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Fig. – (Organizational Complexity trough History) Four forms behind the Organization and Evolution of all societies (David Ronfeldt TIMN). Each form also seems to be triggered by major societal changes in communications and language. Oral speech enabled tribes (T), the written word enabled institutions (I), the printed word fostered regional and global markets (M), and the electric (digital) word is empowering worldwide networks (N). [in David Ronfeldt, “Tribes, Institutions, Markets, Networks: A framework about Societal Evolution“, RAND Corporation, Document Number: P-7967, (1996). PDF link]
[…] Organizational complexity is defined as the amount of differentiation that exists within different elements constituting the organization. This is often operationalized as the number of different professional specializations that exist within the organization. For example, a school would be considered a less complex organization than a hospital, since a hospital requires a large diversity of professional specialties in order to function. Organizational complexity can also be observed via differentiation in structure, authority and locus of control, and attributes of personnel, products, and technologies. Contingency theory states that an organization structures itself and behaves in a particular manner as an attempt to fit with its environment. Thus organizations are more or less complex as a reaction to environmental complexity. An organization’s environment may be complex because it is turbulent, hostile, diverse, technologically complex, or restrictive. An organization may also be complex as a result of the complexity of its underlying technological core. For example, a nuclear power plant is likely to have a more complex organization than a standard power plant because the underlying technology is more difficult to understand and control. There are numerous consequences of environmental and organizational complexity. Organizational members, faced with overwhelming and/or complex decisions, omit, tolerate errors, queue, filter, abstract, use multiple channels, escape, and chunk in order to deal effectively with the complexity. At an organizational level, an organizational will respond to complexity by building barriers around its technical core; by smoothing input and output transactions; by planning and predicting; by segmenting itself and/or becoming decentralized; and by adopting rules.
Complexity science offers a broader view of organizational complexity – it maintains that all organizations are relatively complex, and that such complexity arises that complex behavior is not necessarily the result of complex action on the behalf of a single individual’s effort; rather, complex behavior of the whole can be the result of loosely coupled organizational members behaving in simple ways, acting on local information. Complexity science posits that most organizational behavior is the result of numerous events occurring over extended periods of time, rather than the result of some smaller number of critical incidents. […] in Dooley, K. (2002), “Organizational Complexity,” International Encyclopedia of Business and Management, M. Warner (ed.), London: Thompson Learning, p. 5013-5022. (PDF link)
The Internet has given us a glimpse of the power of networks. We are just beginning to realize how we can use networks as our primary form of living and working. David Ronfeldt has developed the TIMN framework to explain this – Tribal (T); Institutional (I); Markets (M); Networks (N). The TIMN framework shows how we have evolved as a civilization. It has not been a clean progression from one organizing mode to the next but rather each new form built upon and changed the previous mode. He sees the network form not as a modifier of previous forms, but a form in itself that can address issues that the three other forms could not address. This point is very important when it comes to things like implementing social business (a network mode) within corporations (institutional + market modes). Real network models (e.g. wirearchy) are new modes, not modifications of the old ones.
Another key point of this framework is that Tribes exist within Institutions, Markets and Networks. We never lose our affinity for community groups or family, but each mode brings new factors that influence our previous modes. For example, tribalism is alive and well in online social networks. It’s just not the same tribalism of several hundred years ago. Each transition also has its hazards. For instance, while tribal societies may result in nepotism, networked societies can lead to deception. Ronfeldt states that the initial tribal form informs the other modes and can have a profound influence as they evolve:
Balanced combination is apparently imperative: Each form (and its realm) builds on its predecessor(s). In the progression from T through T+I+M+N, the rise of a new form depends on the successes (and failures) achieved through the earlier forms. For a society to progress optimally through the addition of new forms, no single form should be allowed to dominate any other, and none should be suppressed or eliminated. A society’s potential to function well at a given stage, and to evolve to a higher level of complexity, depends on its ability to integrate these inherently contradictory forms into a well-functioning whole. A society can constrain its prospects for evolutionary growth by elevating a single form to primacy — as appears to be a tendency at times in market-mad America. [in David Ronfeldt, “Tribes, Institutions, Markets, Networks: A framework about Societal Evolution“, RAND Corporation, Document Number: P-7967, (1996). PDF link]
Finally, on these areas (far behind the strict topic of organizational topology and complex networks), let me add two books. One his from José Fonseca, a friend researcher I first met in 2001, during a joint interview for the Portuguese Idéias & Negócios Magazine, for his 5th anniversary (old link) embracing innovation in Portugal. His book entitled “Complexity & Innovation in Organizations” (above) was published in December that year, 2001 by Routledge. The other one is more recent and from Ralph Stacey, “Complexity and Organizational Reality: Uncertainty and the Need to Rethink Management After the Collapse of Investment Capitalism” (below), Routledge, 2010. Even if, Ralph as many other past seminal books on this topic. Both, have worked together at the Hertfordshire University.
