Name
Thinking in Systems
Author
Donella Meadows
Pages
242
Date Published
2008-12-03
Date Read
2023-12-03
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Science
Appendix

_____________

System Definitions: A Glossary

Archetypes: Common system structures that produce characteristic patterns of behavior.

Balancing feedback loop: A stabilizing, goal-seeking, regulating feedback loop, also know as a “negative feedback loop” because it opposes, or reverses, whatever direction of change is imposed on the system.

Bounded rationality: The logic that leads to decisions or actions that make sense within one part of a system but are not reasonable within a broader context or when seen as a part of the wider system.

Dynamic equilibrium: The condition in which the state of a stock (its level or its size) is steady and unchanging, despite inflows and outflows. This is possible only when all inflows equal all outflows.

Dynamics: The behavior over time of a system or any of its components.

Feedback loop: The mechanism (rule or information flow or signal) that allows a change in a stock to affect a flow into or out of that same stock. A closed chain of causal connections from a stock, through a set of decisions and actions dependent on the level of the stock, and back again through a flow to change the stock.

Flow: Material or information that enters or leaves a stock over a period of time.

Hierarchy: Systems organized in such a way as to create a larger system. Subsystems within systems.

Limiting factor: A necessary system input that is the one limiting the activity of the system at a particular moment.

Linear relationship: A relationship between two elements in a system that has constant proportion between cause and effect and so can be drawn with a straight line on a graph. The effect is additive.

Nonlinear relationship: A relationship between two elements in a system where the cause does not produce a proportional (straight-line) effect.

Reinforcing feedback loop: An amplifying or enhancing feedback loop, also known as a “positive feedback loop” because it reinforces the direction of change. These are vicious cycles and virtuous circles.

Resilience: The ability of a system to recover from perturbation; the ability to restore or repair or bounce back after a change due to an outside force.

Self-organization: The ability of a system to structure itself, to create new structure, to learn, or diversify.

Shifting dominance: The change over time of the relative strengths of competing feedback loops.

Stock: An accumulation of material or information that has built up in a system over time.

Suboptimization: The behavior resulting from a subsystem’s goals dominating at the expense of the total system’s goals.

System: A set of elements or parts that is coherently organized and interconnected in a pattern or structure that produces a characteristic set of behaviors, often classified as its “function” or “purpose.”

Summary of Systems Principles

Systems

• A system is more than the sum of its parts.
• Many of the interconnections in systems operate through the flow of information.
• The least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior.
• System structure is the source of system behavior. System behavior reveals itself as a series of events over time.

Stocks, Flows, and Dynamic Equilibrium

• A stock is the memory of the history of changing flows within the system.
• If the sum of inflows exceeds the sum of outflows, the stock level will rise.
• If the sum of outflows exceeds the sum of inflows, the stock level will fall.
• If the sum of outflows equals the sum of inflows, the stock level will not change — it will be held in dynamic equilibrium.
• A stock can be increased by decreasing its outflow rate as well as by increasing its inflow rate.
• Stocks act as delays or buffers or shock absorbers in systems.
• Stocks allow inflows and outflows to be de-coupled and independent.

Feedback Loops

• A feedback loop is a closed chain of causal connections from a stock, through a set of decisions or rules or physical laws or actions that are dependent on the level of the stock, and back again through a flow to change the stock.
• Balancing feedback loops are equilibrating or goal-seeking structures in systems and are both sources of stability and sources of resistance to change.
• Reinforcing feedback loops are self-enhancing, leading to exponential growth or to runaway collapses over time.
• The information delivered by a feedback loop—even nonphysical feedback—can affect only future behavior; it can’t deliver a signal fast enough to correct behavior that drove the current feedback.
• A stock-maintaining balancing feedback loop must have its goal set appropriately to compensate for draining or inflowing processes that affect that stock. Otherwise, the feedback process will fall short of or exceed the target for the stock.
• Systems with similar feedback structures produce similar dynamic behaviors.

Shifting Dominance, Delays, and Oscillations

• Complex behaviors of systems often arise as the relative strengths of feedback loops shift, causing first one loop and then another to dominate behavior.
• A delay in a balancing feedback loop makes a system likely to oscillate.
• Changing the length of a delay may make a large change in the behavior of a system.

Scenarios and Testing Models

• System dynamics models explore possible futures and ask “what if” questions.
• Model utility depends not on whether its driving scenarios are realistic (since no one can know that for sure), but on whether it responds with a realistic pattern of behavior.

