All in all an interesting book, but didn't feel as transcendent to me as others have made it out to be. The overwhelming message seems to be that 'life isn't so simple' and that most everything needs a great deal of nuance to make changes or corrections.
'All models, whether mental models or mathematical models, are simplifications of the real world.' (Page 22)
Life is more complex than we ordinarily like to admit, meaning problems are rarely easy to solve; they don't exist in a vacuum. Instead they tend to impact and make changes elsewhere in broader systems that we function in. These systems may also be responsible for maintaining and reproducing those same problems, though... Drug addiction, war, poverty, famine all persist despite the best intentions of smart people with clever solutions.
'A system is a set of things-people, cells, molecules, or whatever-interconnected in such a way that they produce their own pattern of behavior over time. The system may be buffeted, constricted, triggered, or driven by outside forces. But the system's response to these forces is characteristic of itself, and that response is seldom simple in the real world.' (Page 2)
Three things make a system:
Universities, plants, football teams, cities, shops. All are systems.
On elements: can be tangible, intangible, or both. Easier to see elements and understand them than their connections.
On connections: can also be tangible or intangible. Information is a significant intangible one that we often don't think about explicitly.
Functions/purposes: hardest to see. Observation is usually the only way to infer what they might be.
'System purposes need not be human purposes and are not necessarily those intended by any single actor within the system. In fact, one of the most frustrating aspects of systems is that the purposes of subunits may add up to an overall behavior that no one wants.' (Page 15)
Changing elements may not greatly affect the system, but changing connections or the function/purpose will often have a much bigger impact.
A stock is any accumulation or resource that exists to be used within a system. They are determined by flows—in and out—such as births and deaths, profits and losses.
Meadows uses the example of a bathtub to show stability within a system. You can achieve both inflow, outflow and dynamic equilibrium.
'Stocks take time to change, because flows take time to flow.' ...and therefore act as buffers or delays.
'Instead of seeing only how A causes B, you'll begin to wonder how B may also influence A-and how A might reinforce or reverse itself.' (Page 33)
Systems are controlled by feedback loops - changes in the stock that change the inflow or outflow of that stock.
You also get 'goal-seeking feedback', loops that adjust until stocks hit certain points—such as coffee temperature in a heated coffee pot—as well as reinforcing feedback loops that enhances the direction of change one way or another.
'Delays are pervasive in systems, and they are strong determinants of behavior. Changing the length of a delay may (or may not, depending on the type of delay and the relative lengths of other delays) make a large change in the behavior of a system.' (Page 57)
Two competing balancing loops: room temperature with a thermostat and outside temperature. Feedback loops can only affect future behaviour, never the current. This means that there will always be a delay in responding.
One reinforcing loop and one balancing loop: Births > Population < Deaths. Depending on which loop is dominant, the populations will either shrink or grow. Reinforcing loop dominant = exponential population growth. Balancing loop dominant = population eventually shrinks to zero.
Capital stock is another example of the above: Investment > Capital < Depreciation.
A system with delays - business inventory: Deliveries > Cars in Showroom < Sales. Feedback loops include perceived sales, discrepancies, customer demand, orders to factory. This is also affected by delays: perception, response and delivery delays. Delays can lead to wild oscillations in stock levels. Sometimes it can be better to delay responses than to speed them up, or vice versa... but urgency makes us do unusual things.
Renewable stock constrained by a nonrenewable stock - oil economy: Growth with constraint is common but there are always balancing loops because unlimited growth is not possible. Source of balancing loops as non-renewable will determine how growth ends.
The size of the non-renewable resource will affect how long it takes for it to become difficult to extract. As it depletes, it gets harder and more expensive to do so. If non-renewable, the end comes quickly and all at once.
Renewable constrained by renewable - fish economy: renewable sources are flow limited. Can theoretically be extended forever but if extracted too quickly (creating a larger market for fish and therefore greater aggregate demand), become nonrenewable and 'desertification' happens.
'This simplified model of a fishery economy is affected by three nonlinear relationships: price (scarcer fish are more expensive); regeneration rate (scarcer fish don't breed much, nor do crowded fish); and yield per unit of capital (efficiency of the fishing technology and practices).' (Page 67)
There are levels of resilience within the system itself that, on a meta level, rebuild and repair the system with feedback loops. Think of ecosystems, such as the human body. Resilience often sacrificed for stability or productivity because it is often not that obvious
'Resilience is a measure of a system's ability to survive and persist within a variable environment. The opposite of resilience is brittleness or rigidity.' (Page 76)
The ability of systems to organise into forms, actions, and tends to run contrary to power structures. E.g. DNA & RNA self-organise into organisms. There are first instructions, and these follow immutable rules (e.g. physics, chemistry) as well as particular mutable principles (politics, philosophy, movements, culture, society etc.)
'You see self-organization in a more profound way whenever a seed sprouts, or a baby learns to speak, or a neighborhood decides to come together to oppose a toxic waste dump.' (Page 79)
Matryoshka systems - subsystems within larger systems. Allows subsystems to do their jobs better, establishes appropriate allocation of work.
'Hierarchical systems evolve from the bottom up. The purpose of the upper layers of the hierarchy is to serve the purposes of the lower layers.' (Page 85)
'When a systems thinker encounters a problem, the first thing he or she does is look for data, time graphs, the history of the system. That's because long-term behavior provides clues to the underlying system structure. And structure is the key to understanding not just what is happening, but why.' (Page 89)
We are too fascinated with, and fixated on, events. This stops us seeing bigger picture things. This is because we have linear minds, but we live in a mostly nonlinear world.
Systems rarely have real boundaries - we can find overlaps and complexities everywhere. Everything comes from somewhere and everything then goes somewhere.
