This is the fourth in a series of information war-gaming posts. The content of each post in the series will be determined by the discussion that follows the previous post.
Links to the earlier articles in this series are listed at the bottom of this post.
The sixth misconception about social change is that in order to bring it about one must obtain power. This assumes, first, that what I have called negative power is a neutral force, and second, that social change originates in power centers. Both of these assumptions are false.
The worst evils of our political system come from the centralization of power, irrespective of who holds it. It is the most naïve kind of hero-villain thinking to imagine that a new face will change a system. The major organizations in our society have seen dozens of incumbents pass through their top positions without greatly affecting the oppressiveness of their fundamental patterning.
— from Earthwalk, by Philip Slater (Anchor Press/Doubleday 1974, page 153
In the most recent essay of this series I addressed the difference between flat and hierarchically-organized information systems. I chose to highlight the advantages of a flat (or distributed) network because the flat model offers the only possibility for effective resistance to the Great Jihad, which has thus far proved so notably successful against us in the current information war.
None of my arguments is intended to rule out hierarchical networks as a useful component in information warfare. Once a new paradigm has emerged within a distributed network, not only will the existing hierarchically-organized systems be altered from the bottom up, but new ones may arise to perform functions of which we are as yet only barely aware.
In this essay I’ll be looking further into the nature of flat networks and distributed information systems. I’ll return to hierarchical systems in future posts.
The video embedded below was made by Bill Cullison, and shows the formations generated by an enormous flock of starlings in the sky over San Rafael, California.
Take the time to watch the whole thing. It is an amazing and beautiful reminder of Hamlet’s assertion that there are more things in heaven and earth than are dreamt of in our philosophy.
The behavior of these birds en masse is, in a mathematical sense, chaotic — that is, it obeys the laws of cause and effect and is not random, but it is inherently unpredictable. We can observe it and chart it, but we cannot possibly comprehend it in any meaningful way.
And yet there is no intricate master plan which these starlings are carrying out. The coherent formations and movements we see are the product of a set of simple neural instructions implemented simultaneously in thousands of bird-brains.
The starlings are a vivid demonstration of a distributed information network in action.
How does such complex and coordinated behavior emerge from the simultaneous actions of such minimally-programmed brains?
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Until the advent of high-speed digital computing, the mathematical modeling of these flocks was not possible. But in the last thirty years the behavior of birds and other creatures that form flocks has been successfully simulated. In order for the computer algorithm to mimic avian behavior realistically, each “creature” must have the same set of programmed instructions. It also must be able to “see” only its nearest neighbors; that is, no bird in the flock can view the whole flock when adjusting the speed and direction of its flight.
One of the masters in this field is the software developer Craig W. Reynolds. He pioneered and perfected a model of flocking behavior by creating a computer program he calls “boids”. Mr. Reynolds employed three basic steering behaviors for his digital creatures:
Separation: steer to avoid crowding local flockmates Alignment: steer towards the average heading of local flockmates Cohesion: steer to move toward the average position of local flockmates
These instructions make intuitive sense, and a jet fighter pilot is surely using his own version of these algorithms to maintain position within a formation.
The resulting collective behavior of the boids effectively simulates flocks of birds or schools of fish. Mr. Reynolds explains the reasoning behind his algorithm:
Each boid has direct access to the whole scene’s geometric description, but flocking requires that it reacts only to flockmates within a certain small neighborhood around itself. The neighborhood is characterized by a distance (measured from the center of the boid) and an angle, measured from the boid’s direction of flight. Flockmates outside this local neighborhood are ignored. The neighborhood could be considered a model of limited perception (as by fish in murky water) but it is probably more correct to think of it as defining the region in which flockmates influence a boid’s steering.
In the boids model… interaction between simple behaviors of individuals produce complex yet organized group behavior. The component behaviors are inherently nonlinear, so mixing them gives the emergent group dynamics a chaotic aspect. At the same time, the negative feedback provided by the behavioral controllers tends to keep the group dynamics ordered. The result is life-like group behavior.
A significant property of life-like behavior is unpredictability over moderate time scales.
Members of a distributed network have a limited neighborhood of perception. It’s not necessary for any of them to have the overall picture for the system to function effectively. In fact, if enough individuals within the system were to react to the overall picture instead of to their local neighborhoods, the network would cease to be a distributed one, and its collective behavior would disintegrate. It’s possible that it would subsequently reform into a hierarchical network — those having the overall view of the system would take their places near the top of the hierarchy.
A hierarchical network is powerful, but it is also slow and unwieldy. In order to have a rapid response time and maximum flexibility, those of us who act within a distributed network must learn to live with unpredictability.
It’s only human to want to see the big picture, and to control it. But we need to be willing to give up this idea if we are ever to be successful in the information war.
So how does the analogy of the boids apply to our present circumstances?
The same three behavioral traits — separation, alignment, and cohesion — can be generalized for the elements of any distributed network. No particular person in the Counterjihad will formulate these behaviors — when successful steering rules appear, they will be replicated and spread spontaneously throughout the system.
The process of replication occurs when a node in the network — a human being sitting at a computer, just like you or me — reads a set of premises and conclusions that make sense, that resonate deeply within his psyche. The information is then internalized and passed on to other nodes by any available means.
When enough individual nodes are operating with the same instructional base, i.e. using similar steering behaviors, the entire flock will become a coherent formation and begin to act effectively. None of us will understand it all, yet it will work.
When I churn out ideas and display them here, I’m just repeating things I’ve picked up in other places. My material may be synthesized and reorganized idiosyncratically, but it’s not new. If what I say makes sense to you, then you in turn will spread it further within the network.
None of the ideas is original, and no one has any control. These are tough principles to accept, but necessary ones.
When this process matures, the behavior of the entire network will change. I call this phenomenon “distributed emergence”.
It will be the primary engine of the Counterjihad.
Previous Posts about Info War-Gaming:
|2007||Dec||16||An Evolutionarily Stable Strategy|
|22||War-Gaming in Cyberspace|
|29||All Information Warfare is Local|