iGCLC™ Certified - Systems Thinking Practitioner

Let’s get specific about what we actually mean by a system so we can move through the course with a clear understanding. Systems have particular qualities you might not have noticed before.

You can find systems in all areas of life. They take a range of different forms. You might not notice it at first because systems can get complex but they really are all around us.

One of the most important principles of systems thinking is that the system is understood as a whole. All the parts of a system working together has a specific outcome that wouldn’t happen individually.

As we are looking at the system as a whole, we need to explore it in a certain way. Just taking the system apart won’t give you a full understanding of it. You need to see the relationship between the parts in the system.

There are two very different types of complexity. You need to understand them both to understand the variety of systems you could come across. There isn’t just one way to be complicated.

When dealing with complexity, it can be easier to see the system as a web. We’ll look at some examples of what this means. There are advantages and disadvantages to complexity.

In an ideal world, a system would be both stable and easy to change. It's not usually that simple. Pushing too hard for change can have dire consequences but so can not changing a system.

Just because something works as a system doesn’t mean its behaviour is predictable, especially if it is a complex system. Small changes in circumstances can lead to an unusual outcome.

Change is hard because it can take a lot of work to happen and the consequences of change can be surprising. It might take years for the consequences to be spotted but well intended actions can cause negative side effects.

You might not have realised it but we actually think in circles. Our thoughts depend on the feedback we receive. As thoughts lead to action, it influences what feedback we receive.

Now you have a good idea of what a feedback loop is, let’s go through some examples to consolidate your understanding. You might be surprised at just how many feedback loops you can find in the world around you.

Reinforcing feedback keeps the system going in the direction it was headed. It doesn’t challenge change in any way. Like a snowball it just keeps rolling.

Reinforcement can be good or bad. It can keep a system improving or maintain its downward spiral. Usually at some point a balancing feedback loops needs to be brought in because one way of working isn’t effective forever.

While balancing feedback loops can be seen as preventing change, they are needed to maintain stability and sometimes take systems in a new direction. We need to keep adapting to the changing environment we live in afterall.

The two main characteristics of a balancing feedback loop is that it works towards a goal and uses measured feedback to reach it. The goal cannot be reached if the measurements aren’t accurate.

Feedforward is probably not a term you have used much before but it explains how our expectations can affect the future. When we think a certain effect will happen, we will become the cause of it.

Don’t take cause and effect for granted. It is valuable feedback and can be more difficult to spot than you might think. Linking an effect to a cause is essential for understanding systems properly.

A cause will always have an effect. We may have to wait a long time for it or it may happen in an unexpected part of the system but it will always happen somewhere eventually.

You need to find a way to deal with time delays because they are inevitable in some systems. Not everything happens instantly but that doesn’t mean it should be ignored.

We are going to review everything we have learnt in this section so your understanding is solid and you are ready to expand your knowledge of systems thinking.

We might not notice we have mental models but they still influence our thinking and behaviour. They form their own system and are relevant to systems thinking.

Mental models are what we use to find meaning. As they are based on our beliefs and past experiences they can be biased or inaccurate. Just having an awareness of the assumptions we have gives us back control.

We are looking at how mental models are formed and maintained so you know where inaccuracies come from and how you can redefine your beliefs. Mental models can be helpful if updated regularly.

The information we are paying attention to is really important. If we ignore important information or overemphasis irrelevant information, our mental models will not be useful.

There is a system to changing mental models. If you go through these steps, you can update them and take back control of how you interpret the world.

Updating mental models that aren’t realistic or helpful can be hard when we feel like the belief is being reinforced. Any proof that negative beliefs are accurate make it more difficult to change them.

As mental models are how we find meaning, they can do a lot of damage if they are wrong. There are steps you can take to ensure the evidence you base your beliefs on is the best it can be.

Taking a systems thinking approach to mental models we can see their limits and how they can be improved. Instead of taking our beliefs as fact, we are encouraged to step back and take systems into account.