Boundaries are own creation, and we ought to reconsider them for each new discussion.
'The greatest complexities arise exactly at boundaries. There are Czechs on the German side of the border and Germans on the Czech side of the border. Forest species extend beyond the edge of the forest into the field; field species penetrate partway into the forest. Disorderly, mixed-up borders are sources of diversity and creativity.' (Page 95)
Systems also have 'layers of limits'. This is not dissimilar to the theory of constraints and mostly states that there are different limiting factors at different points depending on growth rate and size and so on, and so on.
'There always will be limits to growth. They can be self-imposed. If they aren't, they will be system-imposed.' (Page 103)
Bounded rationalities: systems/people/orgs are rational with information... but they never have all the information.
There are certain archetypal systems that produce problematic behaviour, like addiction, the drift to low performance and escalation. These can be traps but they can also be turned into opportunities.
Subsystems with different goals, inconsistent with each other. Changing policies only looks at part of the problem. Other subsystems compensate. Can often 'ratchet up' the situation, needing first to de-escalate things.
You can either try to overpower the whole system (which has significant consequences) or just let go (which feels risky but can often work). Many of the countermoves you didn't like were likely happening because of you in the first place.
'Policy resistance comes from the bounded rationalities of the actors in a system, each with his or her (or "its" in the case of an institution) own goals. Each actor monitors the state of the system with regard to some important variable-income or prices or housing or drugs or investment and compares that state with his, her, or its goal. If there is a discrepancy, each actor does something to correct the situation.' (Page 113)
Bounded rationality again, making (in this case) farmers add cows to a pasture until it is useless. 'There's no reason for me to limit the number of cows I put on this common land!' There's no feedback loop to the user telling them that this is a bad idea.
Three ways to solve:
Delta between perceived and actual state. But the perceived state affects the desired state, so goals slip. This is a reinforcing feedback loop. 'Things aren't that bad' / boiled frog syndrome.
How to solve:
Competing actors trying to get ahead of one another, e.g. an arms race, negative smear campaigns, price wars or the 'race to the bottom'.How to solve:
Reinforcing loop: 'to those that have shall more be given'. Winners receive the means to compete more effectively in the future, making them more likely to be the winners... and to keep the loop reinforcing. 'Competitive exclusion principle'.
Diversification may happen, but the same problem may just happen yet again.
Solve: antitrust / levelling the playing field.
There's a reliance on drugs, public money, private institutions to help solve here. But states created by intervention don't last, they wear off.
'Then the original problem reappears, since nothing has been done to solve it at its root cause. So the intervenor applies more of the "solution," disguising the real state of the system again, and thereby failing to act on the problem. That makes it necessary to use still more "solution."' (Page 133)
Rather than defaulting to an intervention or 'takeover', instead ask three questions:
When people do things that meet the letter of the law, bug et around it and break the spirit of the law. Usually caused by ill thought-out and rigid rules in the first place. Solved by designing better rules!
Often get effects confused with results, the need to simply choose a measure (even if it's wrong). If you define a rational goal as GDP growth, there's an awful lot of things that won't also grow (welfare, equity, justice). Solve by, again, choosing better goals (or possibly not choosing them at all [WP])
People often know where to find the switches that they need to be pulling, but often pull them in the wrong direction. Most of them are counterintuitive. In descending order:
12. Numbers (e.g. subsidies, taxes, standards): people get passionate about these, but altering them rarely changes the dynamics of a system, e.g. tax vs spending in the government.
11. Buffers: size of the stabilising stock relative to the flows. Again, doesn't fundamentally change system dynamics.
'You can often stabilize a system by increasing the capacity of a buffer. But if a buffer is too big, the system gets inflexible. It reacts too slowly.' (Page 150)
10. Stock and Flow Structures: insufficient structures make things difficult, so rebuild them. Can be slow and expensive, though.
9. Delays: high leverage, but hard to change.
'A delay in a feedback process is critical relative to rates of change in the stocks that the feedback loop is trying to control. Delays that are too short cause overreaction, "chasing your tail," oscillations amplified by the jumpiness of the response. Delays that are too long cause damped, sustained, or exploding oscillations, depending on how much too long.' (Page 152)
8. Balancing Feedback Loops: are they strong enough relative to the impact they are trying to control?
7. Reinforcing Feedback Loops: source of growth, erosion, explosion, collapse.
'Look for leverage points around birth rates, interest rates, erosion rates, "success to the successful" loops, any place where the more you have of something, the more you have the possibility of having more.' (Page 156)
6. Information Flows: Location of electric meters affects energy usage, tragedy of the commons because no information available about the state of the field.
5. Rules and Incentives: rules can completely change what people do and don't do and how they do it. As they can in any system.
'If you want to understand the deepest malfunctions of systems, pay attention to the rules and to who has power over them.' (Page 158)
4. Self-Organisation: the ability to change a system in flight, as it's moving.
'Self-organization is basically a matter of an evolutionary raw material-a highly variable stock of information from which to select possible patterns-and a means for experimentation, for selecting and testing new patterns.' (Page 160)
3. Goals: purpose in a system, the aim of what it wants in the first place. Has the power to drive large groups to do new things.
2. Paradigms: widely held sets of unquestioned beliefs in what is 'true'.
'The shared idea in the minds of society, the great big unstated assumptions, constitute that society's paradigm, or deepest set of beliefs about how the world works. These beliefs are unstated because it is unnecessary to state them-everyone already knows them.' (Page 162)
1. Transcending Paradigms: realising that all paradigms are made up, that everything is made up. Enlightenment.
Important: the higher the leverage point, the strong the resistance from the entire system.
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