Business Success: Models for Adaptive Organizations

Prior articles offered complexity science, chaos theory, and evolutionary biology as models for understanding organizational dynamics in a volatile economy. Further exploration into various aspects of these models unveils concepts for improving organizational adaptability.

A Living Systems Modelchaos image

Leading an organization based on living systems requires an understanding of organizational evolution. “The focus shifts from what is to what is becoming, from structure to dynamics.”

The following steps describe the pattern of change in living systems:

1. Innovation
2. Complexification and Convergence
4. Bifurcation and Chaos

The steps originate in science but have a direct application in the corporate world. Their role and interrelation are critical to understanding adaptability in an emergent organization.


Innovation is essential for maintaining adaptability and resilience. The combination of advances in technology and globalization put pressure on many organizations to adapt or die. Both of these are seen as irreversible. And the speed at which they occur continues to increase. The impact of a high level of innovation is felt through the next step.

Complification and Convergence

As advanced technologies inject new information into the system, complexity increases. However, there are limits to the complexity an organization can handle. To accommodate increased complexity, new levels of organization must be created to control and coordinate the existing levels. As a result, an organization “always converges progressively toward more embracing and coordinated multilevel structures.”

Convergence is seen across the globe as many corporations are partnering, forming alliances, merging, and diversifying into multiple lines of business. Global business standards and regulations are a result of this phenomenon.

What happens when a global company reaches its limit of complexity is unknown. Based on the new science, the next step in the sequence may be chaos.

Bifurcation and Chaos

Scientists have known for decades that as complex systems evolve, chaos and uncertainty increase. Today, computer models are able to simulate the evolutionary path with mathematical precision. The models show the attractors that form the pattern of the evolutionary trajectory.

The evolutionary trajectory can be plotted to show a graphical pattern providing a visual depiction of an attractor. There are several types of attractors. A system that evolves toward a fixed point over time is defined by stable-point attractors; a cyclically recurring state is characterized by period attractors; and an emergent system of order is defined by strange or chaotic attractors. As chaotic attractors are plotted, a shape emerges that has definite boundaries and patterns. The beautiful shapes of the plots prove that chaotic attractors are neither arbitrary nor disorderly.

Bifurcation occurs when a complex system changes trajectory. It is characterized by a change in pattern and a shift from one set of attractors to another. In the real world, complex systems evolve out of a specific initial state until a pattern emerges. If the evolution comes to rest, the process is ruled by static attractors. If the patterns are cyclical, the system is regulated by periodic attractors. If neither of these occurs, the system is controlled by strange or chaotic attractors.

Strange or chaotic attractors are pervasive in our global economy. The recent collapse of the world’s financial markets and its domino effect around the world demonstrates this pattern. Catastrophic bifurcations are occurring as many large financial institutions seek equilibrium amid the chaos.

Chaotic attractors do not operate with total randomness. Scientific analysis has unveiled a subtle order that emerges. Complex systems self-organize through a natural phenomenon known as cross-catalytic cycles. Following periods of instability and chaos, these cycles allow complex systems to return to dynamic stability where they can grow and prosper.

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