Dana Meadows' book, "Thinking in Systems," is probably one of the best books ever written about the concept of systems thinking. The book offers many insights, tools, and methods of systems thinking for solving problems ranging from personal to global.
This is a book that I would highly recommend to anyone who is interested in a primer on systems thinking, and systematic analysis of problems. The book covers a wide range of important concepts with extreme clarity.
In this blog we summarize the concepts of resilience, self-organization, and hierarchy, as described in section 3 of Meadows' book, titled "Why Systems Work So Well."
We quote directly from the book for the most part and end with a mindmap of the concepts covered.
Resilience is a measure of a system's ability to survive and persist within a variable environment. The opposite of resilience is brittleness or rigidity.
Resilience arises from a rich structure of feedback loops that can work in different ways to restore a system after a large perturbation. A single balancing loop brings the system back to its desired state. Resilience is provided by many such loops, operating through different mechanisms, at different time scales, and with redundancy.
A set of feedback loops that can restore or rebuild feedback loops is resilience at a higher level - meta-resilience, if you will. Even higher meta-meta-resilience comes from feedback loops that can learn, create, design, and evolve ever more complex restorative structures. Systems that can do this are self-organizing.
Resilience is not the same thing as being static or constant over time. Resilient systems can be very dynamic. Short-term oscillations, or periodic outbreaks, or long cycles of succession, climax and collapse may in fact be normal condition, which resilience acts to restore.
Conversely, systems that are constant over time can be unresilient. The distinction between static stability and resilience is important. Static stability can be seen and measured by variation in the condition of a system week by week, or year by year. Resilience is something that may be very hard to see, unless you exceed its limits, overwhelm and damage the balancing loops, and the system structure breaks down. Resilience may not be obvious without a whole-system view. Resilience is sometime sacrificed for stability, or productivity, or for some other immediately recognizable system property. For example, just-in-time deliveries of products to retailers or parts to manufacturers have reduced inventory instabilities and brought down costs in many industries. At the same time, the just-in-time model also has made the production system more vulnerable, to perturbations in fuel supply, traffic flow, computer breakdown, labor availability, and other possible glitches.
Systems need to be managed not only for productivity or stability, they also need to be managed for resilience - the ability to recover from perturbation, the ability to restore or repair themselves.
Awareness of resilience enables one to see many ways to preserve or enhance a system's own restorative powers.
The most amazing characteristic of some complex systems is their ability to learn, diversify, complexify, and evolve. This capacity of a system to make its own structure more complex is called self-organization. You see self-organization in a small, mechanistic way whenever you see a snowflake suddenly forming a garden of crystals. You see self-organization in a more profound way whenever a seed sprouts, or a baby learns to speak, or a neighborhood comes together to oppose a toxic waste dump.
Like resilience, self-organization is often sacrificed for purposes of short-term productivity and stability. Productivity and stability are the usual excuses for turning creative human beings into mechanical adjuncts to production processes. Or for narrowing the genetic variability of crop plants. Or for establishing bureaucracies and theories of knowledge that treat people as if they were only numbers.
Self-organization produces heterogeneity and unpredictability. It is likely to come up with whole new structures, whole new ways of doing things. It requires freedom and experimentation, and a certain amount of disorder. These conditions that encourage self-organization can be scary and threatening to power structures. As a consequence education systems may restrict the creative powers of children instead of stimulating those powers. Economic policies may lean toward supporting established, powerful enterprises rather than upstart, new ones. And many governments prefer their people not to be too self-organizing.
Systems theorists used to think that self-organization was such a complex property of systems that it could never be understood. New discoveries, however, suggest that just a few simple organizing principles can lead to widely diverse self-organizing structures. Common examples are structures that are formed according to the rules of fractal geometry.
Out of simple rules of self-organization can grow enormous, diversifying crystals of technology, physical structures, organizations, and cultures.
In the process of creating new structures and increasing complexity, one thing that a self-organizing system often generates is hierarchy.
The world, or at least the part of humans think they understand, is organized in subsystems aggregated into larger subsystems, aggregated into still larger subsystems. A cell in your liver is a subsystem of an organ, which is a subsystem of you as an organism, and you are a subsystem of a family, a social group, and so forth. These groups are subsystems of a town or city, and then a nation, and then the whole global socioeconomic system that dwells within the biosphere system. This arrangement of systems and subsystems is called a hierarchy.
If subsystems can largely take care of themselves, regulate themselves, maintain themselves, and yet serve the needs of the larger system, while the larger system coordinates and enhances the functioning of the subsystems, a stable, resilient, and efficient structure results.
Hierarchies not only give a system stability and resilience, they also reduce the amount of information that any part of the system has to keep track of. In hierarchical systems relationships within each subsystem are denser and stronger than relationships between subsystems. If the differential information links within and between each level of the hierarchy are designed right, feedback delays are minimized. No level is overwhelmed with information. The system works with efficiency and resilience.
Hierarchical systems are partially decomposeable. They can be taken apart and subsystems with their especially dense informtion links can function, at least partially, as systems in their own right. When hierarchies break down, they usually split along their subsystem boundaries. Much can be learned by taking apart systems at different hierarchical levels and studying them separately.
Hierarchies evolve from the lowest level up. - from the pieces to the whole, from cell to organ to organism, from individual to group, from actual production to management of production. The original purpose of hierarchy is to always to help its originating subsystems to do their jobs better. This is something, unfortunately, that both the higher and the lower levels of a greatly articulated hierarchy easily can forget. Therefore, many systems are not meeting our goals because of malfunctioning hierarchies.
When a subsystem's goal dominate at the expense of the total system's goals, the resulting behavior is called suboptimization. Just as damaging as suboptimization, of course, is the problem of too much central control.
To be a highly functional system, hierarchy must balance the welfare, freedoms, and responsibilities of the subsystems and total system - there must be enough central control to achieve coordination toward the large system goal, and enough autonomy to keep all subsystems flourishing, functioning, and self-organizing.
Read Dana Meadows' "Thinking in Systems."