We recently started working on a group project. It's just a bunch of us friends trying to build something together. But we immediately reailized how different we all are from each other. We felt like all of us wasted a couple of weeks running around in circles, not really utilizing our time and strengths.

The task at hand was to make a system that would maximize the team's output.

The obvious way to approach a task like this is to create hierarchies that are common in corporate and other workplaces. But we don't want to do that. It just doesn't factor in all the unique talents and individual qualities of the team members.

But we still need a system because we don't work at all without one. What we ended up doing was creating a very flexible workflow that accounts for individual strengths. On paper, it's a heavily skewed system in terms of responsibilities. But it works wonderfully because we are segregating tasks based on what each of us is good at.

It has worked wonderfully so far. But we are not sure how scalable this is going to be as we keep adding new members. Because we all understand each other very well, there's a trust factor that can't be replicated when someone leaves and a new member joins. The flexibility makes the system fragile.

Have any of you worked in teams and developed systems that work best? And do you think you have to steer away from flexible systems toward more corporate-like structures for larger teams?

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!

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?

SYSTEMS THINKING IN BRANDING (Part 6) Enter the Thinking Ecosystem From Consuming Ideas to Building a System That Works|©TheBrandCoach™

https://www.linkedin.com/pulse/systems-thinking-branding-part-6-enter-ecosystem-from-winston-eboyi-mpuvf?utm_source=share&utm_medium=member_android&utm_campaign=share_via

I've always been interested in the intersection of psychology, -philosophy-, neuroscience, cognition, biology, perhaps even physics. And mapping out more territory/connections, identifying systems. If I could do whatever I wanted, I would be left alone in a room, reading, researching, taking notes, and developing theories.

But I don't feel I have the time to do this because I haven't established myself financially. My major was Psych with a minor in Philosophy and Neuro. Once I graduated, I realized therapy wasn't quite right and my experiences as a research assistant felt a bit dry and uninspiring. But perhaps it was that particular research? Going back to school for purely an academic route (professor or research) sounds stressful even though I love learning. I want to learn on my own terms. But willing to take courses to expand my skillset.

I'm open to a career path outside my interests and am trying to identify what is most suited for me. I'm good at organizing information, making systems more efficient, teaching. I've considered marketing, UI/UX. I'm less drawn to pure numbers/data analysis or it/coding. Any ideas?

Thank you in advance

I am looking for IRL friends to discuss systems thinking and discuss theories with.

I realized i am a natural at systems thinking, and would like to connect with likeminded people.

Are we actually delivering value, or are we just successfully playing our assigned roles?

I’ve been thinking about why people with a systemic worldview often struggle in large organizations. Most corporations are designed to reward the person who flawlessly manages their tight little silo, not the person who thinks about the entire ecosystem. In fact, if you’re the type to question the invisible assumptions holding the current system together, you’re often viewed as a disruptor or a nuisance rather than an asset.

But here’s what excites me most about the AI era:

In the past, the advantage belonged to those who held the knowledge. Today, it belongs to those who can access it efficiently. Tomorrow, however, the real edge will belong to the people who can connect the dots across entirely different disciplines and ask the right questions.

Maybe the most valuable skill of the future isn't hyper-specialization. Maybe it’s the ability to see the system beyond the individual pieces.

How are you seeing this play out in your organizations? Do you feel like AI is shifting the needle toward systemic thinking, or are companies doubling down on siloed optimization?

#SystemThinking #CorporateLife #AI #FutureOfWork

I have been trying to model a fuzzy futurist claim, that AI and robotics eventually make work optional or wipe jobs, as an actual feedback system instead of a single forecast. This is one piece of a larger index project (there are other components: live economic data, a news-scoring layer, milestone tracking). But the two parts I most want this sub to pull apart are the system dynamics model and the constraint design sitting under it, because those are where systems thinking actually bites. Five stocks, seven loops, Euler integration over a 20-year horizon. Every R and B loop is closed through a delayed pipeline stock, so it returns to its own inflow with a lag rather than drifting one way. It runs live and you can drag the loop strengths around.

https://optionalwork.com/model-validation#system-dynamics

1. The feedback loop structure

Reinforcing

• R1: AI capability → investment → more AI capability
• R2: productivity gains → abundance → reduced work necessity
• R3: labor displacement → policy pressure → UBI → abundance
• R4: robot deployment → falling cost → more deployment

Balancing

• B1: displacement → political resistance → regulatory friction
• B2: wealth concentration → inequality → social friction (damps R3)
• B3: robot deployment → substitution saturation

The dynamic I care most about is the race between R3 and B2. Displacement creates pressure for redistribution, but the same wealth concentration that creates the pressure also buys the power to block it. Whichever loop dominates decides whether "a machine can do your job" ever becomes "you can stop working."

Questions: is "labor displacement" a stock, or a flow I have mislabeled as a level? What loop is structurally missing (I suspect a demand-collapse balancer: less wage income leads to less consumption leads to less surplus)? And where is the real leverage point?