Constraints on Systems

• In physical, exponentially growing systems, there must be at least one reinforcing loop driving the growth and at least one balancing loop constraining the growth, because no system can grow forever in a finite environment.
• Nonrenewable resources are stock-limited.
• Renewable resources are flow-limited.

Resilience, Self-Organization, and Hierarchy

• There are always limits to resilience.
• Systems need to be managed not only for productivity or stability, they also need to be managed for resilience.
• Systems often have the property of self-organization—the ability to structure themselves, to create new structure, to learn, diversify, and complexify.
• 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.

Source of System Surprises

• Many relationships in systems are nonlinear.
• There are no separate systems. The world is a continuum. Where to draw a boundary around a system depends on the purpose of the discussion.
• At any given time, the input that is most important to a system is the one that is most limiting.
• Any physical entity with multiple inputs and outputs is surrounded by layers of limits.
• There always will be limits to growth.
• A quantity growing exponentially toward a limit reaches that limit in a surprisingly short time.
• When there are long delays in feedback loops, some sort of foresight is essential.
• The bounded rationality of each actor in a system may not lead to decisions that further the welfare of the system as a whole.

Mindsets and Models

• Everything we think we know about the world is a model.
• Our models do have a strong congruence with the world.
• Our models fall far short of representing the real world fully.

Springing the System Traps

Policy Resistance

Trap: When various actors try to pull a system state toward various goals, the result can be policy resistance. Any new policy, especially if it’s effective, just pulls the system state farther from the goals of other actors and produces additional resistance, with a result that no one likes, but that everyone expends considerable effort in maintaining.

The Way Out: Let go. Bring in all the actors and use the energy formerly expended on resistance to seek out mutually satisfactory ways for all goals to be realized—or redefinitions of larger and more important goals that everyone can pull toward together.

The Tragedy of the Commons

Trap: When there is a commonly shared resource, every user benefits directly from its use, but shares the costs of its abuse with everyone else. Therefore, there is very weak feedback from the condition of the resource to the decisions of the resource users. The consequence is overuse of the resource, eroding it until it becomes unavailable to anyone.

The Way Out: Educate and exhort the users, so they understand the consequences of abusing the resource. And also restore or strengthen the missing feedback link, either by privatizing the resource so each user feels the direct consequences of its abuse or (since many resources cannot be privatized) by regulating the access of all users to the resource.

Drift to Low Performance

Trap: Allowing performance standards to be influenced by past performance, especially if there is a negative bias in perceiving past performance, sets up a reinforcing feedback loop of eroding goals that sets a system drifting toward low performance.

The Way Out: Keep performance standards absolute. Even better, let standards be enhanced by the best actual performances instead of being discouraged by the worst. Set up a drift toward high performance!

Escalation

Trap: When the state of one stock is determined by trying to surpass the state of another stock—and vice versa—then there is a reinforcing feedback loop carrying the system into an arms race, a wealth race, a smear campaign, escalating loudness, escalating violence. The escalation is exponential and can lead to extremes surprisingly quickly. If nothing is done, the spiral will be stopped by someone’s collapse—because exponential growth cannot go on forever.

The Way Out: The best way out of this trap is to avoid getting in it. If caught in an escalating system, one can refuse to compete (unilaterally disarm), thereby interrupting the reinforcing loop. Or one can negotiate a new system with balancing loops to control the escalation.

Success to the Successful

Trap: If the winners of a competition are systematically rewarded with the means to win again, a reinforcing feedback loop is created by which, if it is allowed to proceed uninhibited, the winners eventually take all, while the losers are eliminated.

The Way Out: Diversification, which allows those who are losing the competition to get out of that game and start another one; strict limitation on the fraction of the pie any one winner may win (antitrust laws); policies that level the playing field, removing some of the advantage of the strongest players or increasing the advantage of the weakest; policies that devise rewards for success that do not bias the next round of competition.

Shifting the Burden to the Intervenor

Trap: Shifting the burden, dependence, and addiction arise when a solution to a systemic problem reduces (or disguises) the symptoms, but does nothing to solve the underlying problem. Whether it is a substance that dulls one’s perception or a policy that hides the underlying trouble, the drug of choice interferes with the actions that could solve the real problem.

If the intervention designed to correct the problem causes the self-maintaining capacity of the original system to atrophy or erode, then a destructive reinforcing feedback loop is set in motion. The system deteriorates; more and more of the solution is then required. The system will become more and more dependent on the intervention and less and less able to maintain its own desired state.

