Rules for "Flocking Behavior" in the Web

additional material by Bryan Coffman


Editor's Note:

This material was developed by a team during the recent MG Taylor internal DesignShop® event, Being There, held at our Cambridge, MA knOwhere store. The team was challenged to develop a set of simple rules for members of our network to follow, the collective result of which would lead to a set of desired emergent behaviors displayed by the MG Taylor extended web as a whole.


Step One: "Boids Do It"
For well over a decade now, scientists from many different disciplines have used the computer to experiment with metaphors and algorithms to help us understand how life and all of its complexity has emerged, survived and evolved. Some of these silicon-based art forms have taken on such convincing and surprising behavior, that they have acquired the general name of Artificial Life. At one level, these algorithms appear as games; the famous game of Life is one example, while the Maxis corporation game, SimCity is another. Alternately they appear as a bottom-up approach to robotic intelligence that views robots as a cooperating and competing system of components instead of some all-powerful, centralized decision-making brain struggling for omniscience. A-Life techniques have been applied to solve intractable mathematical puzzles, and have even been used by software to write and evolve its own code. (For a layman's description of the history of A-Life, read Steven Levy's Artificial Life; for a slightly more advanced but very rewarding look at the field, read Christopher G. Langton's Artificial Life: An Overview; for a description of software that anyone can use to design A-life systems, read Mitchel Resnick's Turtles, Termites and Traffic Jams)

In the mid 80's Craig Reynolds applied the principles of A-life to the phenomenon of birds flying in coordinated flocks. The challenge was to uncover simple rules that each bird (or boid) could follow that would produce flocking as an emergent behavior. Flocking is not a quality of any individual bird; it only emerges as a property of a group of birds. Each bird acts as an independent Agent and obeys the simple rules. Reynolds identified three simple rules for each boid to follow within some given global parameters, and the result was an uncanny facsimile of flocking behavior unfolding itself on his computer screen. The phenomenon has been applied repeatedly on screen in SIGGRAPH animations, for the penguins in Batman and the Wildebeest stampede in Lion King. To see the Java code behind boids, view an 8th grader's science project (Jonathan Robbins).

The general assumptions of the model are:

  • Each boid has an ability to sense local flockmates (sensory apparatus)
  • Each boid can sense the whole environment (3D space)
  • All boids recalculate their current state (velocity vector) simultaneously once each time unit during the simulation

The rules of flocking behavior are (according to Reynolds):

  1. Separation: steer to avoid crowding local flockmates.
  2. Alignment: steer towards the average heading of local flockmates.
  3. Cohesion: steer to move toward the average position of local flockmates.

The Boids Come Home to Roost:
Application to Managing the
Business of Enterprise:
The industrial management paradigm has relied on top down control of all activities within an organization. And for good reason. At the birth of the 19th century factory, a single engine would drive all of the factory's processes through a system of interconnected belts. Everyone had to perform their job with numbing precision or they could bring the entire factory to a halt. People were filling roles for which machines had yet to be invented. Roles that required no independent thought and forbid independent action and decision-making. Whole systems of education, policy, culture, and livelihood coalesced around this operating principle. (This is admittedly a gross simplification, but will suffice for purposes of this article.) We are still struggling under this load of behavioral detritus. One of the purposes of Transition Management from MG Taylor Corporation's vantage point is to remove this stigma from the human pursuit of life and health, and return to men, women, and children around the globe the joy of right livelihood within the full breadth of their creative, collaborative capacities. To return a sense of vision, mission, destiny, and power. This is not fantasy, but merely a question of will, discipline and love, as it has been from the beginning. It has been proven.

In order for an enterprise to avoid falling into a strictly hierarchical, bureaucratic model by default, a set of rules must be found that can be employed by each Agent in the system (whether a customer, producer, or investor) so that the enterprise emerges through these countless interactions: This in juxtaposition to creating a mechanical shell of an organization from the top and then filling it with compliant employees. The Agent-based system would allow temporary hierarchies (we call them ad-hocracies) to coalesce about the nuclei of wealth-generating projects for varying durations. The structure emerges as a result of the autocatalytic interaction of the enterprise's network, or web.

The web flocks as an evolutionary imperative to survive and thrive. The rules evolve and so does the emergent behavior, but there is no one-to-one correlation between the rules and the emergent properties.

Our team took a first cut at what these rules might be. The most difficult thing to keep in mind during the process was the "nonlinear" relationship between the rules that the Agents (or Knodes, as we refer to Knowledge Nodes in our network) would follow, and the resulting emergent properties of the network, or enterprise as a whole. For example, if diversity is an emergent feature we wish to foster, it's tempting to build diversity into the behavior of individual Knodes. It's much more difficult to try to think through what types of individual behavior might result in collective diversity.

We had no opportunity, due to time and systems constraints, to devise a means of testing our hypothesis, either in silico, in vitro or in vivo. For this work to gain more value, its premises should be subjected to further scrutiny, simulation and testing.

The Emergent Behavior We Seek
We imagine that a healthy, emergent ValueWeb™ enterprise will exhibit, among others, these properties:

  • Diversity of Knodes and products
  • Ability of Knodes to cycle between being producers, customers, and investors within the web
  • Knodes will find themselves being cross-utilized in the web across a wide range of projects and product lines
  • The web as a whole tells its own story; speaks for itself; and every Knode can learn this story, or song
  • The web generates wealth
  • The web is self-correcting
  • The web is anticipatory
  • The web is sustainable (these last three properties come from the Appropriate Response model)
  • The web develops structure around certain interactive group sizes:
    • The "Active, Passionate Flock" or cell, (72)
    • The "DesignShop Flock", (5025)
    • The "Mother Flock", (125-300) (Above this level, competition for resources tends to stress the local environment, creating a natural splitting and division of the flock into smaller groups)

A First Cut at the Rules
These are the things that each Knode decides for itself:

  • What the "local neighborhood" is (a collection of investors, producers, consumers of some interactive group size. Each Knode may have several overlapping or non-overlapping neighborhoods to which it belongs).
  • What to value--a personal definition of "wealth".
  • It's current "state": skills, values, blocks, activity, anticipation.

These are the rules that each Knode follows:

  1. If another Knode does something you value, emulate it.
  2. Send regular "here's my current state" messages--communicate your current state to your neighborhood.
  3. Hunt for and discover resources and opportunities.
    1. Once discovered, communicate these as a part of your current state message.
  4. Look outward to identify new ideas, toys, tools and techniques.
    1. Once identified, communicate these as a part of your current state message.
  5. Tell the story of the purpose of the web to new Knodes you run into (which may or may not be a part of the current web/flock that you belong to).
  6. Family and immediate neighborhood come first.
  7. Critical mass:
    1. If the neighborhood flock is too small for critical mass, join another one.
    2. If the neighborhood flock is too large and noisy, split off.
  8. Align with and converge upon the currently highest wealth-generating opportunity (from your value vantage point) that you are aware of, based on current state messages you receive from other Knodes.
  9. Always ship a product from your experiences to your local neighborhood. A product is actually a specific subset of a current state message.
  10. Reply to current state messages you think are valuable, even if it's just to say, "thanks, that was valuable." DON'T respond to current state messages which you think are not valuable.

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