My first exposure to systems thinking wasn’t through Donella Meadows. It was years ago, during my undergraduate studies, in a course called Systems Analysis, taught by Dr. Farshid Abdi. The way he structured the course made it so engaging that I kept attending, even after I had completed it. He had this ability to take abstract concepts—feedback loops, stocks and flows, emergent behavior—and make them feel tangible, almost intuitive.
At the time, our main textbook was Business Dynamics by John D. Sterman, which introduced me to the world of causal loops, simulations, and mathematical modeling. But Dr. Abdi didn’t stop there. He encouraged us to explore beyond the equations, handing us side books like The Fifth Discipline by Peter Senge and The Goal by Eliyahu Goldratt—works that framed systems thinking in more practical and strategic terms. Those readings shaped the way I initially understood systems: as structures that could be analyzed, modeled, and optimized.
Years later, coming across Thinking in Systems, I immediately noticed a different approach. Meadows doesn’t just break systems down into technical models; she frames them as living, evolving structures that follow patterns we can observe and, sometimes, change. While I haven’t read the book in its entirety, I’ve reviewed key sections, and what stands out most is how she emphasizes relationships over calculations, mindsets over mechanics.
One of the most striking parts of the book is her discussion of archetypes—common system structures that explain why certain patterns of behavior repeat across industries, economies, and societies. Reading about these archetypes made me reflect on how I’ve seen similar structures emerge in AI-powered project management systems. The same reinforcing and balancing loops that determine whether a business thrives or collapses also shape how AI models adapt to real-world uncertainty. It reinforced something I’ve been thinking about: systems thinking isn’t just about understanding complexity—it’s about recognizing patterns and finding the leverage points to shift them.
Meadows’ idea of leverage points—places where a small change can lead to significant transformation—also resonated with me. In AI-driven decision-making, tweaking a single parameter can have exponential effects on model behavior, much like changing an incentive structure can transform an entire organization. Meadows explains that the most powerful leverage points aren’t just adjusting numbers within a system; they’re changing the system’s goals, rules, or fundamental structures. This is something I’ve encountered firsthand—when working on adaptive AI models, it’s not just about making better predictions; it’s about designing systems that can adjust themselves when the unexpected happens.
Looking back, I realize that had I come across this book earlier, it would have given me a broader conceptual foundation alongside the technical depth of Business Dynamics. Whereas Sterman’s work gave me the tools to model and quantify systems, Meadows’ approach makes me think about how systems evolve and how change actually happens. The more I work in AI, the more I see the importance of both perspectives.
I know I’ll have to read this book in full at some point. The more I explore systems—whether in AI, resilience, or decision-making—the more I realize that the hardest problems aren’t just technical. They’re structural. And understanding how systems behave, adapt, and sometimes resist change is just as important as building better models to predict them.