Causal Loop Diagrams

Learning Goal

This tutorial will introduce you to the basics of Causal Loop Diagrams, and provide you with some examples to help you get started with modeling your own complex systems.

Scope

After completing this introductory tutorial, you will be able to:

• Read and interpret a Causal Loop Diagram
• Understand the basic concepts behind modeling complex systems
• Be able to create your own Causal Loop Diagrams

Structure

This tutorial is structured in three main sections:

Introduction

Systems Thinking is an approach to understanding and solving complex problems by viewing them as parts of an interconnected whole rather than in isolation. It involves recognizing patterns, relationships, and feedback loops within systems to better anticipate the impact of decisions and actions, thereby enabling more holistic and effective solutions.

In order to model these complex systems, we use a variety of tools and techniques to help us visualize and understand the relationships between various elements within our chosen system. One such tool is the Causal Loop Diagram, which is a graphical representation of the feedback loops that (likely) exist within a system.

Basic Concepts

The basic building blocks of a Causal Loop Diagram are Nodes and Links. Nodes represent the elements within the system that we are modeling. These are concepts, objects, or entities that interact with each other in some way. They are generally depicted as circles, boxes, or words, and are written down as nouns or noun phrases.

Links represent the relationships between the nodes. These are the connections, influences, or dependencies that exist between the elements of the diagram. They are generally depicted as directed arrows or simple lines. The direction of the arrow indicates the nature of the relationship. A node can either have no effect, a strengthening effect, or a weakening effect on the node it connects to. In other words, if the quantity of one node increases, what happens to the quantity of the other node?

• Same: If the quantity of one node increases, the quantity of the other node also increases.
  This is depicted by a line with an arrowhead (and optionally a + or S sign). Mathematically, this is know as a correlation.
• Opposite: If the quantity of one node increases, the quantity of the other node decreases.
  This is depicted by a line with an arrowhead and a − or O sign. Mathematically, this is known as an inverse correlation.
• None: If the quantity of one node increases, the quantity of the other node remains the same. This is the easiest to draw, as you don’t need to draw any line at all.

Chickens and Eggs

To illustrate these concepts, let’s consider the classic example of chickens and eggs. In this system, we have two nodes: number of chickens and number of eggs. The relationship between these two nodes is that chickens lay eggs, and eggs hatch into chickens. This creates a reinforcing loop, as the amount of chickens increases the amount of eggs, which in turn increases the amount of chickens. This gives us a causal loop diagram that looks like this:

Chickens and Roads

Now let’s consider another aspect chickens are known for: crossing roads. In this system, we have two other nodes: number of chickens, and number of road crossings. As any chicken owner will tell you, the more chickens you have, the more likely they are to cross the road. So, as the amount of chickens increases, the amount of road crossings also increases.

And as any road safety officer will tell you, the more road crossings you have, the more likely you are to have chickens being run over. Hence: as the amount of road crossing increases, the amount of chickens decreases.

Chickens, Eggs, and Roads

Now, let’s combine these two systems into one big system. We end up with our three nodes: number of chickens, number of eggs, and number of road crossings. Note that there is no direct relationship between eggs and road crossings, so we don’t need to draw a link between them. This gives us a causal loop diagram that looks like this:

We have now created a simple causal loop diagram that shows the relationships between chickens, eggs, and road crossings. This diagram helps us reason about the system and understand how changes in one element can affect the others. As we add more nodes and links to the diagram, we can model more complex systems and explore the feedback loops that exist within them.

We could now start asking questions like: “What happens if we introduce a new road safety measure that reduces the number of road crossings?” or “If we increase the number of chickens, how will that affect the lives of our neighbours?”.

This is just the beginning of what you can do with causal loop diagrams. In the next section, we will explore some more advanced concepts. These will help you create more detailed and accurate models of complex systems if so desired.

Feedback Loops

Balancing Feedback

Reinforcing Feedback

Real-world Examples