Cause and effect within systems needs some explaining. There are lots of factors that can lead to a certain outcome so identifying a definite cause can be difficult. Using systems thinking the process is much more accurate.

We are exploring the false beliefs around cause and effect. These can lead us to misidentifying cause and effect connections. Systems work in circles so the cause and effect process isn’t always obvious.

Introducing the butterfly effect and chaos theory helps us to understand how complex systems can be affected by very small events. This isn’t as random as you might think. There is an explanation behind the chaos and sensitivity.

Despite chaos theory, it is still hard to find patterns in some systems. This is why we have two types of complexity. One can be organised into patterns and one can’t. But there are some factors you need to understand to find these patterns.

Ordered states are useful to systems but sometimes they need to be changed. This is easier said than done as systems can get stuck in attractors. Certain steps can be taken to enact change though.

We all think logic is the best way to solve problems. With systems this is not the case. It cannot handle the complexity. Instead we need to rely on fuzzy logic when it comes to decision making.

We can agree that logic doesn’t work in systems. Now we are going to explore why this is. There are certain elements essential to systems that logic doesn’t take into account. This can leave us stuck in paradoxes.

To be able to avoid getting stuck in paradoxes we need to understand the role mental models have to play. Mental models can limit our beliefs so we aren’t able to see the whole situation and the real cause of the problem we are experiencing.

We are continuing to look at how limiting mental models can be spotted. The language we use can make all the difference. When phrasing information as if there is no alternative, we completely restrict our decision making.

We build systems, like businesses, based on our mental models. This means any limitations our mental models have our system also has. By consistently evaluating our mental models based on new experiences we can keep both up to date.

Having reached the end of section two, we are reviewing everything we have learnt in the three modules. With this information consolidated we can move onto the next section in the course.

In this first lesson of section three we explore what learning actually is beyond the classroom. It forms a system with its own feedback loop as we interact with the world and change from these experiences.

The feedback loop we looked at in the last lesson represents simple learning. This is what the majority of our learning experience is like but it does have its downfalls too.

Generative learning expands upon the single loop set up to include the development of our mental models. Changing our mental models based on feedback gives us more choices and opportunities.

While we have the ability to learn all the time, there are some factors that stop us. We are going to discuss some ways that feedback isn’t taken on board and responded to as it should be.

The two types of learning are used in many real life examples. One of the areas where we can see the use for both of them is in business. Businesses need to be stable but also adapt. The same can be said of our mental models.

In this lesson we tackle three more ways that learning is blocked. We need to consider mental models, how we measure feedback and the precision and accuracy of feedback. We need to be able to interpret feedback too even when it can’t be expressed as a number.

We are finishing up our conversation about how learning can be stunted. Take all of these factors into consideration and you will be able to respond to feedback appropriately. This means you can get the most out of new experiences.

The focus of this module, perspective, is really important. It determines how we see the world and the systems in it and what we miss out on. Overlooking vital information can be detrimental.

This lesson will explain why we need different perspectives to be a successful systems thinker. Mental models have a role to play in the viewpoints we use and don’t use.

Perspectives can be categorised into objective or subjective. They both have their place in systems thinking but you need to understand the distinction and the different value they have.

The subjective view has its benefits and downfalls but is needed for a richer understanding of a system. Taking a solely objective view is too impersonal but it is needed to see the bigger picture.

When we focus too much on our own perspective we can get stuck in reinforcing loops without even realising it. It's only when we step back and bring in a balancing loop that we can move on productively.

There is a way to get out of destructive reinforcing loops. You need to understand how they work so you can find the leverage points and use them to come to a reasonable conclusion.

We are looking over everything we have learnt in section three so that information is consolidated for the next section. We looked at the process of learning and the importance of perspectives.

We can draw conclusions from a system when we fully understand how it works. One way to do this is to use a system diagram and map out all the elements and connections. This is the process we will be learning about in section four.

There are certain factors that you need to consider as you build your story which represents your system. These boundaries of understanding will keep you focused but also include all the relevant information.

