In my early foray into computer graphics in the late 80s I came across Symbolics, a spinoff of MIT AI Lab doing (among other things) research in advanced visualization. I was dumbfounded by this video, premiered by Symbolics at SIGGRAPH 1987. How could they achieve their flock of birds to move in such a natural-looking way? At the time it looked like sorcery: I was a humble economics student in a small town in Italy, with not a chance in hell to grasp the extension of the knowledge wielded by MIT computer whizs. So I put it away in a corner on my mind. Until, in 2009, I chanced on a 1992 book, Mitchell Waldrop’s Complexity, that actually knows the answer to my 22-year old question. Each bird or fish in the flock follows three simple rules of behaviour:
- It tries to maintain a minimum distance from other objects in the environment, including other birds/fish (Symbolics’ Craig Reynolds called them “boids”).
- It tries to match velocities with nearby birds/fish.
- It tries to move toward the perceived center of mass of nearby birds/fish.
The natural-looking flocking behaviour is emergent. As far as the program is concerned, there is no entity called flock: it is just moving about individual boids. Simple rules for local interaction among them produce an elegant and effective collective behaviour.
Wait a minute. This is not so different from what is happening in Kublai. Example: we wanted the community to go and say hello to new members. Of course you cannot issue a decree that this is to happen. So what we did was this: Walter and I, who are friends and also particularly active community members, agreed that we would do it, created a Welcome Group and started doing just that. This produced some sort of flocking behaviour: our “net neighbours” (at least some of them) started imitating us, and joined the group. Soon they developed a more effective way to keep track of who was doing what (after some trial-and-error Pico proposed a widget which everyone was happy with), and their net neighbours started following their example… including the initiators!
Communities are, by definition, impossible to control: but they are certainly possible to influence. This is no rocket science: most of us have some experience of it. This flocking behaviour intuition, if confirmed by analysis, could lead to developing techniques for influencing (not sure “managing” is the appropriate word) social networks based on establishing “islands” of local interactions where certain rules apply, and watching them spread out through the network’s links. Of course where you start matters: it just so happens that Walter and I are by far the most eigenvector central people in Kublai, according to Ruggero.
I am wondering whether this mechanism could somehow help us understand why people seem “too eager” to collaborate in social networks, and why, conversely, oppurtunistic behaviour is a lot less widespread than one would be inclined to think (this remark run in several talks at Public Services 2.0). Cooperation as an emergent property of networks, as opposed to an intrinsic property of individuals?