Dr. Benjamin’ Systems Thinking Blog

Can Little Kids Do “Complex Systems Thinking”? What comes to mind when you hear the term "Systems Thinking"? Perhaps you envision complex computer modeling, corporate strategy meetings, or advanced university research. But would you believe it if I told you that kindergarteners and elementary students are using these very same tools to resolve playground conflicts, analyze story plots, and co-create their own classroom rules? On February 26, the SDS Pre-College SIG hosted a webinar titled Systems Thinking for Young Learners: A Practical Introduction . In that session, Sara Stewart from Borton Elementary Magnet School delivered a presentation that challenged many of our assumptions. Her talk was titled  "Little Kids, Complex Thinking: Systems Thinking in Elementary PBL ." Borton Elementary, located in Tucson, Arizona, has made Project-Based Learning (PBL) and systems thinking part of its educational DNA. In this blog series, I will share concrete classroom examples ...

  I will introduce a pioneering case: the first-ever application of Systems Thinking in Gifted Education in South Korea.   In Korea, gifted education is operated by local education offices under the national education law. This program is unique for two reasons.  First, Systems Thinking was applied for the first time within an official, law-based gifted program. In other words, it received formal institutional recognition.  Second, the curriculum employs a single core concept: Stock–Flow. It is an experimental Systems Thinking program with no Causal Loop Diagrams.  In Systems Thinking, Causal Loop Diagrams (CLDs) and Stock-Flow Diagrams (SFDs) are often used together. Here, however, I intentionally designed the entire program using the Stock–Flow concept alone. Here is the overview. This program serves fifteen selected Grade 7 students, the first year of middle school in Korea. As mentioned, we approach system structures using the Stock and Flow concept vis...

[The Electronic Oracle] ④ Three Yardsticks That Keep Computer Models from Becoming Oracles Before introducing the types of models. Instead, this article organizes three critical distinctions— Structure/Parameter , Exogenous/Endogenous , and Accuracy/Precision —that we must verify to ensure any model does not become an "oracle." Questioning the Essence of Modeling: Three Core Standards from The Electronic Oracle In The Electronic Oracle , D. H. Meadows and J. M. Robinson place these ideas early because they separate not only methods, but mindsets. Authors define what we should look for in a model. These concepts are not mere definitions; they are criteria that distinguish the modeler’s attitude and philosophy toward the world. (p.12) 1. Structure vs. Parameter Structure + Endogenous = 'Why.' (Explanation) Parameters + Exogenous = 'Given.'  (Settings) Structure: Definition:  the system’s working mechanism—the pattern of causality that generates behavior over tim...

  [The Electronic Oracle] ③ Assumptions Aren’t “Guesses”: A Proposal for Transparent Democracy When I teach System Dynamics or computer modeling, I often encounter a word that instinctively annoys both students and practitioners: "Assumption." The dictionary reveals the source of this discomfort. Assumption :  “a thing that is accepted as true or as certain to happen, without proof .” (Oxford Languages) For scientists, the phrase "without proof"  can sound like an insult. So when someone says, “That’s just your assumption,” scientists rush to defend themselves: "No! I have evidence!" In this post, I want to restore the honor of this word. Modeling assumptions are not mere tricks to patch missing data; they are influential political acts that transform secret dictatorships into transparent democracies. This article distinguishes two layers of assumptions: The Assumption as Belief (Type A)   hides in the mind and operates on authority The Model Assumption (T...

  [The Electronic Oracle] ② The Questions to Ask When You Face a Model: A Checklist for Citizens and Policymakers Before we turn to the technical yardsticks for evaluating models (Post ④—research) and the philosophy of assumptions (Post ③), we need to learn how to ask questions. A computer model becomes a modern “oracle” when it dulls our judgment. To prevent that, here are three questions you should ask every time you meet a model. This checklist is not mainly for model builders. It is for model users—citizens, journalists, policymakers, and administrators. Question 1: “What number is currently serving as our ‘authority’?” (Focus: guarding against false certainty) A model becomes an oracle when its output—stripped of context—poses as the essence of reality. Checklist Is the number accepted without proof? Is this number used as the sole justification for a policy decision? Have the conditional statements ("if") attached to the number been cut from meetings, reports, or d...

 South Korea, having recently undergone the shock of an attempted self-coup by a sitting president in the 21st century, is currently abuzz with a topic that has surfaced from the depths of our society: "Shamanism." Why do people rely on shamanism to make critical decisions? The roots of this behavior run deep. When the ancient Greeks felt anxious about the uncertain future, they sought answers from the Oracle of Delphi , even for decisions that would determine the fate of their nation. This blog series will explore computer models , which serve as the standard for modern decision-making, much as the Delphic Oracle did in the past. There is a growing tendency to treat computer models as "objective" and "omniscient," akin to the ancient oracles. With the integration of Artificial Intelligence , this tendency has intensified. We often marvel at the spectacular results without scrutinizing the underlying reasoning process, effectively granting these models th...