Autonomic Social Networks

An intriguing-sounding but broken idea blogged by Judith Meskill on autonomic social networks.
The post applies ideas about self-healing computer networks — networks that automatically detect and detour around or repair damage — to our social and knowledge networks. Meskill quotes Christopher Meyer, coauthor of It’s Alive: The Coming Convergence of Information, Biology, and Business, “When we become adept at applying these insights to the social sphere, we’ll be able to run simulations that reveal, say, the conditions under which Iraq would reconstruct itself.”
A computer network goes down when a node stops routing packets. A human network is damaged when trust breaks down; when people feel hurt, afraid, and angry; and the group doesn’t have the will or the skill to repair the harm. It’s possible to heal human networks, but it’s a lot harder than swapping out a router.
The US government replaced Richard Nixon as president when his team was caught covering up election dirty tricks, but the distrust of government lasted for a generation. The “damaged node” was replaced, but the network didn’t “self-heal.”
Comparing humans to network nodes sounds intriging but is misleading.

One thought on “Autonomic Social Networks”

  1. >Comparing humans to network nodes sounds intriging but is misleading.
    Like any model, it’s only as accurate as the data you put in. A peer-to-peer network of pure trust relationships doesn’t take into account ‘forced’ trust relationships that exist when Alice has power over something like Bob’s employment – Bob *must* trust Alice within the bounds of her duties as employee. Also, no trust networks that I know of model subject-specific trust (ie. I trust Alice about good food but not good movies) or non-recipricatory trust (Alice trusts Bob a little, but Bob trusts Alice a lot)… and those are just model parameters that I came up with off the top of my head; I’m sure in-depth study would reveal more.
    Boils down to: Garbage in, Garbage out
    In theory, theory and practice are the same. In practice, they’re different.

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