With the parts needed to bring a system together consolidated, you can begin mapping your system starting with a reinforcing loop. You do need to consider the types of links you use though. Different ones indicate different relationships.

As well as two types of reinforcing links, there are two types of balancing links. How these differing types of links combine into feedback loops depends on the overall effect of the system.

Balancing loops are also an important part of your system story but they take a slightly different form to reinforcing links. It's also helpful to be aware of the type of element in a system when mapping it out.

Understanding a system’s structure is one thing. Understanding a system’s behaviour is a completely different thing. There isn’t a set relation between the two. Factors like time delay can make a big difference.

All the parts of a system can be combined in lots of different ways to make a range of whole systems. That’s why labelling your systems diagrams correctly is so important. It needs to be clear what is happening in the loop.

There are common patterns repeating throughout systems. The rapid progress followed by decline pattern is one of those traps we can fall into. We can start to overcome it by understanding how it works.

This pattern of success being limited occurs on all sorts in levels in lots of different areas. We are going through some examples to see how detrimental reaching the limit can be.

There are ways that we can overcome limits to growth and not get stuck in the system. Using three leverage points we can prepare to change the system before it is stunted by the balancing loop.

We are explaining what we have learnt about limiting loops through the example of weight loss. It takes the common pattern of starting well but then getting more and more difficult. The balancing loop gets in the way.

A balancing loop is what keeps the limitation on success in place. By achieving the goal of the loop, we get rid of the need for the balancing loop. There are ways we can make this more achievable but they can be used in a damaging way too.

The system of personal finance has a story to tell. Often it ends up with swinging between two financial extremes but we can use leverage points to create a different outcome.

While borrowing money can be a solution to the system of personal finances, it has its own system that we can get stuck in. It seems beneficial but it can actually be worse.

Solutions don’t always work out. If the problem keeps coming back, we shouldn’t keep using the same solution to try and fix it. We need to look deeper at the fault in the system.

Identifying an ineffective long term solution is one thing because replacing it is a whole new challenge. System can become reliant on solutions. The quick fix you have used can become addictive but there is a way to overcome this.

One system story is how escalation can grow to a damaging level for everyone involved. If the right steps are taken, this system can be overcome so everyone benefits and competition is put to the side.

Another systems story that can be told is the monopoly pattern. This is a system where resources are distributed in a way that makes those who are already successful more successful while leaving those struggling behind.

The final story archetype we are going to look at is the tragedy. All systems have a threshold that if met means the system is no longer beneficial for anyone. It's only when stepping back that we can find the solution.

As we have reached the end of section four, we are going to summarise everything we have learnt. This recap will give us the consolidated information we need to move forward with systems thinking.

As we have reached the end of section four, we are going to summarise everything we have learnt. This recap will give us the consolidated information we need to move forward with systems thinking.

Understanding feedback is essential to understanding a system. The consequences of change within a system could be expected or unexpected, good or bad. Either way you need to identify the effects that follow causes.

You need to gather information about a system into a diagram before you can attempt to make a change. It's not as simple as picking any point of the system to influence. It can be ineffective and even counterproductive to do so. Change needs to happen in a certain way.

When improving a system, we need to focus on the weakest point not the strongest point. This will have the least resistance and the most impact on the system as a whole.

Feedback is an essential part of understanding systems but it does not always occur in the way or at the time we expect it to. If we don’t take into account time delays, we won’t have an accurate view of a system.

Feedback is an essential part of understanding systems but it does not always occur in the way or at the time we expect it to. If we don’t take into account time delays, we won’t have an accurate view of a system.

The first systems were discovered centuries ago, both in mechanical and natural forms. We understood how to use feedback loops and create self-regulating systems at a basic level.

It was using systems thinking that the industrial revolution was able to spread at the rate it did. Later on more theoretical discussions were had to apply what was seen in mechanics to societal structures.

We are wrapping up the course by looking at the latest developments of systems thinking and considering what it could mean in the future. As the world changes, we can use systems thinking to understand it.