PLACES TO INTERVENE IN A SYSTEM (in increasing order of effectiveness)

  1. Constants, parameters, numbers (such as subsidies, taxes, standards).
  2. The sizes of buffers and other stabilizing stocks, relative to their flows.
  3. The structure of material stocks and flows (such as transport networks, population age structures).
  4. The lengths of delays, relative to the rate of system change.
  5. The strength of negative feedback loops, relative to the impacts they are trying to correct against.
  6. The gain around driving positive feedback loops.
  7. The structure of information flows (who does and does not have access to information).
  8. The rules of the system (such as incentives, punishments, constraints).
  9. The power to add, change, evolve, or self-organize system structure.
  10. The goals of the system.
  11. The mindset or paradigm out of which the system — its goals, structure, rules, delays, parameters — arises.
  12. The power to transcend paradigms.
  1. constants, parameters, numerical values (subsidies, taxes, reference values, etc.)
  2. the size of buffers and other stabilizing stocks relative to their flows.
  3. structure of material stocks and flows (transport networks, population age structure, etc.)
  4. the length of the delay relative to the rate of change in the system.
  5. the strength of the negative feedback loop is relative to the impact it is trying to compensate for.
  6. Gain to drive positive feedback loop.
  7. the structure of information flows (who gets information and who does not).
  8. system rules (incentives, punishments, constraints, etc.).
  9. the ability to add to, change, evolve, or self-organize the structure of the system.
  10. Goals of the system
  11. the idea or paradigm from which the system (its goals, structure, rules, delays, parameters) emerges.
  12. The power to transcend paradigms.

This page is auto-translated from [/nishio/Leverage Points: Places to Intervene in a System](https://scrapbox.io/nishio/Leverage Points: Places to Intervene in a System) using DeepL. If you looks something interesting but the auto-translated English is not good enough to understand it, feel free to let me know at @nishio_en. I’m very happy to spread my thought to non-Japanese readers.