Merriam-Webster defines the notion of hive mind as ‘the collective mental activity expressed in the complex, coordinated behaviour of a colony of social insects (such as bees or ants) regarded as comparable to a single mind controlling the behaviour of an individual organism’.
It is often seen as a form of intelligence that only exists by the assembly of numerous organisms, one that is not available to the individual.
Related terms include swarm intelligence (a term rather associated with AI, defining the behaviour of systems that are able to self-organize) and collective consciousness (a sociological aspect, referring to the shared understanding of moral attitudes and social norms).
This concept has its roots in nature, inspired by the fascinating lives of small insects and animals that live in large communities. Such is the case of bees, ants, locusts, even some birds and fish.
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One ant is not remarkable in its features. It has a small body, limited sight and can only perform a handful of functions – such as move around, carry objects, defend the nest or care for larvae.
Ant colonies, on the other hand, are amazingly efficient organizational structures. They can build not only living spaces, but also temporary structures such as bridges or rafts using their own bodies. They can explore vast amounts of space walking in simple trajectories just relying on sheer numbers. They can find and retrieve pieces of food, often much larger than themselves. The amazing fact is that they perform all these functions without a ruler or chain of command, that would guide them as to what needs to be done when and by whom.
Instead, each of the individuals follows a set of simple genetically encoded rules and only communicates with a few other ants that it may encounter during its relentless exploring. When all members follow the same set of simple rules, and have a general idea as to what needs to be accomplished, the swarm behaviour manifests itself – they seem to move like a single organism, arranging and rearranging their conformation so as to collectively succeed.
One form of swarm behaviour becomes apparent during foraging. The foundation for this essential function of the colony is simple: a foraging ant will leave a trail of pheromones on its path. If it finds a food source, it will reinforce this pheromone trail by walking it back to the colony. If not, the path will not be taken again, and the pheromones will soon evaporate.
Due to this form of indirect sharing of knowledge within the colony, a network of pheromone trails ultimately emerges, linking the nest to food sources.
This simple yet efficient exploration behaviour inspired programmers to write the Ant Colony Optimization algorithm (ACO). It is of use, for example, while trying to determine the shortest path that visits a number of cities.
In principle, a set of ‘ants’ starts one after the other in an attempt to construct a solution by traversing the map. Each ant stores its travelled distance. Progressively, a likelihood for each ant’s solution to be the optimal one is calculated, reminiscent of the pheromone trail. Ultimately, one ant obtains the shortest overall path.
This flexible algorithm has many applications, ranging from routing problems, to scheduling, or even determining a DNA sequence or a protein’s conformation.
Bees are another prime example of swarm intelligence, hence the ‘hive mind’ terminology. Their coordinated behaviour becomes especially clear when they need to move their hive to a new location. Bees must do so in a single, committed unit. As the hive is a fragile structure, this must be done with extreme caution, using a safe route, and relocating to an ultimately ideal location, protected from predators or extreme temperatures.
In order to determine this new route and destination, scout bees head out to explore the surroundings and identify potential new homes. Upon their return, each scout bee will try to prove that their determined path is the optimal one, by performing a series of movements termed the ‘waggle dance’. In doing so, they indicate the route they have found and may recruit other bees to advocate for their site.
Iterating these dances and head-butting the competing scouts act as stop-signals, meant to inhibit all other bees until one bee and its followers, representing one route, wins. There is no overarching decision maker, just bees interacting with bees until they reach a consensus, guided by a common ultimate goal.
This observation prompted scientists to consider that ‘hive mind’ may prove to be more than a metaphor. An interesting 2012 paper draws a parallel between how decisions are made in a beehive and how they are achieved in our brains. Much in the way scout bees dance in order to inhibit other scout bees and deem their route as optimal, different populations of neurons representing different alternatives in a particular situation inhibit each other until a single course of action is chosen.
While humans do not exhibit typical swarm behaviour, there is a subconscious wish for alignment with the community in all of us. Our most astonishing advancements and our gravest downfalls were, ultimately, achieved collectively. How far this collaborative mindset can go might prove hard to believe.
Over the centuries, humanity has experienced a few episodes of dramatic yet ill-understood instances of mass hysteria. While it is such a rare occurrence that psychiatric guidelines do not treat it as a disease per-se, tales of these episodes give the feeling that we are not, in fact, as individualistic as we’d like to believe.
Obvious examples are those of the dancing plagues. In 1374, in dozens of French medieval towns, people experienced an unexplainable compulsion to dance. They would gather in groups and dance continuously for hours or days on end, seldom pausing to eat or rest. Some literally danced to exhaustion, the craze causing several deaths over its course. After weeks of extending to large areas of France and the Netherlands, it eventually subsided on its own.
More than 100 years later, strange similar episodes began to be documented in nunneries across Europe. Nuns were thought to be possessed, as they behaved like animals, racing around, jumping out of trees, or clawing their way up tree trunks. Over the next 200 years, these delusions only grew in intensity and content, disproportionately affecting nuns as per the accounts of many physicians, chroniclers, monks, and priests of the time.
Various suppositions as to what may have caused these peculiar occurrences were raised over time – from dancing cults, to the ingestion of hallucinogenic substances, and ultimately a bizarre psychological phenomenon: mass psychogenic illness, or mass hysteria.
What may prompt a collective descension into delusional states is hard to pinpoint. All above-mentioned communities did, however, experience particularly harsh living conditions. Be it extreme poverty, natural disasters, or the strict environment of nunneries, these people had to collectively face very stressful environments.
Mass hysteria is not necessarily a thing of the distant past either. Occurrences in schools and isolated communities, especially in the form of mass anxiety, have been described in the past century, and even in the past decade. Arguably, we may have even experienced a hint of it these past months, in the form of mass grocery shopping triggered by the Covid pandemic.
What could be the neurobiological foundation of this sense of collective consciousness - be it the adherence to social rules or, on a different level, the feeling of belonging to a community, of syncing with it to some extent?
Mirror neurons are a logical candidate. Thought to be the basis of empathy, they trigger reactions in our brain that emulate the feelings or actions of those around us, as if we were experiencing them ourselves. That they are able to somehow align our brain activity to match that of others, prompting a form of synchronicity reminiscent of colonies or hives, may be an exaggeration. Nevertheless, they do make us social beings, best fit for communities, even while valuing our individuality.
Swarm behaviour, hive mind or collective consciousness, this fascinatingly efficient way of life of small units is eye-opening. It gives the sense of just how complex a concept intelligence is. It spans beyond the individual and reaches otherwise insurmountable heights at the collective level. How may we benefit from these insights?
One area which seems eager to take inspiration from this perspective of small, simple units achieving a common goal through iterations and plain interactions is that of robotics. In an interesting TED talk, Professor Radhika Nagpal shows how relatively uncomplicated robots can achieve complicated structures together. They do so without a supervisor or a view of what the ultimate arrangement needs to look like, guided just by interactions with their immediate neighbours and their surroundings.
This impressive behaviour, very much reminiscent of the way bees or ants act in nature, may pave the way for an entirely new direction in robotics, one that does not aim for intelligence in the form of a superior, single ‘brain’, but rather a decentralized, collective effort to follow a specific goal.
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