Loops, Time Delays, Accumulations and Change Over Time
On this page 6 headings
Start here You’re hungry, so you eat. A few hours later you’re hungry again. Cause and effect often run in circles, not straight lines. And sometimes the effect shows up much later than the cause.
Habit: See the circular nature of cause and effect (feedback loops)
In many systems, A causes B, and B in turn affects A. This is a feedback loop.
| Example | The loop |
|---|---|
| Hunger | Hungry → eat → full → hungry again |
| Prof. Shiva’s health (page 1) | Less walking → higher sugar → higher BP → anxiety → higher BP |
| Rumours in an office | A draft policy leaks → whispers spread → people react → leaders sense the reaction → the decision is shaped |
Lecture example — the king in disguise Old kings would dress as common people and sit at tea shops to hear what people said about an idea. They used that feedback before taking the final decision.
Lecture example — words shape actions A child hangs from a bar. One passer-by says, “Very good, hold on tight!” Another says, “Don’t — you’ll fall!” The brain grabs the word “fall”, and a fall becomes more likely. “Great minds discuss ideas, average minds discuss events, small minds discuss people.” What we think becomes what we say, and what we say becomes what we do.
Technology also evolves through loops of feedback and improvement. Radio went from a big box on the wall to a phone app; cameras went from 36-photo film rolls to instant digital photos.
Habit: Recognise the impact of time delays
Cause and effect are often far apart in time. A systems thinker:
- understands how long the “gestation period” will be before results show, and
- tells people about the delay, so they don’t get anxious.
Lecture example — the new education policy It wasn’t brought in overnight. Between the announcement and the final policy, the public gave suggestions, committees and experts discussed them, and social media was full of debate. That delay helped shape a better policy.
Everyday example: you start exercising today, but you see results only after weeks. Expect the delay; don’t quit after day 3.
Habit: Pay attention to accumulations and their rates of change
Some things build up (accumulate) or run down over time. Notice what is accumulating and how fast.
| Example | What accumulates |
|---|---|
| Elections | Candidates try to build up supporters and votes, from national politics to housing-society elections |
| Factory inventory | Some items move fast and run out; others pile up as dead stock. Both are unhealthy. Just-in-time stock control prevents unwanted build-up. |
Habit: Observe how elements change over time (patterns and trends)
Lecture example — rainwater harvesting, 20 years later (Prof. Kartic) The 1990s system worked well. But over 20 years:
- Cars increased, so the soil around the building was paved over and less rain soaked in.
- More concrete houses were built nearby, and more washing machines meant more waste water.
- The groundwater became salty, and the city drains began to overflow. Lesson: a one-time fix isn’t enough. Systems change, so keep monitoring and revising, and help everyone understand the bigger system.
Habits: Test assumptions, and check results by “successive approximation”
Ask “What if…?”: Who else will be affected? What else would change? Try something, observe, adjust, and try again.
Lecture example — work from home after COVID (Prof. Kartic) Before COVID: long commutes; offices far from the city; companies needed to be near talent. During COVID: everyone worked from home, and companies and employees adapted. After COVID: no commute and lower office costs, but weaker team bonding and harder monitoring. Companies are now testing options: “What if people come to office twice a week?” “What if we rent workstations near employees’ homes?” They observe, fine-tune and try again. The likely result is a hybrid model.
In one line: Look for feedback loops, expect time delays, watch what builds up and how fast, track how systems change over time, and keep testing your assumptions. Exam words: circular causality, feedback loop, time delay, gestation period, accumulation, rate of change, dead stock, just-in-time, patterns and trends, test assumptions, “what if” questions, successive approximation, hybrid work.