Posts Tagged ‘Emergence’

The Holy Grail for complex organizations experiencing high risk is finding a balance between stability and flexibility. This presents a very real challenge since the environment is almost always shifting and the team has to think on its feet because time, money, people, and other resources are limited. There isn’t enough time to cycle up to senior management and back down to the team.

The previous two blogs presented linear models of success and failure that are inadequate in complex situations but which are still alive and well in many organizations. They are also limited in term of being either fixed solely on the individual (Domino model) or top-down in terms of policies and procedures (Barrier model).

This blog starts the process of looking at a more realistic model for addressing success and failure in dynamic situations, the Functional Resonance Accident Model (FRAM) developed by Hollnagel. Its roots are in complexity theory and it comprises four principles:

  • Equivalence of success and failure. Successful teams rely heavily on anticipatory awareness, i.e., paying close attention to the environment as it is, without expectations. They perform early-warning weak signal analysis, and decide how best to organize for the situation. An anesthesia team might best characterize this behavior. Guiding medical principles are present but the number of hard-and-fast rules is low compared to how much the anesthesia team must monitor the surgery and think on their feet constantly assessing the entire situation while simultaneously monitoring details. Failure can occur when the team temporarily losses this ability.
  • Approximate adjustment. The team is constantly adjusting its performance to suit the situation. This includes adapting to shifts in resources as well as unique requirements for the specific task at hand. Imagine your elderly, sick grandmother is staying with you and she is very sensitive to excess heat but also chills easily. You have an air conditioner that can maintain 75°F indoors in direct sunlight only if the outside temperature is below 95°F.  On days forecast to be hotter than 95°F what do you do? You must gauge what time in the morning to turn the thermostat below 75°F. How low do you turn down the temperature? At what time do you do it? Does it vary with the afternoon forecast? Could she chill with the setting you’ve chosen? Answering these questions from day to day is making an approximate adjustment in the presence of limited resources and high risk.
  • Emergence. The constant adjustments in performance means there is constant variability. This variability can have a compounding effect, which is non-linear and disproportionately large. New behaviors can emerge. A tipping point can be reached. Think of the impact one failed safety relay has had on the electrical grid in the United States. Whole areas have been plunged into darkness.
  • Functional resonance. A whole constellation of variables can show emergent behavior and impact each other, causing a particular function in a system to resonate without there being one direct, cause-and-effect relationship to which one can point. Think of the speed with which Google grew initially or sales of the iPad or the initial impact of Palm. Failure can emerge as well. Think of Palm’s sales for the last few years before being bought by HP. In a different area, look at how the functional resonance of political dissent has changed in the Middle East. Have changes in communications had an impact?

In principle you can see that FRAM is much more robust than the Domino or Barrier models covered in previous blogs. It goes well beyond the individual or attempts to create all-encompassing policies and procedures. It addresses the dynamics of the situation, which keeps it grounded. We will go deeper into the FRAM model in the next blog.

Chaos and Complexity #5: Chaos vs. Complexity

by Gary Monti on October 12, 2010

What is the difference between chaos and complexity? Many of the previous blogs have referred to both terms. While related, they are distinct. Here they will be differentiated.

Chaos vs Random

First, let’s look at what chaos is and isn’t. In everyday language chaos and randomness are considered synonyms. In chaos theory they are very different.

Random refers to a lack of structure at any level. No intelligence or pattern can be discerned.

Chaos does have observable patterns present. Chaos refers to the unpredictable behavior a deterministic (rules-driven) system displays. Chaotic systems are non-linear. This means small changes might produce a large change at certain times (tipping points). At other times a chaotic system can display remarkable robustness and remain intact when being hit with many, substantial impacts. There are other characteristics associated with chaotic systems, which we will explore in later blogs. For now, one more characteristic will be addressed which leads into the development of complex systems – emergence.

Emergence and Adaptation

Emergence is the appearance of patterns or intelligence arising from the interactions of components at a granular level. The most important distinction with emergence is the bottoms-up rather than top-down development of patterns. The resulting patterns can’t be predicted but they can be capitalized upon, amplified, and used to push adaption.

Adaptation is a transformative modification of the initial system, i.e., the system one ends up with can be different from the one started with. A good example of this is the map of Europe before and after World War II. The war began with England and France’s response to Germany’s invasion of Poland. The initial goal was the preservation of the sovereignty of Poland. In the end the German’s were defeated but Poland was lost behind the Iron Curtain. Notice how the adaption can have beneficial effects but may not necessarily result in the desired goals being met. This is a good example of the riskiness associated with working in the realm of chaotic systems. It still is better than trying to work in a deterministic fashion on a dancing terrain. Do you remember CompuServe? It had a chance to buy AOL, felt satisfied with being the big dog in business computing, stuck to a linear model, failed to adapt, got bought by MCI and now is a part of Verizon’s network.

Complexity

Complex systems are a special type of chaotic system. They display a very interesting type of emergent behavior called, logically enough, complex adaptive behavior. But we are getting ahead of ourselves. There’s a need to back up a bit and describe a fundamental behavior that occurs at the granular level and leads to complex adaptive behavior. It is self -organization.

Self Organization occurs when the individual components in a chaotic system come together to work as a team to achieve the desired goal. Remember the non-linear component of chaotic systems? This applies during self-organization and means teams may form, work for a while then fall apart and reconstitute in a different form when an obstacle is met to keep on moving forward.

Complex Adaptive Behavior is the name given to this forming-falling apart-reforming-falling apart-… behavior. Specifically it is defined as many agents working in parallel to accomplish a goal. It is conflict ridden, very fluid, and very positive. The hallmark of emergent, complex adaptive behavior is it brings about a change from the starting point that is not just different in degree but in kind. In biology a good example of this is the emergence of consciousness. Another example is the Manhattan Project and the development of the atomic bomb.

Back to Linearity

The development of a complex system within a chaotic situation has a big plus. Complex systems can cross over into predictability where the newly developed rules work, e.g., the actual development and delivery of the atomic bomb. Remember the equilibium-disequilibrium talked about in the previous blog?

We now have a good basis for moving forward. In future blogs we will draw upon both the vocabulary and frame-of-mind presented here to look at how one leads in chaotic situations.