Chaos theory in organizational development. How do organizations evolve? Who generates the demand to organize such relationship structures? What the causality in effects on the Organizational Structure. To consider these one must 

Self-organization

Self-organization, as opposed to natural or social selection, is a dynamic change within the organization where system changes are made by recalculating, re-inventing and modifying its structure in order to adapt, survive, grow, and develop. Self-organization is the result of re-invention and creative adaptation due to the introduction of, or being in a constant state of, perturbed equilibrium. One example of an organization which exists in a constant state of perturbation is that of the learning organization, which is "one that allows self-organization, rather than attempting to control the bifurcation through planned change." (Dooley, 1995) Being "off-balance" lends itself to regrouping and re-evaluating the system’s present state in order to make needed adjustments and regain control and equilibrium. By understanding and introducing the element of punctuated equilibrium (chaos) while facilitating networks for growth, an organization can change gears from "cruise" to "turbo" in regard to speed and intensity of organizational change. While maintaining an equilibrial state seems to be an intuitively rational method for enabling an organization to gain a sense of consistency and solidarity, existing on the edge of a chaotic state remains the most beneficial environment for systems to flourish develop and grow.

For instance, two competing organizations that differ in regard to their levels of homeostasis will not be in competition for long. Generally speaking, the organization with the less-stable structure will come out ahead while the constant stability of the latter will eventually lead to its own demise. Although quite similar, small differences in homeostasis levels are enough to make a tremendous difference in future outcomes for each organization. The notion of similarity in origin vs. dissimilar results comes to fruition with the emergence of bifurcation

Bifurcation

The concept of bifurcation cannot be explained without discussion of the term frequently labeled "sensitivity to initial conditions." Sensitivity to initial conditions refers to the high level of importance of primary conditions from which the future path and direction of a system stems. This sensitivity to initial conditions is commonly referred to as the "Butterfly Effect," in which a butterfly flaps its tiny wings in one end of the world which results in a typhoon or hurricane somewhere else on the globe. While this is an entertaining notion, sensitivity to initial conditions remains in reality a very abstract concept without the presence of bifurcation, which is mathematically labeled as the actual splitting point of two near-identical entities which, due to the sensitivity of initial conditions, tend to take two very distinct paths and result in two totally different geographically or even evolutionary places.

Imagine dropping two identical coins from your fingertips off a 25-story balcony at the same time. Unless they are glued together, they will each take a different path towards the ground. Even though the force of gravity determines their general direction and speed, a host of uncontrollable variables such as wind and dust particles affect each coin independently. The infinitesimal and perhaps unidentifiable difference in starting conditions exponentially amplifies the effects of all other variables encountered which then feed back and add even more variation to the system resulting in very different paths taken to the ground. The moment the two coins split paths is known as the bifurcation point. The importance of this point lies in its implication of change and new direction.

Self-similarity

So what do the ANTS have to do with this

Swarm Intelligence: A Whole New Way to Think About Business

For years, scientists have been studying ants, bees, and wasps because of the amazing efficiency of social insects. Now companies like Southwest Airlines and Unilever are actually putting that research to work, with impressive paybacks.

by Eric Bonabeau and Christopher Meyer

A little more than a year ago, Southwest Airlines was having trouble with its cargo operations. Even though the average plane was using only 7% of its cargo space, at some airports there wasn’t enough capacity to accommodate scheduled loads of freight, leading to bottlenecks throughout Southwest’s cargo routing and handling system. At the time, employees were trying to load freight onto the first plane going in the right direction—a seemingly reasonable strategy. But because of it, workers were spending an unnecessary amount of time moving cargo around and sometimes filling aircraft needlessly.

To solve its problem, Southwest turned to an unlikely source: ants.Specifically,researchers looked at the way ants forage, using simple rules, always finding efficient routes to food sources. When they applied this research to Southwest’s problem, they discovered something surprising: it can be better to leave cargo on a plane headed initially in the wrong direction. If, for example, they wanted to send a package from Chicago to Boston, it might actually be more efficient to leave it on a plane heading for Atlanta and then Boston than to take it off and put it on the next flight to Boston.

Applying this insight has slashed freight transfer rates by as much as 80% at the busiest cargo stations, decreased the workload for the people who move cargo by as much as 20%, and dramatically reduced the number of overnight transfers. That’s allowed Southwest to cut back on its cargo storage facilities and minimize wage costs. In addition, fewer planes are now flying full, which opens up significant opportunities for the company to generate new business. Thanks to the improvements, Southwest estimates an annual gain of more than $10 million.