The Way Out: Again, the best way out of this trap is to avoid getting in. Beware of symptom-relieving or signal-denying policies or practices that don’t really address the problem. Take the focus off short-term relief and put it on long term restructuring.

If you are the intervenor, work in such a way as to restore or enhance the system’s own ability to solve its problems, then remove yourself.

If you are the one with an unsupportable dependency, build your system’s own capabilities back up before removing the intervention. Do it right away. The longer you wait, the harder the withdrawal process will be.

Rule Beating

Trap: Rules to govern a system can lead to rule-beating—perverse behavior that gives the appearance of obeying the rules or achieving the goals, but that actually distorts the system.

The Way Out: Design, or redesign, rules to release creativity not in the direction of beating the rules, but in the direction of achieving the purpose of the rules.

Seeking the Wrong Goal

Trap: System behavior is particularly sensitive to the goals of feedback loops. If the goals—the indicators of satisfaction of the rules—are defined inaccurately or incompletely, the system may obediently work to produce a result that is not really intended or wanted.

The Way Out: Specify indicators and goals that reflect the real welfare of the system. Be especially careful not to confuse effort with result or you will end up with a system that is producing effort, not result.

Places to Intervene in a System (in increasing order of effectiveness)

12. Numbers: Constants and parameters such as subsidies, taxes, and standards

11. Buffers: The sizes of stabilizing stocks relative to their flows

10. Stock-and-Flow Structures: Physical systems and their nodes of intersection

9. Delays: The lengths of time relative to the rates of system changes

8. Balancing loops: 8. Balancing Feedback Loops: The strength of the feedbacks relative to the impacts they are trying to correct

7. Reinforcing Feedback Loops: The strength of the gain of driving loops

6. Information Flows: The structure of who does and does not have access to information

5. Rules: Incentives, punishments, constraints 4. Self-Organization: The power to add, change, or evolve system structure

3. Goals: The purpose of the system

2. Paradigms: The mind-set out of which the system—its goals, structure, rules, delays, parameters—arises

1. Transcending Paradigms:

Guidelines for Living in a World of Systems

1. Get the beat of the system.

2. Expose your mental models to the light of day.

3. Honor, respect, and distribute information.

4. Use language with care and enrich it with systems concepts.

5. Pay attention to what is important, not just what is quantifiable.

6. Make feedback policies for feedback systems.

7. Go for the good of the whole.

8. Listen to the wisdom of the system.

9. Locate responsibility within the system.

10. Stay humble—stay a learner.

11. Celebrate complexity.

12. Expand time horizons.

13. Defy the disciplines.

14. Expand the boundary of caring.

15. Don’t erode the goal of goodness.

Model Equations

There is much to be learned about systems without using a computer. However, once you have started to explore the behavior of even very simple systems, you may well find that you wish to learn more about building your own formal mathematical models of systems. The models in this book were originally developed using STELLA modeling software, by isee systems Inc. (formerly High Performance Systems). The equations in this section are written to be easily translated into various modeling software, such as Vensim by Ventana Systems Inc. as well as STELLA and iThink by isee systems Inc.

The following model equations are those used for the nine dynamic models discussed in chapters 1 and 2. “Converters” can be constants or calculations based on other elements of the system model. Time is abbreviated (t) and the change in time from one calculation to the next, the time interval, is noted as (dt).

Chapter One

Bathtub—for Figures 5, 6 and 7

Stock: water in tub(t) = water in tub(t – dt) + (inflow – outflow) × dt Initial stock value: water in tub = 50 gal

t = minutes

dt = 1 minute

Run time = 10 minutes

Inflow: inflow = 0 gal/min . . . for time 0 to 5; 5 gal/min . . . for time 6 to 10

Outflow: outflow = 5 gal/min

Coffee Cup Cooling or Warming—for Figures 10 and 11

Cooling

Stock: coffee temperature(t) = coffee temperature (t – dt) – (cooling × dt) Initial stock value: coffee temperature = 100°C, 80°C, and 60°C . . . for three comparative model runs.

t = minutes

dt = 1 minute

Run time = 8 minutes

Outflow: cooling = discrepancy × 10%

Converters: discrepancy = coffee temperature – room temperature room temperature = 18°C

Warming

Stock: coffee temperature(t) = coffee temperature(t – dt) + (heating × dt)

Initial stock value: coffee temperature = 0°C, 5°C, and 10°C . . . for three comparative model runs.

t = minutes

dt = 1 minute

Run time = 8 minutes

Inflow: heating = discrepancy × 10%

Converters: discrepancy = room temperature – coffee temperature room temperature = 18°C

Bank Account—for Figures 12 and 13

Stock: money in bank account(t) = money in bank account(t – dt) + (interest added × dt)

Initial stock value: money in bank account = $100

t = years dt = 1 year

Run time = 12 years

Inflow: interest added ($/year) = money in bank account × interest rate

Converter: interest rate = 2%, 4%, 6%, 8%, & 10% annual interest . . . for five comparative model runs.