[…] Dumb parts, properly connected into a swarm, yield smart results. […] ~ Kevin Kelly. / […] “Now make a four!” the voice booms. Within moments a “4” emerges. “Three.” And in a blink a “3” appears. Then in rapid succession, “Two… One…Zero.” The emergent thing is on a roll. […], Kevin Kelly, Out of Control, 1994.
video -‘Swarm Showreel’ by SwarmWorks Ltd. December 2009 (EVENTS ZUM SCHWÄRMEN – Von Entertainment bis Business).
[…] In a darkened Las Vegas conference room, a cheering audience waves cardboard wands in the air. Each wand is red on one side, green on the other. Far in back of the huge auditorium, a camera scans the frantic attendees. The video camera links the color spots of the wands to a nest of computers set up by graphics wizard Loren Carpenter. Carpenter’s custom software locates each red and each green wand in the auditorium. Tonight there are just shy of 5,000 wandwavers. The computer displays the precise location of each wand (and its color) onto an immense, detailed video map of the auditorium hung on the front stage, which all can see. More importantly, the computer counts the total red or green wands and uses that value to control software. As the audience wave the wands, the display screen shows a sea of lights dancing crazily in the dark, like a candlelight parade gone punk. The viewers see themselves on the map; they are either a red or green pixel. By flipping their own wands, they can change the color of their projected pixels instantly.
Loren Carpenter boots up the ancient video game of Pong onto the immense screen. Pong was the first commercial video game to reach pop consciousness. It’s a minimalist arrangement: a white dot bounces inside a square; two movable rectangles on each side act as virtual paddles. In short, electronic ping-pong. In this version, displaying the red side of your wand moves the paddle up. Green moves it down. More precisely, the Pong paddle moves as the average number of red wands in the auditorium increases or decreases. Your wand is just one vote.
Carpenter doesn’t need to explain very much. Every attendee at this 1991 conference of computer graphic experts was probably once hooked on Pong. His amplified voice booms in the hall, “Okay guys. Folks on the left side of the auditorium control the left paddle. Folks on the right side control the right paddle. If you think you are on the left, then you really are. Okay? Go!”
The audience roars in delight. Without a moment’s hesitation, 5,000 people are playing a reasonably good game of Pong. Each move of the paddle is the average of several thousand players’ intentions. The sensation is unnerving. The paddle usually does what you intend, but not always. When it doesn’t, you find yourself spending as much attention trying to anticipate the paddle as the incoming ball. One is definitely aware of another intelligence online: it’s this hollering mob.
The group mind plays Pong so well that Carpenter decides to up the ante. Without warning the ball bounces faster. The participants squeal in unison. In a second or two, the mob has adjusted to the quicker pace and is playing better than before. Carpenter speeds up the game further; the mob learns instantly.
“Let’s try something else,” Carpenter suggests. A map of seats in the auditorium appears on the screen. He draws a wide circle in white around the center. “Can you make a green ‘5’ in the circle?” he asks the audience. The audience stares at the rows of red pixels. The game is similar to that of holding a placard up in a stadium to make a picture, but now there are no preset orders, just a virtual mirror. Almost immediately wiggles of green pixels appear and grow haphazardly, as those who think their seat is in the path of the “5” flip their wands to green. A vague figure is materializing. The audience collectively begins to discern a “5” in the noise. Once discerned, the “5” quickly precipitates out into stark clarity. The wand-wavers on the fuzzy edge of the figure decide what side they “should” be on, and the emerging “5” sharpens up. The number assembles itself.
“Now make a four!” the voice booms. Within moments a “4” emerges. “Three.” And in a blink a “3” appears. Then in rapid succession, “Two… One…Zero.” The emergent thing is on a roll.
Loren Carpenter launches an airplane flight simulator on the screen. His instructions are terse: “You guys on the left are controlling roll; you on the right, pitch. If you point the plane at anything interesting, I’ll fire a rocket at it.” The plane is airborne. The pilot is…5,000 novices. For once the auditorium is completely silent. Everyone studies the navigation instruments as the scene outside the windshield sinks in. The plane is headed for a landing in a pink valley among pink hills. The runway looks very tiny. There is something both delicious and ludicrous about the notion of having the passengers of a plane collectively fly it. The brute democratic sense of it all is very appealing. As a passenger you get to vote for everything; not only where the group is headed, but when to trim the flaps.
