5 Principles of Systems Thinking

Fri Aug 23 2019

by John Hill

In this post, I want to introduce you to 5 of the key principles of systems thinking and show you how they can help you achieve a better understanding of complex systems.

1 .Interconnectedness

Systems Thinkers embrace the fact that all things are interconnected. In the natural world, everything is reliant on something else for its survival. Businesses do not exist in isolation. They rely on staff, customers and suppliers. And they face competitors who are constantly evolving. When seen this way, the world is not linear and mechanical, but non-linear, dynamic and interconnected.

All systems are comprised of a set of components that are interconnected and work together to perform functions which collectively attempt to satisfy the system's objectives. In the world of business, we can think of those components as staff, or departments. If we take a reductionist view of the system, and consider the functioning of these components only in isolation - i.e. ignoring their interconnectivity - we will fail to understand how they perform as a collective to achieve the system's objectives.

2. Feedback Loops

The structure of interconnectivity in a system matters. Almost all systems contain multiple feedback loops. These are connections between components where information from one component forms an input to information processing in another component. As a result of this cyclicality, these interconnections have special properties. Feedback loops can be reinforcing, or balancing in nature. The classic example of a balancing feedback loop in nature is the predator/prey interaction - whereby predation keeps both populations in balance. Interventions in these balancing feedback loops can trigger threshold effects - changing the nature of the feedback loop from balancing to reinforcing. For example when humans intervene and eradicate too many predators. The result is runaway population explosion of the prey.

3. Causality

When we have identified the interconnections and feedback in a system, we can more clearly identify causality. When we do not have a clear understanding of how a system functions, we may misunderstand causality. This is a common problem in many businesses and leads to actions which have unintended consequences. It is also a common cause of poor results arising from data-driven decision-making. Without a coherent model of causality, relationships inferred by data could simply be correlation - not causation. And if we intervene in a system where we have misidenitified, or misunderstood casusality - we can produce worse outcomes than had we not intervened at all. Once we know how actions lead to results which shape the future, we are better equipped to make policy decisions, or prescribe interventions.

4. Emergence

Emergence describes the way that new order comes about as a result of the interaction of the large number of components in a system. When things come together, the results are often counterintuitive. In nature, large numbers of termites are capable of building large and complex mounds. Analysis of each individual termite would not reveal the ability of the colony as a whole to create such structures. System Thinkers therefore are just as concerned with synthesis - the combination of components or elements to form a connected whole - as they are with analysis - detailed examination of the elements or structure of something.

Emergence is encapsulated in the adage: "The Whole is Greater than the Sum of its Parts"

5. Systems Maps

To facilitate synthesis - and to encourage us to think about the system as a whole - we need systems maps. There are many ways to map a system, but the underlying principles are the same. The goal is to first identify, and then to map the elements of a system in order to understand how they are interconnected and the nature of those interconnections - for example whether feedback loops are reinforcing or balancing. A coherent systems map is the first step towards insights about the functioning of the system that can lead to novel interventions or policy decisions that can favourably change the ability of the system to acheive its objectives.