Creation of a subset within a Social Network with a specific focus on v 2.0 of the 'Dunbar’s limit' that is dynamic in nature and continually evolves
Amongst other things, I've also been thinking about the Dunbars limit. I think this area, or rather ‘the logic encapsulated within this theory’ needs further research. So, that the main tenet being proposed can be broken down analytically, reconstructed and reapplied to a wide and varied dataset. With the eventual goal of having these newly defined concepts, for them to be reapplied in new and unique ways. I am thinking ‘influence’ (1:1 ratio) and how that can be monetized at the very least.
Now, it seems to me that, some subset of the 150 people that we can have stable relationships with, that this subset and the subset within this subset, that they keep evolving on an ongoing basis. If that even makes sense. Maybe the second visual below will make some sense.
What I mean by evolution in this respect, is that the individuals within some (if not all) of these subset, that they keep changing and are constantly being replaced by other individuals on an ongoing basis. That there are different gradients within each of these categories.
It true, I would suspect that this can be attributed to the new and emerging forms of communication. Whether it be social media, new ways of getting work/projects done etc.
So if we can visualize what I'm actually saying right now, then what I’d do is to look at the total number of people that an individual could have some kind of association with.
Since I'm not an expert in this field, what I've done is to simply come up with some simple categorizations. For simplicity’s sake, let’s go with the frequency of communications between an individual, let’s call this individual Jane and the group that Jane would interact with.
Here is how I would visualize these very interactions. Obviously these numbers are made up.
Hence, this (above) could be considered as a very basic model and framework for depicting a logical breakdown of Jane’s association with others, in her network. The percentages would be governed by the recency and frequency of the various interactions.
But, what if this framework could then be broken down into different layers. A sample visualization has been provided below with my very limited photoshop skills at play.
Now these layers would contain data relating to the frequency and recency of interactions with each and every individual that Jane would interact with. We don’t care if this interaction is in the offline world (in-person, phone e.t.c) or the digital world. As long as it can be measured, it can then be applied within the constructs of the system being discussed.
Now you may ask, what's the benefit of such a model? Well, for starters, such a model would allow the ability to calculate, with some level of precision, the ‘influence’ individuals have over one another.
Aggregating the sum total, of this influence, in it's different forms, may also allow us to have a better gauge over an individual's influence over a group or groups.
But, in a Donald's Rumsfeldian way, one would have to be mindful of the fact that we can't measure what we can't measure. As in, offline communications that cannot be measured, but may have powerful influence, relating to one individual over another.
Overall, it begets the question why would you want to do something like that? As in, measure influence that individuals would have over one another in their own group settings.
To go back to the very instance, where I came up with the thought of connecting the different gradients (hypothetical) and layers (hypothetical) within the Dunbar’s limit and connecting them with the spheres of influence (also hypothetical). I think that's where the money is.
We have now come to a point where we can measure the influence an individual can exhibit over their network (Linkedin, Klout e.t.c). I think the time has come, to be able to measure these 1:1 interactions and to then be able to aggregate and measure them holistically.
I can think of a lot of different ways this mechanism for measuring influence can actually be monetized. Some examples that I can think of right now:
I can think of many other ideas along these categories. But the time has come to call it a day and hit the gym. Overall, this idea needs more time, research and thinking. As frequency and recency alone are not a good indicator of measuring influence.
By the way, I've been working on a project in this area that I have just elaborated upon. Something that could potentially morph into a subset of this very idea. A very tiny subset.
Details to follow.