A system is a set of connected things or parts forming a complex whole. Think of an ecosystem — different species of plants, animals, fungi, bacteria, etc. all interacting with one another in complex ways.
A system is a set of related components that work together in a particular environment to perform whatever functions are required to acheive the system's objective.
– Donella MeadowsSystems thinking is about seeing the whole - a way of looking at something (a boat, a forest, an organization) where you see everything as part of a larger whole, rather than just isolated pieces. It's like zooming out to get a bird's-eye view of a puzzle. Instead of just focusing on one piece, you consider how all the pieces fit together and affect each other
In systems thinking, events are just the "tip of the iceberg". What we see is only a small part of a much larger picture. Underneath are deeper layers that shape what happens above. Patterns and trends are driven by underlying structures - the rules, policies, and frameworks within which a system operates.
At the base of the iceberg, shaping everything, are our mental models. These are our beliefs, values, and assumptions that influence how we interpret the world and make decisions. Events are visible and grab our attention, and the goal of systems thinking is to understand the underlying patterns, structures, and mental models.
If we want to change events, or prevent them from happening again, we have to change one or more of the "invisible" deeper layers that create them.
System dynamics is a methodology and mathematical modeling technique to frame, understand, and discuss these underlying layers. A system dynamics model is a tool for understanding how different parts of a system interact with each other over time.
SD models let you play around with different scenarios "on paper". You could test what happens if you add more buses to a city's transport system, or change your business strategy. It's a way of experimenting and learning about complex systems in a safe and controlled way. They are critical to building good strategies, finding where your real leverage is by understanding what underlying patterns, structures and mental models are driving the events (behaviour) you are trying to change.
Key elements and distinctions of sytems thinking.
When an action has one set of consequences locally and a very different set of consequences in another part of the system or when repeated in a different context, there is dynamic complexity. When obvious interventions produce nonobvious consequences, there is dynamic complexity.” [Senge 1990].
A great example of dynamic complexity is raising a child. There is no book, no set of instructions you can follow, that would enable people to raise the same child every time. There are too many factors that affect the outcome.
Systems thinking helps us understand how complex systems work so we can create better strategies and make better decisions about how to affect change in a complex system. Governance, running a business, even making personal life choices, are all attempts to create change in complex systems.
Detail complexity can refer to complex things - anything that has a list of components in a system or the number of combinations.
A great example of detail complexity is a rocket ship. Sending a rocketship into space is incredibly complex, but once we know exactly how all the parts fit together to overcome the forces of nature, we can repeat the exact same process over and over.
People fail to achieve their intended results between 70 - 90% of the time in a wide array of activities. Starting a business, technical projects, change management and so on. A key factor in this high failure rate is that we use linear thinking (A + B + C = D) - to complex systems. Linear thinking is effective for problems with DETAIL complexity. It is very ineffective on problems with DYNAMIC complexity.
In general, synthesis refers to the combining of two or more things to create something new. When it comes to systems thinking, the goal is synthesis – to aggregate the individual ideas or 'things' in a system to discover the emergent properties.
We understand what a neuron is, how it functions, how to build a computer with neurons, and how to build a neural network. But put neurons together with other parts of the brain, and we get the emergence of consciousness – something we cannot recreate and have only begun to understand.
Systems thinking, and systems dynamics modeling, attempt to make complex problems easier to understand so we can make decisions we hope will result in the outcomes we want. No model is complete, no model is right, and no model can guarantee we make the right decision.
But the more we understand how a system works, the more likely we are at continually learning to see the whole together and working together to create the results we desire.
Analysis is the dissection of complex things into components. People are great at analysis. We can break things down to understand how they works.
"When it comes to predicting the future, we’re all stupid. Each and every one of us." (The End of History Illusion).
Imagine a blender full of flour. Place a single dark grain of flour amongst all the white grains. You are about to turn the blender on and leave it running for 5 years. But before you do, you need to predict where that dark grain will end up.
You can't - even though each of the millions of events where the blades of the blender hit the grains of flour that bounce off each other are relatively simple and explainable events. Like governance, like business, like some life choices, there are too many parts affecting too many things to predict.
A mental model is simply a representation of how something works based on a perspective, experience, history, intuition, etc. It is the simplification of the actual world.
We cannot keep all of the details of the world in our brains. From galaxies to sub atomic particles, concepts like war and peace, the reasons we love and hate, there is far too much information in "reality". Our brains use models to simplify the complexity into understandable and organizable chunks. Mental models are how we understand the world. They shape what we think, how we understand, what we consider important, how we reason and solve problems.
People with different mental models will answer questions differently. Ask the question "How do we get to the moon" to an an engineer, a politician, a citizen, Elon Musk, Albert Einstein, and a child. You might get answers like blueprints for a rocket, a strategic plan and policy framework, my tax dollars, a private corporation, imagination, by blasting off!
When one perspective or a certain worldview dominates your thinking, you will try to explain every problem from that perspective. What looks like expertise can often become a limitation if we fail to bring a broader set of perspectives to the table. “If all you have is a hammer, everything looks like a nail.”
Relying on a narrow set of perspectives limits the cognitive range of motion a group is capable of. The 70-90% failure rate we experience in things like starting a business, technical projects and change management is the result we get when we try to solve complex problems using a limited set of perspectives.
When your set of mental models is limited, so is your potential for finding a solution. The more models you have—the bigger your toolbox—the more likely you are to have the right models to see reality. It turns out that when it comes to improving your ability to make decisions variety matters.
Below are some of the more common "archetypes" in system dynamics. Archetypes are like common patterns or models that show up repeatedly in different systems and situations. They're a bit like the recurring themes or storylines you might find in books and movies. These archetypes help us understand how systems behave and why certain issues keep popping up.
Many of the problems we have in our lives and our organizations will have one or more of these archetypes at play. A good place to read more about these and other principles of systems thinking is The Fifth Discipline by Peter Senge.
Archetypes are recurring patterns of behavior that give insights into the structures that drive systems.
A solution is rapidly implemented to address the symptoms of an urgent problem. This quick fix sets into motion unintended consequences that are not evident at first but end up adding to the symptoms.
A problem symptom is addressed by a short-term and a fundamental solution. The short-term solution produces side effects affecting the fundamental solution. As this occurs, the system’s attention shifts to the short-term solution or to the side effects.
A given effort initially generates positive performance. However, over time the effort reaches a constraint that slows down the overall performance no matter how much energy is applied.
As a gap between goal and actual performance is realized, the conscious decision is to lower the goal. The effect of this decision is a gradual decline in the system performance.
Growth approaches a limit potentially avoidable with investments in capacity. However, a decision is made to not invest resulting in performance degradation, which results in the decline in demand validating the decision not to invest.
Two or more efforts compete for the same finite resources. The more successful effort gets a disproportionately larger allocation of the resources to the detriment of the others.
Parties take mutually threatening actions, which escalate their retaliation attempting to “one-up” each other.
Multiple parties enjoying the benefits of a common resource do not pay attention to the effects they are having on the common resource. Eventually, this resource is exhausted resulting in the shutdown of the activities of all parties in the system.
Parties take mutually threatening actions, which escalate their retaliation attempting to “one-up” each other.
Understanding how the pieces affect each other is the key to successful growth and change.
