I’m looking for way to encourage my kids (still in the early single digit range) to begin thinking in a way that they can recognize, understand, operate, and/or build systems.
I often see negative feedback loops forming around LLMs, and even this sub's rules say AI does not simulate thought in any meaningful sense. I agree with the intent. But I want to gently push on one narrow part of it, from experience: harnessed with the right tools and instructions, AI does simulate a thought process well enough to change what one person can do with a system model.
I remember few years ago, when I was learning about Futures Studies, the idea of mapping out systems and understanding its feedback loops really hooked me into thinking in maps. But even then, validating where the thought process had systemic flaws or utilizing mathematical calculations to simulate was never an easy process and often ignored. Now with AI, this has changed.
I'm a software engineer, so I experience this wave up close. The way I experiment now: I let agents build models using system dynamics tools I'm familiar with. I visually check the structure and ask it to simulate various scenarios. And this goes into a loop of its own: often into a subsystems where my interest hones in. None of this works with a raw model. It has to be guided thoroughly with tools and instructions — AI is bureaucratic by design, to borrow Harari's term, which is exactly why it's good at this kind of structured formalization work. Guided that way, it does well. Often beyond my expectation.
I remember the time in highschool (being korean who memorized multiplications, you know what I am talking about if you are Korean) knowing how calculate stuff in my head was thought to be gifted. It was like in test, if you use a calculator, you're cheating. But then the point was not about having the ability to memorize or do complex calculations in your head but to understand the system behind its mathematical journey which leads to a bigger system where it is almost impossible to reach without the help of scientific tools.
I think systems thinking is at the same moment. AI needs the right tools to do the right work, and people — including myself — are building those tools. My claim: bigger systems, whose modeling and simulation used to be delegated to a particular set of people, are becoming accessible to a much broader audience.
So I'm curious:
Has anyone here tried this — letting an agent do the model-building and simulation grind while you stay at the level of structure and judgment? Did it deepen your thinking or shallow it?
The calculator eventually became allowed in the exam room. What's the equivalent boundary for AI in systems work — what should still be done "in your head"?
I’m new to the world of systems thinking, although my work is somewhat adjacent to it. “Thinking in Systems” by Donella Meadows seemed to be a common recommendation for getting started in this space, so I started there and just finished reading.
I’m having trouble understanding the hype about this book, or perhaps this entire field in general. It was well written and fine to read, but the concepts feel incredibly obvious to me - to the point where I don’t understand how people haven’t already been thinking this way their whole lives. From what I took away from it, the book essentially has these main points:
- Things affect other things. When a system’s components work together, they can become more than they were on their own. Systems depend on their inputs, outputs, and rates of change, not just their individual components.
- Big societal problems have more than one cause. Many problems are interdisciplinary. Making a change to one part of a system can have delayed or unexpected effects.
- Systems can fail in many ways. People have a lot of cognitive biases and behavioral tendencies that make group dynamics and effective governance challenging.
The whole time I was reading, my main thought was “Yes, obviously.” Are these concepts really not obvious to other people? Am I unusual in that these things seem very intuitive? I’m wondering if I’m missing some underlying meaning or if this really is the main crux of systems thinking as a field.
Over the past few months I've become less interested in "how to be more motivated" and more interested in treating procrastination as a systems problem.
Instead of asking:
How do I become more disciplined?
I ask:
- Where is the bottleneck?
- Is it friction?
- Decision fatigue?
- Poor environment design?
- Energy?
- Unclear next actions?
- Reward loops?
I'm treating myself like an engineering project: identify bottlenecks, run small experiments, measure results, and iterate.
After reading the responses on my previous post - https://www.reddit.com/r/systemsthinking/s/RQRKQpcRX5 , I realized there are quite a few people who think about productivity this way. So I had an idea.
What if we formed a small group , not for accountability in the usual sense, but as a place to investigate ourselves as systems?
The goal wouldn't be to spam motivational quotes or "grind harder." It would be to:
- Share experiments.
- Analyze failures without shame.
- Map bottlenecks.
- Discuss systems thinking, habits, and behavior.
- Help each other design better personal systems.
If that sounds interesting to you, leave a comment or send me a DM. If enough people are interested, we can create a small group (Discord, WhatsApp, or whatever works best).
I'm curious whether we can make consistency an engineering problem instead of a willpower problem.
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For the past few months, I've been thinking about procrastination differently.
Instead of asking, "How do I become more motivated?" I'm asking, "What part of my system is failing?"
For example, procrastination could be caused by:
- Too much friction to start.
- Poor environment design.
- Decision fatigue.
- Unclear next actions.
- Reward loops from social media.
- Sleep, nutrition, or energy problems.
- Identity not matching the habits I'm trying to build.
I'm starting to think that discipline isn't a personality trait , it's an emergent property of a well-designed system.
I'm treating myself like an engineering project: identify bottlenecks, run small experiments, measure results, and iterate.
For people who've managed to become consistently disciplined for a year or more:
What system change had the biggest long-term impact? Not a motivational quote or productivity hack, but a structural change that permanently made consistency easier.
I'd love to learn from your experiences.
Hello Systems Community,
What applications do you tend to use to visualize systems thinking? Something like graphic databases like neo4J?, or something more flexible?
I'm at a loss to know how to apply this theory in a more lab environment when changing one variable would affect and I can see graphically and easily its effects.
Thanks!
The current polycrisis and tech disruptions in almost every aspect of our lives have made our world too complex to predict and too fast to adapt to.
Building resilience takes finding and building around structural constraints, unchanging elements that provide the necessary stability.
