Swarm intelligence is a term popularised in the research on artificial intelligence. It denotes a unified and focused behaviour pattern of elements in the environment. A typical example of swarm behaviour is found in Bees, Ants, Wasps, Birds flying in a typical pattern, animals following a certain path during migration and bacterial growth. In all these examples there is no central intelligence or central control unit directing the behaviour but a collectivised emergence of logical and intelligent route formed through random behaviour pattern which is unknown to the individual entities.
Based on the intuitively developed Swarm Intelligence it was Craig Reynolds who developed for the first time the Boids Program- a term coined from the bird-oid object. The Boids Program studied the simulated pattern of flocking behaviour of artificial birds. The birds were, through a set of algorithms made to follow a set of rules as follows:
- separation: steer to avoid hitting other birds
- alignment: steer towards the average heading of other birds
- cohesion: steer to move towards the centre of the mass
Based on the above logic and model several experiments were conducted to understand the swarm behaviour in a group. Whichever was the element that was used be it ants, bees or robots one thing surprising was the emergence of a unique and predictable set of behaviour. This finding has profound implications for understanding human group behaviour. I will explain later how this connects to the central theme of this article. The principles of swarm intelligence are already in use in telecom networks, airports and in defence. A seemingly simple sociometry of ants collecting the food and getting back to its anthill has been used to map flight routing, clustering, scheduling and optimization of the available space at the airports. Taking the same example of ants, we notice several principles that have been effectively deployed in many situations:
- Flexibility: The ants are quick to take remedial measures to internal and external changes/disturbances. Try throwing some water in the anthill and you will notice what I write.
- Robustness: Even if some ants die due to heat or accident the others effectively take over and the assigned task gets completed.
- Scalable: Very quickly the ants assemble if the object that needs to be moved is big and they continue till they succeed.
- Self-organised: The roles are not given but assumed. The results are not pre-defined.
- Adaptation: In the event of a new threat the ants quickly regroup and respond to the stimuli effectively.
- Speed: Any quick mid-course correction is very effectively communicated to other members.
- Modularity: Each unit is independent of each other but acts in complete unison.
Now let us try to understand the concept of Stigmergy using the same example of ant behaviour. How does one ant follows the other? How it develops the internal coordination mechanism so that the food far from the anthill is taken to its final destination? The ants follow the traces left by the earlier ant and try to follow the same path. The ants also pause near each other and they smell the other ant for information exchange. In this process, they emit pheromones and the path where the traces are left forms a complex path of the network which is followed by all. The network then results in a newly formed external memory or intelligence. This concept of ant colony optimisation is effectively used in computer science. In humans, bacterial behaviour also follows the same pattern. Even the bodily cells follow the complex path of travel within the body. The cancerous cells in the body also follow exactly the same way as ants.
https://warwick.ac.uk/fac/cross_fac/complexity/study/emmcs/outcomes/studentprojects/credi.pdf
The above study carried out at the University of Warwick studied the collective behaviour and stigmergy of cancer cells and concluded “Increasing experimental and theoretical evidence suggests that malignant tumours can exhibit a range of collective patterns similar to evolved adaptive behaviour found in other biological systems, including collective decision making and collective exploration of the micro-environment.”
How can we as a homogeneous society comprising of responsible citizens collaborate to achieve the common goals? Can we learn from the ants and the bacteria and the cells that seemingly have a seamless and tension-free way of realising their goals? This kind of stigmergic collaboration between different groups of individuals having different ideologies and viewpoints is possible. This calls for a decentralised environment where each member respects the other and collaborates to achieve the common good. A classic example would be a Wiki page or open-source software or the latest addiction named WhatsApp. Here comes the concept of Blockchain. Blockchain eliminates middlemen or centralised monitoring and goes for distributed collaboration. In social groups, the success of collaboration can happen if accurate sociometric feedback is provided in a timely fashion. Sociometric feedback is a real-time visualization of the quantitative measurement of social interactions. Sociometric feedback helps distributed group members have a better understanding of the members that are not co-present. Moreover, persuasively designed sociometric feedback can control the direction of change in the communication pattern of groups, so that the change can lead to a performance increase.
To conclude in these troubled times where social conflicts are the order of the day and differing points of view are dividing societies across the globe and the so-called central leadership, rather than dousing the passions and increasing the hate, communities have to learn to have their own swarm intelligence, avoid centralised agencies to guide and monitor them and follow each other exactly like the ants or the cells in our body. That is the only way to main individual and societal harmony.
