McAfee's table below summarizes the potential benefits, candidate technologies, and type of emergence at each ring of the bullseye (in other words, for each type of tie), and highlights some important differences at each of the four levels.
Tie Strength | Potential Benefits | Technology Example | What is Emergent? |
| Strong | Collaboration, Productivity, Agility | Wiki | Document |
| Weak | Innovation, Non-redundant information, Network bridging | Social Networking Software | Information |
| Potential | Efficient search, Tie formation | Blogosphere | Team |
| None | Collective Intelligence | Prediction Market | Answer |
One of the aspects of social networking that has become better understood since McAfee produced this model is the phenomenon of asymmetric follow - where there may be a gross imbalance between a person's inbound ties and outbound ties. This is particularly evident with such platforms as Twitter, but has also helped people to see through the illusion of symmetrical friendship on other networks such as Facebook. This means that strength of tie may not be symmetric - I may slavishly follow the most trivial thought of some celebrity, but this may not be reciprocated.
The most obvious example of strong ties is where a relatively cohesive group of people have a common interest and stake in some activity, which is reflected by their proximity in the social networking topography. As McAfee suggests, the wiki provides a pretty good platform for this kind of collaboration. It is also interesting to note how successful large public wikis can sometimes be - notably Wikipedia, which is surprisingly good in some areas and unsurprisingly bad in others - despite the lack of strong ties between most of the participants.
McAfee's simple topography raises some interesting dynamic questions.
- how do ties develop over time - at what point does a weak tie become a strong-enough tie to migrate to a different kind of platform
- how does the strength of a given set of ties respond to key events - the most obvious event being that someone changes allegiance - for example, moving from one company to another, or the company itself is taken over or enters a strategic partnership with a competitor - thus requiring a whole set of relationships and associative trust to be recalculated
- how are weak signals transferred between circles - for example, I may not pay attention to a given forum, because I believe that one of my close colleagues is active on that forum and will let me know if anything significant comes up
- how do we pay attention to the network itself - designing the network that best suits our specific situation and requirements
- or alternative, to what extent does it make sense not to be too calculating about the network, to put stuff in with uncertain confidence that someday somehow one will be repaid
There are also some interesting questions about how these networks work for different kinds of issue. Prediction markets are all very well for relatively straightfoward closed questions (“How many units of this product will we sell next quarter?” “What will our market share be at the end of the quarter?” “Will our competitor release their product on time?” “Will we release our product on time?”) - which can be understood as attaching probabilities to specific future events. Prediction markets are not so obviously applicable for addressing more complicated or complex or chaotic matters, where the events we are trying to predict are not amenable to being listed on a voting form.
Clearly there is some relationship between the strength of social ties and the strength of trust (see my post on Social Proximity and Trust), but we can also link these factors with the intensity of knowledge (Amin and Cohendet) and the strength of organizational coupling (Weick, Hagel). The critical question now is how to mobilize all these networks and tools and platforms in the service of individual and organizational intelligence.
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