switching tasks, either in stages of life or in the short term, is not consistent with organisation by division of labour. However appealing it might be to imagine ant colonies organised by division of labour, the evidence tells us they are not.
What I and others have found, instead, is that the collective process of task allocation in ant colonies is based on networks of simple interactions. For example, in harvester ants, colonies regulate foraging activity, adjusting the numbers of ants currently out searching for seeds to the amount of food available. An outgoing forager does not leave the nest until it meets enough returning foragers coming back with food. This creates a simple form of positive feedback: the more food is available, the more quickly foragers find it, and the more quickly they return to the nest, eliciting more foraging. When I provide a windfall of food by placing a lovely little pile of organic millet outside the colony, ants that formerly performed other tasks switch to become foragers. Each encounter, in the form of a brief antennal contact, has no meaning to the ant, but in the aggregate, the rate of encounters determines how many ants are currently foraging.
The system that ant colonies use to organise their work is a distributed process. Like division of labour, distributed processes can take different forms. A distributed process is not the opposite of division of labour – but it’s different in important ways. Primarily, in a distributed process, there is never central control, while in division of labour there might be. A leader can tell one citizen to make candles and another to make shoes. In a distributed process this would happen through local interactions, for example with people who want to buy candles or shoes – creating demand that is filled by an entrepreneur who then meets the demand. Most fathers might not be as good at changing diapers as most mothers but, at 3am, the finer points of technique don’t matter
At least in the short term, a system organised by a distributed process and one organised by division of labour could look the same: the same individuals could do the same task over and over. An ant might do the same task day after day. It might go out to forage, come back to the nest, engage again in the interactions that stimulate it to forage, and spend the night among other ants that recently returned from foraging. The next morning, it is again in a situation in which it is likely to forage, and this could continue day after day. However, in different conditions, the ant might do another task, and so its role is not fixed.
Distributed processes and division of labour can both be effective, but they don’t function in the same way. For division of labour, specialisation can lead to better work. By contrast, in a distributed process, the fact that individuals are interchangeable makes the whole system more robust and more resilient. If the individual who performs a task gets lost or becomes unfit to do it, another can step in. The individuals don’t have to be all alike, but the differences among them are not large enough to affect the viability of the system. Most fathers might not be as good at changing diapers as most mothers but, at 3am, the finer points of technique don’t matter. If anyone changes the diaper, the baby goes back to sleep.
The term ‘distributed process’ originated in computer science. There, it means that no single unit, such as a router in a data network, knows what all the others are doing and tells them what to do. Instead, interactions between each unit and its local connections add up to the desired outcome. Distributed processes often operate in parallel rather than in series. An assembly line works in series: the handle of the car door must be put on before the door is installed, and the door can’t be installed until the person who puts on the handle has finished. In a parallel process, different steps can be done at the same time. Suppose each worker built a car from beginning to end. Then if one worker takes a little longer to put on the door handle on one car, this will not affect when the next worker can install the door on their car. If all the tasks are relatively simple, parallel processes go much faster than serial ones. This is true of computers, in which the logic gates perform very simple tasks, creating electrical versions of 1s and 0s. Compared with processing in series, parallel processing makes it possible to accomplish far more elaborate operations in a short time.
Because data networks, such as the internet, are undergoing very rapid growth, distributed processes are attracting great interest. But they entail a fundamental departure from systems based on central control: for many distributed algorithms, the outcome is not completely predictable. Although it’s possible to say what will happen on average, what will happen in particular cases can’t be specified precisely. Such uncertainty is inimical to the hearts of engineers who love things to work the same way every time. That engineers value predictability is a good thing for all of us who cross bridges and travel in airplanes. But distributed processes have distinct advantages for certain kinds of engineered systems, such as large data or electrical networks, in which the failure of one tiny part is not critical. They create redundancy at the expense of efficiency, and sacrifice precision for solutions that are good enough most of the time.
Distributed processes also have analogues in nature. In the 1970s and ’80s, as computer scientists saw the value of distributed processes in programming, they began to point out the analogies with natural systems. Douglas Hofstadter’s influential book Gödel, Escher, Bach (1979) used ant colonies and brains as metaphors for computer systems. David Rumelhart, another computer scientist, extended this idea to neural networks, models that explain how parts of a brain might work using parallel distributed processes. Now, scientists are studying distributed algorithms throughout nature, from circuits formed by neurons in brains or the interactions of metastasising cancer cells, to the movement of a flock of starlings or school of fish.
Ants can show how distributed processes might allow us to adjust to a changing environment; to build nests, decide when to move, or change from working inside the nest to foraging outside. It is becoming clear that the ant colonies’ algorithms are diverse, in interesting ways. Similar processes are at work in other natural systems without central control. For example, although certain large regions of the brain seem to be involved in particular tasks, at the level of neurons it looks like division of labour is not the rule. The same neurons are involved in different tasks, and the same task can be accomplished by different neurons.
It can be very difficult to let go of the idea of division of labour. Humans have always used arguments about supposedly intrinsic attributes to justify social roles. Kings ruled by divine right and ancestry, while others were slaves based on race or physical attributes. Such ideas pervade the rhetoric of US society and politics. We are told that Mexicans are rapists and Muslims are terrorists and, from the other side, a much more benign version but deriving from a similar philosophical stance: that Americans are optimistic and energetic.
Such explanations, relying on intrinsic attributes rather than relations and circumstances, also dominate our views of nature. Last summer, for instance, a bride whose father had died asked the man who received her father’s transplanted heart to give her away at her wedding. It is the heart’s job to love, therefore her father’s feelings must reside in her father’s heart. Genetic determinism is another example. We say that disease, intelligence, psychosis, athletic ability and so on are ‘genetic’, as if inside a person’s cells there were little switches labelled ‘cancer’ or ‘paranoia’ or ‘endurance’. In fact stress, sunlight, exercise and similar influences can change which genes are turned off and on. Biologists are learning that what genes do depends as much on what is happening outside as well as inside the cell." (https://aeon.co/essays/how-ant-societies-point-to-radical-possibilities-for-humans?preview=true)