Following from my recent post on the subject it occurs to me that, not only might it be an interesting study to find out how learning infects social groups, it is quite possible that we might use knowledge so gleaned to improve learning throughout a community. For instance, if we can identify ‘promiscuous learners’ (sorry, might need a less value-laden term!) then it might make sense to concentrate our educational efforts on them. Essentially, these would be people with a large number of social ties, strong ones if possible, what Malcom Gladwell calls Connectors. It is very likely that they might have other interesting shared characteristics too.
Incidentally, I think that Gladwell’s ideas on how social epidemics occur are quite relevant to my developing thesis, but are also quite distinct. This is not just about the spread of ideas, memes or fashions. It is about the spread of knowledge and the growth of understanding, not just the transmission of information. Learning changes us and consequently changes those around us, but not necessarily in the same ways that we have changed. This series of posts illustrates this fairly well, albeit coarsely: the direct inspiration comes from a paper about the spread of obesity, but pulls in ideas and connections from many other things that interest me. In selecting the aspect of the paper that had personal relevance (almost certainly not the main thing the authors wanted to convey) I was infected by the learning but not (directly) the content of the paper.
This way of seeing things is helping me to make more sense of the distinctions between groups, networks and collectives that Terry Anderson and I have been writing about lately.
Networks are ultimately concerned with (often weak but sometimes strong) one-to-one connections between the individual nodes of the system and it is at this level that transmission of infection may be most likely to occur. I suspect that this is also where mutation may be most likely to happen: the stronger the mutual link, the more likely it is that shared interests and common goals will lead to more harmonised learning. Weaker and more occasional ties increase the diversity and range of knowledge within the greater system.
Links may often be much stronger in one direction than another but still cause infection. For example, I very much doubt that Howard Rheingold sees me as part of his network but, through subscribing to Smart Mobs, I see him as, at least in a sense, a part of mine. This asymmetry again opens up the system to greater mutation, and seems increasingly common as our networks extend through social software. A well-connected node may communicate with many other nodes, but seldom with the intention of singling out a particular set of identified individuals. Indeed, the node may have no awareness at all of those who see him or her as part of their networks.
In contrast, communication in groups may sometimes be between individuals but it more typically follows a one-to-specified-many pattern. The spread of infection is therefore quite different and, significantly, quite contained within the group. Just as stronger one-to-one links in networks probably lead to greater alignment, there is likely to be greater homogeneity of the infection in a group. Groups may be seen as hothouses for cultivating specific kinds of learning (although this does not necessarily mean that the final form of that learning will be clear in advance).
It is not unusual for large groups to be composed of sub-groups, many of which overlap, leading to hybrid network-group entities. Equally, it is certain that almost all members of a given group will be networked with others, so networks again take infection out of the group and into the wild. It would be interesting to study groups with few or no network ties (perhaps some monasteries or isolated villages, for instance) to see what happens. I would hypothesise that, without the infectious agency of networking, they might run out of learning steam or, at the very least, lead to more homogenous and aligned learning than those with more connections.
Collectives are different again, and may have no equivalent in natural outbreaks of disease. Transmission in a collective occurs from the group to the individual, the group entity arising as a result of individual interactions, so any infection comes from the many, not the one. Where it gets interesting is in the implied recursion: the collective infects the individual, so the collective is composed of many infected individuals, which as a collective again reinfect themselves and so on ad infinitum. If we look at a massive collective system like Google, Amazon or Wikipedia, this gets really interesting: we might see them as causing a worldwide plague of learning, a positive recursive feedback loop not unlike that suggested in Gordon Pask‘s conversation theory, but where one of the actors is the system itself.
Collectives can co-exist peacefully with other forms and even arise from them. For instance, networks are often interesting when we look at the large-scale patterns within them. When we use a computer system to identify those patterns, it can (in principle) feed them back to the individuals who make up the network: i.e. the network becomes a collective, a cyborg composed of algorithms, connections and people. To an extent, anonymised voting within a group might serve a collective function. In effect, collectives increase the number of entities communicating within a system and, in so doing, increase the opportunities for infection and potential mutations along the way.
My ideas are still a bit fuzzy here, but I’m enjoying this intellectual excursion into the land of metaphor. Hope you do too.
By: Jon Dron
Posted: July 27, 2007, 10:37 am