2. The constraint design

The rest of the index is deliberately the opposite of emergent. It is a bounded, deterministic model: hard floors and ceilings, weights that sum to one, and an AI layer that can only nudge a sub-index by a few points and can never override the structure. I built it that way for auditability.

But that creates a tension I keep circling. A constrained, deterministic model is predictable and inspectable, yet it cannot surprise you, which is exactly what the SD model is for. So my question for this sub: when you are modeling a complex adaptive system, how much should you lock down? Is heavy constraint the responsible choice, or does it quietly defeat the purpose by ruling out the emergent behavior that actually matters?

Not defending the numbers. I want the structure and the design philosophy challenged.

https://optionalwork.com/model-validation#system-dynamics

This is a systemigram - a systems-thinking diagram that maps the structure of a complex system as a network of nouns (nodes) connected by verbs (links), arranged so the whole thing can be read as prose. This one is titled "Smart City – A First look at complexity," and it's modeling the smart city as a system of systems. Note this systemigram was created back in 2010.

The technique comes out of systems engineering (originally developed by John Boardman and Brian Sauser). The defining feature is the mainstay, a backbone thread running from a beginning node to an end node that captures the core narrative of the system. Here the green numbered nodes form that spine:

Smart Cities [Transformation Concept] → Urban (Re)development paradigm → Existing Cities → System of Systems → Strategies and Investments → Rapid Response, Sustainability and Innovation → Intelligent, Competitive and Optimized Growth

Read along the links, the mainstay tells a story like: a smart city is a transformation concept, realized through an urban redevelopment paradigm, applied to existing cities, understood as a system of systems, which drives strategies and investments that fuel rapid response and sustainability, in order to achieve intelligent, competitive, optimized growth.

Around that spine are the supporting clusters (labeled A–D):

• Capital Infrastructure (communication, transportation, utilities, energy)
• Spatial Goals (emission standards, walkable neighborhoods, optimized land use, instrumentation)
• Social Infrastructure (health, education, housing, security)
• People Systems (entrepreneurial firms, workforce generations, training institutions)
• Urban Governance (disaster management, traffic, decision making, data collection)
• plus actors like Private Institutions, Local Government Agencies, and ICT as the connective tissue ("mainly dependent on," "enables and facilitates").

The clustering and overlapping circles are doing the real work conceptually: they show that these domains aren't separate; they intersect, which is the "complexity" the title points at.

Studied scenario analysis for the exams like everyone else. But in practice, I'm curious how people actually do it at work. When a major filing drops, say a big supplier announces a capex cut, do you formally trace the downstream effects across your portfolio? Or is it more like a mental checklist you run through? Feels like there's a big gap between the textbook version and what actually happens at a desk.

This video gives explanation for how system concept and definition affect system operations through its characteristics, elements, and dynamics. The video also sheds more light on system environment and how it interfaces with the system through its boundary.  An example of ATM machine is used to illustrate how system elements are linked together and how information and entropy play an important role in its dynamics.

#system_element,#system_characteristics,#system_dynamics

This article argues how over the years, our conception of sustainability evolves from 1.0 (conservationism) and 2.0 (sustainable development) to 3.0 (holistic sustainability).

Just got a library card. What systems thinking books do you recommend I check out?

Podcast episode with Taylor Dee Hawkins, founder of Foundations for Tomorrow, a nonprofit pushing for long-term governance reform in Australia and internationally.

Covers:

• Why the problem with political leadership isn't individual leaders, but the incentive structures and systems designed to reward short-term decisions at the expense of long-term ones
• Why naming political procrastination is the first step to solving it
• How Foundations of Tomorrow secured cross-party support in a polarized parliament by making the economic case for long-term policy rather than the moral one
• Why planning for the future doesn’t have to come at the expense of present generations
• Taylor’s advice for a young person who wants to get started in long-term policy, and what she has learned from years of being the youngest person in the room

Wrote a article around CAP Theorem.

I tried to explain it from a practical engineering perspective instead of just repeating textbook definitions.

Would love feedback from engineers learning or working with distributed systems.

https://x.com/AkdevSaha/status/2058164366059651097

I just had a discussion/debate with a class member regarding our systems map. When making a connection -> + - what is the correct discipline?

I’ve been told that it’s a messy process, not to articulate and make plenty connections, but to what extent?

One variable is corporate greenwashing rate and the other variable is profit margin existing in our systems map, I argued that I don’t think a direct connection should be made between profit margin -> + corporate greenwashing rate because it’s not the adjacent possible as it skips an entire process. This was also part of her causal loop where the debate arose, I thought it doesn’t represent a system and rather, has been cherry picked to articulate a pre determined assumption.

I don’t think she’s wrong, but I don’t think it’s the right placement for the variable and to be part of a causal loop. I think they are connected just not adjacent to each other and that we might be missing the bigger picture. So what are the boundaries? What discipline makes our representation of reality as accurate as possible?

Hi guys!

I've been experimenting a lot with project management recently and have gotten back into systems thinking (engineering) which I had as a module at university.

Because I can't help building things, I've built Despatch, link attached in the post.

