Complexity arises from the inter-relationship, inter-action and inter-connectivity of components within a
system and between a system and its environment. However, we are not only concerned with complexity. We are also concerned with complexity that results in self-regulating, self-correcting and adaptive systems. For the purposes of MPI, we’re mostly concerned with the understanding of self-correcting, natural systems and the extent to which this understanding can be applied to man-made systems in order to make them better. Manmade systems includes our governments, corporations, social movements and our own personal projects. We would consider these projects to be better if they were self-correcting, self-regulating, self adapting and did not deplete the systems that they exist within or the systems that exist inside them.
The Santa Fe Institute in New Mexico conducts research into what it calls “complex adaptive systems” (“CAS”). CAS are dynamic systems that are able to adapt in and evolve with a changing environment. The study of CAS makes the important assumption that there is no separation between the system and its environment and the concept to be examined is that of a system closely linked with all other related systems making up an ecosystem. Therefore, within this context, change should be interpreted in terms of co-evolution with all other related systems, rather than as and adaptation to a separate and distinct environment.
CAS (and, for our purposes natural systems) tend to have the following common characteristics:
Distributed Control
This essentially means that the system has no leader or any centralized control mechanism that governs its behavior. However, there are many interactions and interrelationships that result in coherent behavior.
Connectivity
Complexity results from the inter-relationship, inter-action and inter-connectivity of the components within a system and between a system and its environment. Therefore, a decision or action by one component within a system will influence all of the other directly related components and may influence many of the indirectly related components.
Co-evolution
Co-evolution occurs when the components in the system change and adapt based on the interactions that they have with the other components in their own system and the system that they exist within.
Sensitive Dependence
CAS are sensitive. Small changes can have a profound impact on overall behavior.
Emergent Order
Complexity in complex adaptive systems creates the potential for emergent behavior. Examples of complex adapting systems include the economy, ecosystems, the human brain, developing embryos and ant colonies. These are systems that include a network of many agents acting concurrently. In an economy, the agents are consumers, corporations and governments. In an ecosystem, the agents are the various species. In a brain, the agents are neurons. In these systems, each agent exists within an environment produced by its interactions with the other agents in the system. There is constant action and reaction to what other agents are doing and therefore nothing remains static.
As all of the interactive components approach some form of equilibrium of some kind and a pattern or global order eventually emerges. This is often something that cannot be predicted by simply analyzing one isolated component and this why we need more holistic / systems thinking.
Control of a complex evolving system tends to be highly dispersed. For example, there is no governing
neuron in the brain and no species dictates the behavior of every other species in its environment. The overall behavior observed in the economy is a result of the cumulative decisions made by millions of individual people.
Order can result from non-linear feedback interactions between agents where each agent goes about his own business. For example, emergent behavior can be seen in the flocking behavior of birds. Research using computer simulations has shown that it is possible to model the flocking behavior of birds by using a small number of simple rules like the distance each bird maintains between itself and other birds and other objects. These rules are entirely local to each bird. There is no explicit rule required to form a flock. If a flock does form, it would have done so from the bottom up, as an emergent phenomenon. In fact, flocks did form every time the simulation was run and, therefore, it appears that self-organization is an inherent property of CAS.
Next article in this series: “Systems Theory & The Universe“