But group mind seems to be a liability in the decisive moments of touchdown, where there is no room for averages. As the 5,000 conference participants begin to take down their plane for landing, the hush in the hall is ended by abrupt shouts and urgent commands. The auditorium becomes a gigantic cockpit in crisis. “Green, green, green!” one faction shouts. “More red!” a moment later from the crowd. “Red, red! REEEEED !” The plane is pitching to the left in a sickening way. It is obvious that it will miss the landing strip and arrive wing first. Unlike Pong, the flight simulator entails long delays in feedback from lever to effect, from the moment you tap the aileron to the moment it banks. The latent signals confuse the group mind. It is caught in oscillations of overcompensation. The plane is lurching wildly. Yet the mob somehow aborts the landing and pulls the plane up sensibly. They turn the plane around to try again.
How did they turn around? Nobody decided whether to turn left or right, or even to turn at all. Nobody was in charge. But as if of one mind, the plane banks and turns wide. It tries landing again. Again it approaches cockeyed. The mob decides in unison, without lateral communication, like a flock of birds taking off, to pull up once more. On the way up the plane rolls a bit. And then rolls a bit more. At some magical moment, the same strong thought simultaneously infects five thousand minds: “I wonder if we can do a 360?”
Without speaking a word, the collective keeps tilting the plane. There’s no undoing it. As the horizon spins dizzily, 5,000 amateur pilots roll a jet on their first solo flight. It was actually quite graceful. They give themselves a standing ovation. The conferees did what birds do: they flocked. But they flocked self- consciously. They responded to an overview of themselves as they co-formed a “5” or steered the jet. A bird on the fly, however, has no overarching concept of the shape of its flock. “Flockness” emerges from creatures completely oblivious of their collective shape, size, or alignment. A flocking bird is blind to the grace and cohesiveness of a flock in flight.
At dawn, on a weedy Michigan lake, ten thousand mallards fidget. In the soft pink glow of morning, the ducks jabber, shake out their wings, and dunk for breakfast. Ducks are spread everywhere. Suddenly, cued by some imperceptible signal, a thousand birds rise as one thing. They lift themselves into the air in a great thunder. As they take off they pull up a thousand more birds from the surface of the lake with them, as if they were all but part of a reclining giant now rising. The monstrous beast hovers in the air, swerves to the east sun, and then, in a blink, reverses direction, turning itself inside out. A second later, the entire swarm veers west and away, as if steered by a single mind. In the 17th century, an anonymous poet wrote: “…and the thousands of fishes moved as a huge beast, piercing the water. They appeared united, inexorably bound to a common fate. How comes this unity?”
A flock is not a big bird. Writes the science reporter James Gleick, “Nothing in the motion of an individual bird or fish, no matter how fluid, can prepare us for the sight of a skyful of starlings pivoting over a cornfield, or a million minnows snapping into a tight, polarized array….High-speed film [of flocks turning to avoid predators] reveals that the turning motion travels through the flock as a wave, passing from bird to bird in the space of about one-seventieth of a second. That is far less than the bird’s reaction time.” The flock is more than the sum of the birds.
In the film Batman Returns a horde of large black bats swarmed through flooded tunnels into downtown Gotham. The bats were computer generated. A single bat was created and given leeway to automatically flap its wings. The one bat was copied by the dozens until the animators had a mob. Then each bat was instructed to move about on its own on the screen following only a few simple rules encoded into an algorithm: don’t bump into another bat, keep up with your neighbors, and don’t stray too far away. When the algorithmic bats were run, they flocked like real bats.
The flocking rules were discovered by Craig Reynolds, a computer scientist working at Symbolics, a graphics hardware manufacturer. By tuning the various forces in his simple equation a little more cohesion, a little less lag time. Reynolds could shape the flock to behave like living bats, sparrows, or fish. Even the marching mob of penguins in Batman Returns were flocked by Reynolds’s algorithms. Like the bats, the computer-modeled 3-D penguins were cloned en masse and then set loose into the scene aimed in a certain direction. Their crowdlike jostling as they marched down the snowy street simply emerged, out of anyone’s control. So realistic is the flocking of Reynolds’s simple algorithms that biologists have gone back to their hi-speed films and concluded that the flocking behavior of real birds and fish must emerge from a similar set of simple rules. A flock was once thought to be a decisive sign of life, some noble formation only life could achieve. Via Reynolds’s algorithm it is now seen as an adaptive trick suitable for any distributed vivisystem, organic or made. […] in Kevin Kelly, “Out of Control – the New Biology of Machines, Social Systems and the Economic World“, pp. 11-12-13, 1994 (full pdf book)