Complex Adaptive Systems — Simple Rules, Unpredictable Outcomes
The concept of managing through a series of simple rules is an intriguing one. Could magic in our organisations occur through less rather than more control? Complex adaptive systems show how this might happen.
Complex Adaptive Systems are systems that use simple rules to generate complex behaviours. Take the series of lights in the video link below. They are set up with a very simple set of rules that adjusts the timing based on the behavior of the lights around it. With this simple set of rules a coherent pattern emerges remarkably quickly. And this emergent behaviour occurs without being explicitly programmed, without a whole forest of technical or overbearing rules. As Wheatley writes in “Leadership and the New Science”
“we need to be able to trust that something as simple as a clear core of values and vision, kept in motion through continuous dialogue, can lead to order” (Wheatley, 1994, p. 174).
Peter Martin Dickins PhD published online on complex adaptive systems :
Complex adaptive systems (CASs) are made up of agents, like nurses and patients, that learn and that relate to each other and the environment in nonlinear ways. A key result of this pattern of interaction is self-organization. Complex adaptive systems organize themselves in fairly stable patterns of relationship that are not governed by hierarchical intent. Such a pattern could be how nursing assistants, nurses, and nurse managers interact in a nursing home.
Emergent properties are a second result of these interactions. Emergent properties are characteristics of the system — like the well-being of patients or infection rates — that cannot be completely understood by knowing the characteristics of the systems parts. (Anderson & McDaniel as cited Cited by Dickins )
In a post on Beyond Intractability, Burgess writes how:
One of the simplest adaptive systems is a flock of birds. We have all watched in amazement the graceful and coordinated movements of a flock of birds. Yet there is no bird-in-chief directing the action. There is no script distributed to each bird prescribing the actions of the flock. However, this collective behavior can be modeled very nicely. In these models, individual birds have a degree of decision-making capacity, but all the flight decisions must follow the simple rules. Each must:
- avoid hitting neighbours or obstacles,
- align flight to match the neighbours, and
- fly an average distance from the neighbours.
From these simple rules, very complex flocking behavior proceeds.
The simple algorithm that ants use to construct a bridge is another example of a complex adaptive system from nature. Researchers have been able to model this behavior in simulations using two simple rules:
- The first tells the ant that when it feels other ants walking on its back, it should freeze. “As long as someone walks over you, you stay put,” This same process repeats in the other ants: They step over the first ant, but — uh-oh — the gap is still there, so the next ant in line slows, gets trampled and freezes in place. In this way, the ants build a bridge long enough to span whatever gap is in front of them. The trailing ants in the colony then walk over it.
- The second rule — As individual ants run the “bridging” algorithm, they have a sensitivity to being stampeded. When traffic over their backs is above a certain level, they hold in place, but when it dips below some threshold — perhaps because too many other ants are now occupied in bridge-building themselves — the ant unfreezes and rejoins the march.
Critical to an understanding of complex adaptive systems is understanding the difference between complicated and complex systems. I have explored this previously in a blog on Dave Snowden’s CYNEFIN framework
Of particular interest to me is the un-ordered “Complex” domain which Snowden describes in some detail in an excellent HBR article as having the following characteristics:
It involves large numbers of interacting elements. The interactions are nonlinear, and minor changes can produce disproportionately major consequences.
The system is dynamic, the whole is greater than the sum of its parts, and solutions can’t be imposed; rather, they arise from the circumstances. This is frequently referred to as emergence.
The system has a history, and the past is integrated with the present; the elements evolve with one another and with the environment; and evolution is irreversible.
Though a complex system may, in retrospect, appear to be ordered and predictable, hindsight does not lead to foresight because the external conditions and systems constantly change.
Unlike in ordered systems (where the system constrains the agents), or chaotic systems (where there are no constraints), in a complex system the agents and the system constrain one another, especially over time. This means that we cannot forecast or predict what will happen.
Leaders who don’t recognise that a complex domain requires a more experimental mode of management may become impatient when they don’t seem to be achieving the results they were aiming for. They may also find it difficult to tolerate failure, which is an essential aspect of experimental understanding. If they try to overcontrol the organization, they will preempt the opportunity for informative patterns to emerge. Leaders who try to impose order in a complex context will fail, but those who set the stage, step back a bit, allow patterns to emerge, and determine which ones are desirable will succeed.
An important aspect of adaptive systems is that of emergence. Taken again from Peter Martin Dickins PhD published online on complex adaptive systems in the health industry, the author writes how:
…“order is an emergent property of disorder and it comes about through self-organizing processes operating from within the system itself”….“instead of being designed from the top down, the way a human engineer would do it, living systems always seem to emerge from the bottom up, from a population of much simpler systems” … This begins to define emergence as a property of living systems. Emergent properties are ones that “exist at one level of the organization that cannot be explained by understanding properties at other levels of the organization” …
[these are] in the form of new structures, patterns or processes that are unpredictable from the components that created them.
I have previously written about complex adaptive systems in the context of the Kanban system which has 5 core principles:
- Visualize the workflow
- Limit WIP
- Manage Flow
- Make Process Policies Explicit
- Improve Collaboratively (using models & the scientific method)
The Principles of the Kanban Method are designed to lay the foundation for an organization that can improve incrementally by setting out the conditions that will stimulate improvement. The improvements are emergent behavior. The outcome cannot be predicted. All that can reasonably be predicted is that things will change
Swarm intelligence is another aspect of emergent behavior. The advantage of swarm intelligence include
- Flexibility: the group can quickly adapt to a changing environment.
- Robustness: even when one or more individuals fail, the group can still perform its tasks
- Self-Organization: the group needs relatively little supervision or top-down control.
In this 2001 article on HBR the authors discuss the concept :
Social insects work without supervision. In fact, their teamwork is largely self-organized, and coordination arises from the different interactions among individuals in the colony. Although these interactions might be primitive (one ant merely following the trail left by another, for instance), taken together they result in efficient solutions to difficult problems (such as finding the shortest route to a food source among myriad possible paths)
The concept of managing through a series of simple rules is an intriguing one. Maybe instead of hundreds of operating instructions and procedures, magic in our organisations could happen through less rather than more control? Complex adaptive systems show how this might happen.
Let me know what you think? I’d love your feedback. If you haven’t already then sign up for a weekly dose just like this.
You might also like:
- Boundaries of failure — Rasmussen’s model of how accidents happen.
- Systems Archetypes- Places to intervene — An advantage with using systems archetypes as a problem solving methodology is that places to intervene in the system can be thought through and played with.
- Swarm Intelligence — Can managers develop simple rules to shape the behaviour of their organizations and replace rigid command-and-control structures?
- Kanban your work — The Kanban method is in the Agile suite of tools that can help you visualise and prioritise work.
