Systems Thinking

Systems Model


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 Meadows
Systems Model

Systems Thinking

The fundamental principle of systems thinking is interconnection. Complex systems – the human body, government, business, etc – have a great number of possible interconnections between the parts that make it up. Everything is connected to something else. People work on desks. A business needs wood to make desks. A government makes rules on how we cut down trees, etc.

Systems Model

System Dynamics

System dynamics is a methodology and mathematical modeling technique to frame, understand, and discuss complex issues and problems. Originally developed in the 1950s to help corporate managers improve their understanding of industrial processes, SD is currently being used throughout the public and private sector for policy analysis and design.

People use system dynamics to understanding the nonlinear behaviour of complex systems over time using stocks, flows, internal feedback loops, table functions and time delays.

How Systems Thinking Helps

Key elements and distinctions of sytems thinking.

Dynamic Complexity

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.

Dynamic vs vs Detail Complexity

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.

Synthesis vs vs Analysis

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.

Mental Models

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!

Mental Models vs vs Dominant Perspectives

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.

Systems Thinking Archetypes

Archetypes are recurring patterns of behavior that give insights into the structures that drive systems.

Systems Model

Fixes That Fail

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.

Systems Model

Shifting the Burden

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.

Systems Model

Limits to Success

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.

Systems Model

Drifting Goals

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.

Systems Model

Growth and Underinvestment

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.

Systems Model

Success to the Successful

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.

Systems Model


Parties take mutually threatening actions, which escalate their retaliation attempting to “one-up” each other.

Systems Model

Tragedy of the Commons

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.

Systems Model

Accidental Adversaries

Parties take mutually threatening actions, which escalate their retaliation attempting to “one-up” each other.

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