I've written a number of times that social software is powerful because it makes natural social processes more efficient. If we already gravitate towards 'webs' of contacts in the real world, search and database technologies simply allow those webs to become bigger. If we already rely upon certain friends' movie recommendations, collaborative filters such as Yahoo's Movie Recommendations simply expands our scope. Those are all farily 'explicit' social processes, though. We often 'specify' professional contacts by exchanging business cards, for example, and one way or another we decide whose advice to rely upon for movie recommendations. What about 'implicit' social processes -- what could we learn about social software by looking for those out there in the world?
A neat example springs to mind. Imagine a sizeable plot of empty land, and imagine further that you're assigned to prepare it for public use. You'd probably like to maintain as much healthy (read: un-trampled) 'green space' as you can manage, but some paving will be necessary. How are you going to organize the plot? Maybe you'll try a grid pattern because on-average, a grid ensures the most efficient path between any two points. But you'd realize that most people will decide to 'cut across' sections of the grid when doing so seems expedient. So anticipating this, maybe you'd pave a tighter grid than you had first planned. The process would repeat and soon you might conclude that paving the entire plot is the only solution. But that can't be right.
Actually, some planners capitalize on social processes to avoid precisely this problem. They don't lay any pavement at all for the first season, waiting instead to interpret the most trampled areas as paths favored by the public. By paving those paths, irregular though they may be, planners have begun to intuit that they can minimize most people's travel-time while saving the remaining grass from excessive abuse. Some even say the streets of Boston were actually fashioned after cowpath ruts worn hundreds of years ago, although there's some disagreement there. More recently, I understand that the paths along University of Maryland's McKeldin Mall (pictured below) were defined by the worn footpaths of travelling students after a snowstorm. Notice the irregularity of McKeldin's paths. Maybe it's fair to consider these plots which have bubbled-up from users to planners to be examples of social urban planning.
For those who will allow my stretched metaphor to stretch just a little farther, let's consider the results of planning a campus mall or an entire city by implicit expression. I have visited UMD a number of times, and I've made my way across the mall fairly quickly. I have also had the pleasure of driving in Boston, which has uniformly been a disaster. Accustomed as I am to Philadelphia's regular grid pattern, missed turns in Boston have sometimes almost doubled my travel time.
Maybe the key insight here is that social processes -- implicit and explicit -- are most powerful when constrained in scope (although not necessarily in scale). McKeldin mall is intuitive enough to navigate because the vast majority of travellers move between similar start and endpoints (say, dormitories in one general direction and lecture halls in the other general direction). But the logic of wayward tourists in Boston has exceptionally little to do with that of long-dead cowhands, so the result of that social collaboration is spectacularly inefficient.
So perhaps, for many applications, social software optimally considers a limited scope of information. Such a tendency might explain the Balkanization we're seeing on some social software platforms. One recent stat notes, for example, that Google's Orkut is populated by greater than 41% Brazillians, versus less than 25% Americans. That would be great news for those of us who try to value these platforms based on growth within constrained user demographics (Doostang, TheFaceBook). It might be damning for those of us who hope to grow the scope of user bases by means of viral marketing (MySpace, 360).
In the coming weeks, it will be important to consider how well that jives with my earlier prediction of a unified profile space.
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