Systems Thinking

Complexity management is an alternative paradigm to our traditional management approach. If we are going to manage anything, we are going to have to have some representation or model of how it works. A paradigm is the fundamental conceptual framework or model we use for interpreting events. In science, there is fundamentally just two different paradigms or processes of reasoning – analysis and synthesis. Analysis is the standard process of reasoning used within mainstream science, management, and engineering, while synthesis is typically seen as an alternative and it forms the foundations to what we call systems thinking.


With the process of analysis, we build a model of something by focusing on that system, decomposing it to understand its constituent parts, the simple interactions between those elements, and then we form an account of the whole in terms of these parts. The process of doing this is called reductionism, because we are reducing the system to its most elementary parts. This is the standard approach taken by modern science and it is why, when we think of science, we think of things like atoms, molecules and people in labs that isolate things and take them apart to understand their properties in isolation.


In contrast to analysis, synthesis aims at understanding a system in relation to its environment

Synthesis, in contrast to analysis, is the process of reasoning whereby we try to understand something not by taking it apart but, instead, we try to look at the system in relation to its environment. So here, we are reasoning upwards instead of downwards. We build up a model of the system by understanding its connections to other things within its environment and by looking at the function or role that it plays within that network of connections that make up the context or environment. So, for example, instead of trying to understand something like a bird by taking it into the lab and dissecting it to analyze its anatomy, we would instead go out and look at the bird within its native environment, trying to understand its place and function within that ecosystem as a whole. So where analysis is characterized by the approach of reductionism, synthesis is characterized as being holistic. It is always referring to the whole context or environment in order to create a model of the system in question.

Both synthesis and analysis are equally valid processes of reasoning. We use them both all day every day. When we go to buy a car, we think about the properties of that car in isolation, its fuel consumption, the quality of its tires, the interior, etc. But we also think about how well it will be suited to the context within which it will be used, will we be driving it in the city or country, does my wife have a similar car or not etc. In order to properly build up a model of something, we really need to use both processes of reasoning.

We typically call synthesis and systems thinking an alternative approach because the modern era has been characterized by a dominance of the analytical scientific approach. For approximately the past 5 hundred years, since the scientific revolution and the work of Galileo, Newton and others, the reductionist approach has been the mainstream approach to modeling and interpreting our world. As such, it has formed the backbone of modern society’s collective body of knowledge. And during the industrial revolution, this body of scientific knowledge was applied to creating a more formal management approach, what we call scientific management.

The industrial revolution and the arrival of mass society necessitated a whole new approach to management as it became increasingly professionalized and specialized in response to having to deal with larger and more complicated forms of organization such as the modern nation state and large corporations.

Scientific management was an early management theory to come out of this period and gained widespread acceptance in analyzing and improving business organization and the workflows in factories. Scientific management is based on the work of Frederick Taylor who laid down the fundamental principles of large-scale manufacturing through the assembly line. It emphasizes rationalization and standardization of work through the division of labor. Frederick Taylor looked at each step of production – breaking those steps into sub-steps – and recorded exactly how much time and how much motion was necessary to complete each task. By reducing the amount of motion, the worker could get more done and productivity was increased.  It was an idea that fitted perfectly with industrial age mechanization. This approach to managing organizations that was born out of the industrial age has gone on to be applied to all areas of organization, and it remains the default approach that we inherit today. So we will quickly consider some of its advantages and limitations.


Firstly, reductionism breaks systems down into their individual parts and focuses on these well-defined components. So within management, this involves the dividing of an organization or process into categories, departments or stages. By doing this, we create component parts that can be easily isolated and measured.

By breaking a process up in this way, we are also able to isolate the organization’s components sufficiently to identify simple linear interactions of cause and effect. We can then use these simple cause and effect interactions to influence or control how the components function. We can measure its efficiency and control its output by manipulating its input. By using this method, we can divide up a complex system like a large corporation into simple components that can be measured, controlled and thus managed.


Hierarchical structure characteristic of scientific management

This breaking down of the system into individual components then inevitably requires, at some stage, for us to put all the parts back together so as to achieve the end product or result. In order to achieve this, traditional organizations build a hierarchical pyramid, right at the top of which is one or a small group of elements that are responsible for integrating the whole system. Below this, is a small set of positions responsible for managing and integrating the primary domains of the organization and, farther down, more people are responsible for more specialized areas and so on until we get to the front lines of the organization. Each level is responsible for the integration of the different set of functions beneath it. In this way, the organization can be coordinated from one centralized position. All components can be controlled through a direct line of command and measured through a defined set of metrics relevant to their domain.


This model of the industrial organization was developed in response to a particular environment that required the large-scale, mass production of standardized products and services, in a stable and predictable fashion. Within this context, the industrial model for organizations has, in many ways, proven itself highly successful. But faced with the changing environment of the 21st century, its limitations are becoming increasingly clear to us.

Complexity management is based upon this alternative paradigm of synthesis that gives us a very different model to organizations and how to manage them. Being holistic, it is focused on the whole instead of the parts. It is primarily focused on the whole functionality of the organization, positing that this is an emergent phenomenon. That is to say, that the whole organization is something more than the sum of its parts. From this perspective, it is the particular way in which the parts interact that gives rise to this added value, the emergent global functionality and it is this emergent functionality that we are really interested in. Thus, we are not particularly interested in any parts of the organization, but instead how those parts are arranged or organized into a functional unit.

Instead of focusing on the components of the organization, we focus instead on the connections. That is to say, we do not try to achieve the desired outcome by defining component parts and trying to control those parts through a hierarchy. We instead create the connections, the context within which those components interact and, out of that, we get the emergence of global functionality to the organization that we desire.

So these are two very different approaches. With the standard approach, we are essentially trying to manage and control the output to the system. Within this approach, management defines and specifies to the organization what they want the outcome to be, this is why it is seen to be top-down in nature – management is constraining the elements of the organization towards some desired outcome. In contrast, the complexity approach does not seek to control or even define the desired output to the system. Instead, it is focused on creating the right input conditions that will enable the elements in the organization to self-organize into a functional unit, out of which emerges the desired global outcome to the organization. This approach is essentially a creative one. Your capacity as a manager to influence the system is only in your capacity to create the input context. You don’t get to control the system. In that sense, emergence is fundamentally a process of creation so leading or managing an organization within this paradigm means creating the context.