FAQ

What is Teach Yourself Systems and who is it for?
TYS is an interactive learning resource that helps engineers, product people, analysts, and curious thinkers understand, design, and manage complex systems. It combines short concept chapters with hands‑on simulations so you can see structure turn into behaviour in real time.
How is the site organised?
Content is split into two main areas: Chapters – the core ideas (Purpose & Boundary → Stocks & Flows → Feedback Loops → Delays → Leverage Points → Emergence → Dynamic Behaviour Patterns) and Examples – ready‑made models you can open in the Playgroundand tweak. Each chapter page links forward and back, so reading in order or dipping in is equally easy. Browse all Examples when you want more practice.
I’m brand‑new to systems thinking—where should I start?
Follow the chapter sequence shown above. Beginning with Purpose & Boundarygrounds you in what makes something a system. Only after that does it make sense to study Stocks & Flows, Feedback Loops, and so on, because each concept builds on the previous one.
What’s the difference between a “collection” and a “system”?
A collection is just parts in a pile; a system is those parts arranged so their interactions achieve a purpose (e.g., bike parts vs. a functioning bicycle). Relationship, not the parts themselves, creates capability. See Purpose & Boundaryfor more on what distinguishes a system from a loose collection.
How do I decide where to draw system boundaries?
Boundaries are analytic choices, not facts. Shift them to reveal new inputs, outputs, and leverage points—for instance, carbon accounting changes radically when you widen a coffee‑shop boundary to include farming and shipping. Read more in Purpose & Boundary.
Why do the chapters start with Stocks & Flows?
Stocks give a system memory; flows are the only things that change those stocks. Until you can spot them, later topics (loops, delays, leverage) have nothing to “act on.” Learn more in Stocks & Flows.

Bathtub Fill and Drain

A bathtub simulation illustrating stock and flow of water volume.

What’s the practical difference between reinforcing and balancing feedback loops?
Reinforcing (positive) loops amplify change and often create exponential growth or decline. Balancing (negative) loops counter change and seek equilibrium, sometimes producing oscillations when delayed. Knowing which dominates tells you whether you need to pour fuel on the fire or damp it down. See Feedback Loopsfor a deeper explanation.

Logistic Growth

A logistic growth simulation of population increase with saturation.

Thermostat Simulation

A thermostat simulation showing balancing feedback and time delays.

How do delays create oscillations and overshoot?
When information or actions arrive late, decision‑makers “drive using the rear‑view mirror,” over‑correct, and set up cycles of too‑much / too‑little—classically seen in inventory swings and boom‑bust markets. The Delayschapter explores this in depth.

Thermostat Simulation

A thermostat simulation showing balancing feedback and time delays.

How can I find high‑leverage intervention points?
Look deeper than parameters: information flows, rules, goals, and even paradigms outweigh budget tweaks. Changing incentives or the system’s goal often beats doubling resources. Read about strategicLeverage Pointsto find where small shifts make a big impact.
What does emergence mean here?
Emergence is the appearance of qualitatively new properties produced by interactions—wetness from H₂O, culture from countless Slack messages—none of which exists in the parts alone. It reminds us that fixing isolated pieces can miss the real problem living “between” them. See the Emergencechapter for more.

Game of Life

Conway's Game of Life producing grid frames.

How do I recognise key dynamic behaviour patterns?
TYS catalogues fingerprints such as exponential growth, goal‑seeking decay, overshoot‑and‑collapse, and S‑curve saturation. Learning these patterns lets you anticipate a system’s trajectory before numbers shout the story. Visit Dynamic Behavior Patternsto explore common shapes.

Logistic Growth

A logistic growth simulation of population increase with saturation.

Logistic Map

A logistic map simulation that iterates x_{n+1}=r*x_n*(1-x_n) to illustrate chaos.

What’s the best way to practise?
Open any Example model, press Run, and tweak one knob at a time. Watching the time‑series graph respond cements intuition faster than reading another paragraph. Browse the Examples libraryor head straight to the Playground.
Do I need special software or a powerful machine?
No. All simulations run in the browser; nothing to install. If the model list feels slow, enable JavaScript and use a modern browser. Just open the Playgroundin any modern browser.
How do I use the playground controls?
Play / Pause – runs the model. Sliders / number boxes – edit parameters live. Probes – choose which variables to plot. Small, deliberate experiments (change one variable, observe, change it back) build understanding quickly. The Playgroundpage explains each control.
Where can I find extra reading or tools?
See the Resources pagefor curated lectures (e.g., Donella Meadows’ 1977 talk), visual tools like LOOPY, and a link to the code playground for advanced tinkering.
Where do I look up unfamiliar jargon?
The Glossarydefines every highlighted term, links back to a deeper chapter, and often embeds a runnable model for context. Use it whenever a chapter uses a word you can’t quite place.
How can I send feedback or request new content?
Every page footer lists the maintainer’s email and X/Twitter handle (@andreisavu). Drop a note with suggestions, corrections, or models you’d love to see.
How does this FAQ differ from the Glossary?
Glossary – “What does this word mean?” FAQ – “Why does it matter, how do I use it, and what should I read or try next?” Think of theGlossary as a dictionary and the FAQ as the course instructor standing beside you.