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.
— Part 2
- 3) Why systems work so well?
Resilience: + dynamic -> learn. – sacrifice resilience for stability
Delf-organization: capability to make its own structure more complex. Produces heterogeneous + unpredictability
Hierarchy: evolve from bottom up. The top serves the purpose of the lower layers.
- 4) Why systems surprise us?
World is greater than our knowledge. We can make only models in our heads (never exact to reality)
Behaviour = performance over time. System structure is the source of behavior.
The non-linear relationships: This is something we struggle to deal with (and to notice)
We need to create boundaries so we can ask question
What’s the limiting factor: there is always a limit to growth
Bounded rationality may not lead to the better decision that improve the system (this kills the idea of the market takes care by itself for the best – there is always somebody that makes a killing and can’t be good)
- 5) System traps and opportunities?
Policy resistance (drugs): Seek mutual satisfactory for all goals from all parts
Tragedy of the commons (immigration): Educate users to understand consequences of abuse. Privatise or regulate.
Drift to low performance: Enforce standards by best actual performance.
Escalation (violence/war – nuclear heads): Not getting involve in first place or refuse to complete.
Success to the successful: Diversification: try something else. Limit the winner prize.
??? Solution to a systemic problem reduces (or disguises) the symptoms, but does nothing to solve the underlying problem: Focus on long-term restructuring instead of short-term relief.
?? Rule beating: perverse behavior that gives appearance of obeying the rules or achieving the goals, but that actually distorts the system. Design or redesign rules based on feedback, always aiming to achieve of the goal of the rule.
?? Seeking the wrong goal (GNP is not a good goal)
— Part 3
- 6) Leverage points: Places to intervene
MIT’s Jay Forrester: The source of all problems is growth (populations and economic) Leverage points frequently are not intuitive
Numbers: constants and parameters such as subsidies, taxes, standards, etc
Buffers: the size of stabilizing stocks relative to their flows.
Stock-and-flow-structures: Physical systems and their nodes of intersection (energy conservation: straight out bent pipes and enlarge the too small ones)
Delays: the length of time relative to the rates of system changes.
Balancing Feedback loops: the strength of the feedbacks relative to the impacts they are trying to correct. Big mistake is to remove these “emergency response mechanisms because they are not used often and they look costly (ie: emergency cooling system in nuclear plant)
Reinforcing feedback loops: The strength of the gain of driving loops
Information flows: The structure of who does and doesn’t have access to information
Rules: Incentives, punishments, constraints
Self-Organization: The power to add, change or evolve system structure.
Goals: The purpose or function of the system.
Paradigms: The mindset out of which the system (its goals, structure, rules, delays, parameters, etc) arises
Transcending paradigms: Keep oneself unattached in the arena of paradigms, to stay flexible
- 7) Living in a world of systems
Getting the behaviour of the system forces you to focus on facts, not theories.
Get your models outside of your head: discuss with others, this creates mental flexibility.
Honor, respect and distribute information: Information is power.
Keep your language as concrete, meaningful and truthful as possible.
Pay attention to what is important, not just what is quantifiable: how you measure justice, democracy, freedom, etc?
Make feedback policies for feedback systems: Jimmy Carter examples: oil tax depending on the import levels (more import, more tax), Mexico immigration (help Mexico to improve society instead of creating walls and putting more security in the border)
Improve a system as a whole (hierarchies exist to serve the bottom layer, not the top)
Listen to the Wisdom of the System (before trying to make any change): Example, Aid worker trying to improve things in Guatemala.
Locate and apply responsibility in the system.
Stay humble (you will make mistakes), stay a learner
Complexity is expected: it is part of evolution
Expand the time horizons: ie Native Americans think of the consequences for the 7th generation. We think too short-term
Seeing systems as a whole requires more than interdisciplinary. We need to learn and communicate
Dont weight the bad news more heavily than the good ones. Keep the standards absolute.
