Connectivism as Swarm Intelligence

We have now reached a level of sophistication in our signalling and communications that we can compare our networks, our trails forming abilities, with those of ants. The traceries, the scent trails laid down that enable a colony’s survival, providing way-finding pathways to sources of sustenance, demarcating territory, identifying threats, are much like the recorded electronic linkage we have developed as a means to connect and map sources or nodes of knowledge, to set boundaries and to protect our networks. In ants, “simple osmotropotaxic scent following is not only sufficient to allow for trail following behaviour…, but is sufficient to produce evolution of a complex pattern of organized flow of social insect traffic.” (Erik M. Rauch, Mark M. Millonas, Dante R. Chialvo. 1995. Pattern formation and functionality in swarm models. Physics Letters A. Elsevier Science. p. 189.).

In the information age, knowledge is our daily bread, and the discovery of a rich source of information is similar to a communal feast. As word spreads, as node access levels increase and are documented across network connections and displayed on more interfaces, the strength or importance of the source of knowledge increases. Like ant trails, these pathways can also, decay. Browsing the Internet, bookmarking media, linking and embedding content, and sharing this knowledge collectively, pinpoints and heightens the activity around nodes of interest. I see considerable similarity between these activities, whether for the purposes of learning and education or otherwise, as swarm intelligence and the behaviour of colony insects. It is the “emergent collective intelligence of groups of simple autonomous agents” (Yang Liu and Kevin M. Passino. 2000. Swarm Intelligence: Literature Overview. p. 1.) where the agent is a subsystem that interacts, relatively independently, with its environment and probably other agents in a stigmergic relationship. According to Gerardo Beni, “swarms can undergo a transition from non convergence to convergence as their degree of partial synchronicity diminishes, i.e. as they get more disordered.” (Gerardo Beni. 2005. Order by Disordered Action in Swarms. p. 153. Swarm Robotics.).

There are a number of applications where swarm intelligence is being used, including optimization problems in combinatorial mathematics and network routing (B.D. Shirodkar, S.S. Manvi, A.J. Umbarkar. May, 2009. Multicast Routing for Mobile Ad-Hoc Networks using Swarm Intelligence. International Journal of Recent Trends in Engineering, Vol. 1, No. 1.), construction algorithms using self-organization and stigmergy and in cooperative transport tasks. Currently, principles in swarm intelligence are being used to develop social networking environments, such as PittCult, a swarm events calendar (Deb Smit. November 19, 2009. Pop City.


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