Similar research into the behavior of social insects has helped several companies, including Unilever, McGraw-Hill, and Capital One, to develop more efficient ways to schedule factory equipment, divide tasks among workers, organize people, and even plot strategy.

Just what valuable insights do ants, bees, and other social insects hold? Consider termites. Individually, they have meager intelligence. And they work with no supervision. Yet collectively they build mounds that are engineering marvels, able to maintain ambient temperature and comfortable levels of oxygen and carbon dioxide even as the nest grows. Indeed, for social insects teamwork is largely self-organized, coordinated primarily through the interactions of individual colony members. Together they can solve difficult problems (like choosing the shortest route to a food source from myriad possible pathways) even though each interaction might be very simple (one ant merely following the trail left by another). The collective behavior that emerges from a group of social insects has been dubbed “swarm intelligence.”

What Is Swarm Intelligence?

To be sure, many business gurus have overused biological metaphors, spinning clever stories to explain the past woes or successes of companies by using analogies from the life sciences. But the emerging field of swarm intelligence goes far deeper. Over the past 20 years, we and other researchers have developed rigorous mathematical models to describe the behavior of social insects, and we are now applying those techniques to business issues. As evidenced by Southwest and other early adopters, the preliminary results have been promising.

In essence, we believe that social insects have been so successful—they are almost everywhere in the ecosphere—because of three characteristics:

• flexibility (the colony can adapt to a changing environment);

The Bees and the Ants

Shared Environment

2.1. Two Ways to Communicate

As far as we can tell, information travels from one person's mind to another's by way of the physical world. One person affects the world, and another perceives it. Sometimes the effect is vibrations in the air, and another hears it; sometimes the effect is marks on a piece of paper, which another might see. More recently, with computer systems, the intermediate effects might be tiny electrical currents or magnetic domains, but the result is the same: people are generally able to convey their thoughts, with some degree of accuracy and at some cost, to other people.

Specific techniques for communication have varied over the centuries and with circumstances. Today, we have an enormous range of options, from the most ancient to the latest gadgets. Among all these, there remains a strong split between techniques which involve a persistent effect on the environment and those which are by nature transitory. Shouting out a warning is transitory; erecting a big red sign is persistent.

This division is not absolute: the erected sign might immediately collapse, and the shouted warning might echo for several seconds, but the qualitative difference remains. Persistent messages are issued with much less idea of and control over who will receive the information and with less opportunity for feedback and continued interaction. Balancing these losses, persistent messages can gain a much larger audience across time and space, and can reach some audiences at a much lower cost (such as with a posted warning). For the receiver, getting information from persistent messages offers the freedom, control, and simplicity of wandering in a bookstore, where obtaining transitory information requires complex social interactions to locate informed people and get them to communicate.

The differences between communicating by making persistent changes to the environment versus just "sending a message" are everywhere. In designing distributed or multi-threaded computer applications, developers weigh these same two approaches for arranging how the computer processes communicate, calling them "message-passing" and "shared-memory". With message-passing, they imagine a process constructing a digital message and transmitting it to one or more receivers. People using computers behave similarly when they send e-mail. With shared-memory, by contrast, an area of storage is allocated where one or more processes can place information for others to later see (and perhaps modify). People using their computers communicate like this when they author and read web pages.

In fact, this distinction goes back long before human society or even the human species. For tens of millions of years, bees and ants have each lived in communities with social structure involving essential division of labor and communication of information necessary to survival. In each of them, scouts are tasked with finding food sources and reporting back; they must inform others where to go and gather food. This architecture obviously allows much greater efficiency than having each worker do their own scouting.

The bees and the ants use different communication techniques, however. Honey bees use direct communication: the scout does a "dance" in which particular body movements indicate the direction and distance to the discovered food source. Ants, on the other hand, modify their environment and leave a persistent message: the scout, on the way back home after finding some food, activates a scent gland and drags it on the ground. This creates a coded trail for the gatherers to use reach the discovery.

There are clearly scaling advantages for the ants: workers returning from other jobs learn about other food sources immediately, without needing someone to repeat the directions for them. The directions can also be much more complex, involving numerous twists and turns. Of course one cannot leave long-lasting scent trails in moving air, so the bees, in their different environment, do what they can.