I like the classic example of Jeff Bezos and Amazon to drive this point home.
When asked what's going to change in the next 10 years, Jeff Bezos said he's hardly ever been asked what isn't going to change. For him, that's the more interesting question, because you can build a strategy around the things that are stable in time.
No matter the state of the economy or the identity of the president, says Bezos, people will want low prices, vast selection, and fast delivery.
When you know something is true even over the long term, you can afford to put a lot of energy into it. To this day, these three elements make up the core of Amazon's strategy and identity.
Instead of trying to predict what's next or to change faster than the world, Embrace the Unchanging.
But how do you find these unchanging elements?
At any given moment, there is an infinite number of things around you that do not change. The sun rises in the east and sets in the west. Gravity pulls you towards the Earth. The global population is growing. Unchanging elements are everywhere you look. Most of them are irrelevant and hold no value to you. But a few could be the foundation you build your entire strategy around.
How do you know which elements are right for you? How do you find the enabling constraints that are valuable to you?
I've wanted to learn systems thinking for a while, but I keep getting overwhelmed and don't know where to start.
I don't have a college background, but I've taken short courses in permaculture and agroecology, where I got a small introduction to systems thinking. I'm also interested in ecological economics and related fields.
I'm wondering if there's already a beginner-friendly study group I could join. If not, would anyone be interested in starting one?
I'm imagining something fairly casual but consistent:
If something like this already exists, I'd love to hear about it. Otherwise, let me know if you'd be interested in creating one.
https://youtu.be/mjTgkm-h__M?si=vcm2EFUV_ThHeAbX
Hi everyone. Just wanted to share with everyone on this video I watched which I found super insightful and relevant for me, especially as a software engineer.
So in short, Sandeep talks about systems thinking, which sounds like a software engineering mod (that i really enjoyed in uni) but actually is very relevant to everyone.
He first talks about 3 reasons why we get confused about theses everyday systems:
He then outlines the 4 different types of systems we encounter in our lives.
First up, we have the simple system. This system is defined by the obvious cause and effects in the system, making it simple to understand. For e.g. these are the steps outlined in a SOP. Checklists help for this system by reducing mistakes from human error.
Second, we have the complicated system. This system's cause and effect is not as obvious. The cause and effects might be hidden or require some expertise to uncover. For me, this system is like the requests from customers where it is not obvious the final result they are trying to achieve. E.g. a new dashboard that tracks a metric, but we don't know what this metric does. To help in these systems, we can take some time to analyse it and find the correct expert for this.
Third, we have the complex system. This system's cause and effect is only uncovered in hindsight. This means while in this system, we cannot discern if whatever we do will actually have the desired effect. For me, this is like if there is no way to know what the metric does until after we create the dashboard. To help, we should write many tests, stay adaptable and course correct when necessary.
Lastly, we have the chaotic system. There is absolutely no way to find out the cause and effect in this system, as the name suggests. A metaphor for this could be a failure in production, where a bug causes some part of the system to fail and it is not immediately obvious why it happens. To combat this effectively, we can stabilise first, before finding out the root cause.
For me, I see these systems as different levels of every problem I encounter, where the higher levels can be decomposed into the lower levels. For e.g. with regards to the new dashboard, we can find out what is the exact cause and effect by running tests, asking the customer questions or sometimes just after some time it becomes clear. Of course, not every time it can be decomposed so readily or in time, so we have these measures to work with these systems in the meanwhile.
He proposes a framework DART to analyse and breakdown the type of system we are in. Deconstruct, where we break down the problem into its sub parts to see if the parts are stable or constantly shifting. Analyse the link between cause and effect, is it clear, hidden, require hindsight or completely broken? Recognise any previous patterns that are applicable to this problem. Test it by running the smallest possible experiment to see how the system responds before committing to a strategy.
There are also three techniques/plaform tools he suggests to affirm the system we are in. Mentors, which are the person "on the platform", an outsider from the system which can see it from the outside. Data, which gives you the hard facts that are undeniable proof of the system. Finally, Time, which means comparing your actions to your past actions. These all can tell you whether you are doing the correct things in the system, and in which direction your train is moving.
We can use DART and MDT in tandem by recognising that Deconstruct and Recognise can be used with mentors, who can help you find hidden patterns or missing components. Analyse with data, which means using hard data to back up your analysis instead of relying on developer intuition or complaints. Tests with time, as only over time will we be able to tell if the tests we built was a good model for the actual system.
Finally, he talks about our internal feedback loops, which is our own core beliefs that might be holding ourselves back. Only by using these platform tools could he determine that his brain had mapped these false cause and effects togther.
Thank you.
An interesting and important essay, I'm sure many of you would appreciate.
An article formulating the thesis that our philosophy on sustainability evolves over time: from 1.0 (conservationism) and 2.0 (sustainable development) to 3.0 (holistic sustainability)
What do you think needs to happen or change, fundamentally, within the existing systems to not just capture all your thoughts or ideas but to actually synthesis them and be able to compound on your thought process itself?? Like how we build our understanding on top of what we already know and cross connect all our understanding.
What benefits do you see from such systems and how will it change the way you process things?
Does anyone have tips for working effectively together with your team on a system dynamics project from qualitative data?
We have a project on very tight timelines (nature of the project and other hold ups), and are looking for good ways to keep track of loops in our causal loop diagrams, which will also hopefully help us check for archetypes quickly.
Our previous experience with systems dynamics was usually on longer timeframes, or where one person did most of the analysis, so any advice would be welcome!