Thinking in Systems
We want to think of systems as a series of events each of which causes the other. We want to focus on the flows and try to link those flows as causal events.
In reality, however, systems consist of flows, stocks, and loops; all of which effect multiple aspects of a system. Therefore, a single event could have an impact on even seemingly disconnected flows, or the impact of the event could be delayed by a stock.
Behavior based models work great for predicting the near future outcomes but do a poor job in determining long term behavior.
We like linear relationships because they are easy to calculate and visualize. We can solve linear equations and draw graphs of the relationships. Unfortunately, most systems behave in nonlinear ways that can’t be easily solved or visualized.
We often get surprised by nonlinear systems because they can act in a linear fashion up to a point, and then hit a dynamic shift that completely boggles our expectations.
We see examples non-linearity in applying fertilizer to plants, advertising, and even in the amount of time we dedicate to work. All of these have a positive benefit until, suddenly, they start having a negative effect when applied to liberally.
The world contains no separate systems. Everything is connected together and on a long enough timescale all system flows will impact each other.
Despite the interconnections of everything, we must use boundaries if we wish to understand any scenario. The question becomes where is the appropriate place to draw our boundaries.
Every system has limiting factors that surround it’s growth. By addressing the most limiting factor, a systems growth can resume. But undoubtedly, another one will soon arise.
We can only gracefully handle limiting factors if we impose them on the system ourselves. Otherwise, the system will produce one in the environment with potentially disastrous side effects.
Foresight is essential in systems because every system contains many delays. Without foresight, a problem will continue to escalate in a system because the cause of the initial problem was delayed while the behavior that caused it continued.
The actors in the system work with limited information and don’t act in fully rational ways. Because of this, blaming or replacing any of the actors will rarely have any effect. The best way to change the outcome of a system lies in modifying the system itself.