Use this when you suspect a problem is systemic but can't see the connections — when the variables are clear enough to list but the relationships between them remain invisible. Connection Circles force you to map how elements in a system influence each other, revealing feedback loops and hidden dependencies that linear thinking consistently misses.
Section 1
What This Tool Does
Most people think in lists. When confronted with a complex situation — declining user engagement, a supply chain that keeps breaking in new places, a team that ships features but never moves the metric — the instinct is to enumerate the factors. Pricing.
Competition. Onboarding friction. Engineering velocity. The list grows. Each item sits in its own row, isolated, waiting for someone to prioritise it. And that's where the thinking stalls, because the real problem was never any single item on the list. The real problem is how those items interact.
Connection Circles emerged from the systems thinking tradition, most directly from the work of practitioners at the MIT Sloan School of Management and the Waters Foundation, which adapted systems dynamics tools for broader use. The technique is deliberately primitive — a circle, a set of elements written around its perimeter, and arrows drawn between elements that influence each other. No software required. No mathematical modelling. Just a group of people with a whiteboard, forcing themselves to answer one deceptively difficult question for every pair of elements: "Does this thing change that thing? And if so, in which direction?"
The mechanism is almost embarrassingly simple, which is precisely why it works. You write 5–10 key variables around the circumference of a circle. Then you draw directed arrows from each element to every other element it causally influences, marking each arrow with a "+" (same direction — when A increases, B increases) or a "−" (opposite direction — when A increases, B decreases). What emerges is a web of relationships. And within that web, you start to see loops — chains of influence that circle back on themselves, either amplifying (reinforcing loops) or stabilising (balancing loops). The core cognitive shift is this: you stop seeing a collection of independent problems and start seeing a system of interdependent behaviours, where intervening on one element ripples through every element it touches. That shift changes which interventions you choose, which metrics you watch, and which "obvious" fixes you learn to distrust.
Connection Circles sit at the entry point of systems mapping. They're less rigorous than formal Causal Loop Diagrams and far less technical than
Stock and Flow models. That's the point. They're designed to be the first systems tool a team reaches for — the one that requires no training in systems dynamics notation, no specialised software, no facilitator with a PhD. A product manager, a founder, a department head can draw one in fifteen minutes and surface structural insights that months of linear analysis missed. The tradeoff is real: Connection Circles sacrifice precision for accessibility. They won't give you simulation-ready models. But they will show you where the feedback loops are hiding, and that's usually enough to stop you from making the most expensive mistake available — optimising a single variable in a system where everything is connected to everything else.