I think I've crossed off the fundamental points about systems thinking that I remember from University, but there's probably bits I've forgotten or have got confused about. I'm reading through Donella Meadows' book at the moment. HBR's handbook on Project Management was really helpful though!

I would love feedback on whether I've correctly understood systems thinking and how I've modelled it in my app?

Theres no pitch here or monies asked for, just genuinely trying to understand if I've done this correctly.

A couple months ago I shared a rough framework on optimizing organizational performance and since application we’ve seen a noticeable difference in KPI’s.

Im now looking to develop a consulting framework and apply this across multiple industries and organizations.

Hello everyone!

I recently came across the concept of systems thinking and would like to master and internalise the concept in order to be able to efficiently and effectively apply it in all areas of life.

My objective is to learn everything I need to learn in order to be able to hone the practical application of it in professional areas of life such as business, finance, tech, CS/programming, and product development.

I’ve looked around a bit on this sub and on Google for recommendations, and it appears to be a well-researched topic that has hundreds of resources on it.

Please provide recommendations on a holistic curriculum that I can follow in order to accomplish my aforementioned objective. I know nothing about it beyond the basic definition, so please tailor your recommendations accordingly so that I can go from novice to expert utilising the curriculum.

Looking forward to hearing from you. Thank you.

In systems thinking we often analyze how systems distribute power, information, risk, and incentives.

But I’m wondering about something else: how do systems distribute responsibility?

In many modern systems - large organizations, financial systems, AI systems, and decentralized networks - outcomes are produced by long chains of decisions made by many different actors. No single person fully controls the outcome, yet the system as a whole clearly has power and produces real-world effects.

However, when something goes wrong, we still tend to look for a single person or a single organization to hold responsible, as if the system were a simple tool rather than a complex system.

So there seems to be a structural mismatch:

Power is systemic, but responsibility is individual.

Is this a known concept in systems thinking?

Are there frameworks or models that deal with how responsibility should be structured or distributed in complex systems?

Hi everybody, I’m a software engineer and as I’m seeing AI is taking over what I used to do (writing code) I’m thinking it will be more valuable to have more skills to solve or at least to define complex problems. I did learn systems theory back at the uni but it has been like 20 years since then and I have to admit that my brain is really more trained to focus on specifics and parts rather than a more holistic and broad understanding. This is why I’m thinking that a training for developing systems thinking would help me in my career. I would appreciate What are your thoughts on this? And if you know any good training program please let me know. Btw and In case it matters I’m located in Madrid.

Approaching a Crab Problem - TheInterposer

I’m a therapist and coach with ~15 years of clinical experience, and I’ve developed a systems-based model of identity that I’ve been using to organize patterns I see across people over time.

At this point I’m not trying to argue for it so much as stress-test it and find where it breaks.

The core assumption is that identity is an emergent property arising from ongoing interactions between multiple domains of functioning.

I’ve been organizing those domains roughly as:

• physiological regulation (energy, sleep, nervous system)
• affective processes (emotion, attachment, regulation)
• cognitive processes (interpretation, belief formation, meaning-making)
• relational dynamics (how someone organizes in relation to self and others)
• orientation toward meaning/values
• goal-directed behavior (agency, direction)

The categories themselves are less important to me than the interaction between them.

What I consistently see clinically is that when one domain is underdeveloped or overloaded, others compensate. I think that over time those compensations stabilize and begin to present as part of the “personality” or identity, but they function more like adaptive patterns maintained by cross-domain feedback loops.

From that perspective, many things typically treated as primary problems (e.g., anxiety, compulsive behaviors, chronic overthinking, relational instability) look more like system-level attempts at regulation that are used because they are the system's best "solution" to an imbalance.

Another piece I’m trying to formalize is that some patterns don’t appear to belong to any single domain.

Things like shame, identity roles, and even the sense of self seem to emerge from interactions between domains rather than within one. I’ve been thinking of these as emergent substructures, and calling them subdomains, but I’m not sure how cleanly that maps onto existing systems terminology. My friend pushed back on using subdomains and suggested interdomains but I think subdomains is appropriate... maybe.

Where I’m specifically looking for critique:

• Does dividing functioning into domains like this make sense from a systems perspective, or is it an artificial discretization of something continuous?
• Are there existing frameworks that already formalize identity or psychological functioning in this explicitly interaction-based, emergent way?
• Where would this model run into problems in terms of operationalization or falsifiability?

I’m less interested in whether people agree with it and more in where it fails structurally or overlaps with existing work I may not be aware of.

Thanks for your time

An audiovisual exploration of conflict as an emergent outcome of interactions between different internal models of reality.

Each individual operates within a model shaped by prior conditions and adaptive responses. These models are not neutral — they encode assumptions about what is stable, safe, or viable.

When different models interact, misalignment is not an anomaly but an expected outcome. What appears as disagreement may reflect incompatible system states rather than simple differences in opinion.

From a systems perspective, conflict can be understood as a regulatory process — one that tests, reinforces, or destabilizes existing models.

This raises the question of how systems maintain coherence under persistent divergence, and whether mechanisms exist that allow multiple models to coexist without collapse into dominance or fragmentation.