Thinking in Systems

I struggle to give a good summary of this book. My take is how to see systems (a very generic word) in the big picture as most systems are too complex to understand how fully work (economy, stock market, etc). In general, once we see the relationship between structure and behaviour, we can start to understand the system and modify it. You can’t know a system just by its parts. Look for the interconnections of those parts.

A system is formed by “stock” (water in a reservoir, mineral deposits, etc) and the stock changes overtime due to the actions of “flows” (rain = inflow, evaporation = outflow, mining = outflow, etc) Inflow increases the stock. Outflow decreases the stock. If the rate of inflow and outflow is identical, you have a system in a state of dynamic equilibrium. You want to see the systems behaviour based on time. Generally, stocks change slowly compared with the rate of change of in flows. So stocks act like a “buffer” in systems. A feedback loop is formed when changes in a stock affect the flows into or out of that same stock. You have two types of feedback loops: balancing (seek stability and resistance to change) and amplifying/reinforcing (can cause healthy growth or destruction) (ie: learning piano, the more I practice, the more I learn, the more keep practicing and so on). Doubling time = 70/growth rate (It takes 14 years to double your money in a back at a rate of 5%) In real systems, a single stock can be influenced by several types of feedback loops (with different directions and strengths)

The information delivered by a feedback loop can only affect future behaviour (can’t have an impact fast enough to correct the behaviour triggered the current feedback). And there will always be delays in responding. As well, because systems often have competing feedback loops working at the same time, the loop that dominates the system will determine the behaviour. You can have shifting dominance of feedback loops (dead rate vs birth rate)

System dynamics models explore possible futures and ask “what if” questions. Testing the value of a model: 1) Are the driving factors likely to unfold this way? 2) If they did, would the system react this way? 3) What is driving the driving factors?

Dynamic systems studies are designed to explore what would happen if a number of driving factors unfold in a range of different ways.

Systems largely cause their own behaviour. Systems with similar feedback structures produce similar dynamic behaviours, even if the outward appearance is not similar (population vs industrial economy, coffee cup cooling vs radioactivity decay)

A delay in a balancing feedback loop makes a system likely to oscillate (ie: response of orders and deliveries in a car dealer). Delays are pervasive and are strong determinants of behaviour. Changing the length of a delay may (or nor) make a large change in the behaviour of a system.

Examples of two-stock systems:

  • A renewable stock (capital) constrained by a non-renewable stock (oil): oil company: Non-renewable resources are stock-limited. The entire stock (oil) is available at once and can be extracted at any rate (limited by extraction capital). The faster the extraction rate, the shorted the lifetime of the resource.
  • A renewable stock (capital) constrained by a renewable stock (fish): fishing company: Renewable resources are flow-limited. They can support extraction indefinitely but only at a finite flow rate equal to the regeneration rate;

No-physical system can grow forever in a finite environment.