Chapter Two

Thermostat—For Figures 14–20

Stock: room temperature(t) = room temperature(t – dt) + (heat from furnace – heat to outside) × dt

Initial stock value: room temperature = 10°C for cold-room warming;

18°C for warm-room cooling

t = hours

dt = 1 hour

Run time = 8 hours and 24 hours

Inflow: heat from furnace = minimum of discrepancy between desired and actual room temperature or 5

Outflow: heat to outside = discrepancy between inside and outside temperature × 10% . . . for “normal” house; discrepancy between inside and outside temperature × 30% . . . for very leaky house

Converters: thermostat setting = 18°C discrepancy between desired and actual room temperature = maximum of (thermostat setting – room temperature) or 0 discrepancy between inside and outside temperature = room temperature – 10°C . . . for constant outside temperature (Figures 16–18); room temperature – 24-hour outside temp . . . for full day-and night cycle (Figures 19 and 20) 24-hour outside temp ranges from 10°C (50°F) during the day to – 5°C (23°F) at night, as shown in graph

Population—for Figures 21–26

Stock: population(t) = population(t – dt) + (births – deaths) × dt Initial stock value: population = 6.6 billion people

t = years

dt = 1 year

Run time = 100 years

Inflow: births = population × fertility

Outflow: deaths = population × mortality
Converters:

Figure 22:

mortality = .009 . . . or 9 deaths per 1000 population

fertility = .021 . . . or 21 births per 1000 population

Figure 23:

mortality = .030

fertility = .021

Figure 24:

mortality = .009

fertility starts at .021 and falls over time to .009 as shown in graph

Figure 26:

mortality = .009

fertility starts at .021, drops to .009, but then rises .030 as shown in graph

Capital—for Figures 27 and 28

Stock: capital stock(t) = capital stock(t – dt) + (investment – depreciation) × dt Initial stock value: capital stock = 100

t = years

dt = 1 year

Run time = 50 years

Inflow: investment = annual output × investment fraction

Outflow: depreciation = capital stock / capital lifetime

Converters: annual output = capital stock × output per unit capital capital lifetime = 10 years, 15 years, and 20 years . . . for three comparative model runs.

investment fraction = 20%

output per unit capital = 1/3

Business Inventory—for Figures 29–36

Stock: inventory of cars on the lot(t) = inventory of cars on the lot(t – dt) + (deliveries – sales) × dt Initial stock values: inventory of cars on the lot = 200 cars

t = days

dt = 1 day

Run time = 100 days

Inflows: deliveries = 20 . . . for time 0 to 5; orders to factory (t – delivery delay) . . . for time 6 to 100

Outflows: sales = minimum of inventory of cars on the lot or customer demand

Converters: customer demand = 20 cars per day . . . for time 0 to 25; 22 cars per day . . . for time 26 to 100

perceived sales = sales averaged over perception delay (i.e., sales smoothed over perception delay)

desired inventory = perceived sales × 10

discrepancy = desired inventory – inventory of cars on the lot

orders to factory = maximum of (perceived sales + discrepancy) or 0 . . . for

Figure 32; maximum of (perceived sales + discrepancy/response delay) or 0 . . . for Figures 34–36

Delays, Figure 30:

perception delay = 0

response delay = 0

delivery delay = 0

Delays, Figure 32:

perception delay = 5 days

response delay = 3 days

delivery delay = 5 days

Delays, Figure 34:

perception delay = 2 days

response delay = 3 days

delivery delay = 5 days

Delays, Figure 35:

perception delay = 5 days

response delay = 2 days

delivery delay = 5 days

Delays, Figure 36:

perception delay = 5 days

response delay = 6 days

delivery delay = 5 days

A Renewable Stock Constrained by a Nonrenewable Resource—for Figures 37–41 Stock: resource(t) = resource(t – dt) – (extraction × dt)

Initial stock values: resource = 1000 . . . for Figures 38, 40, and 41; 1000, 2000, and 4000 . . . for three comparative model runs in Figure 39 Outflow: extraction = capital × yield per unit capital

t = years

dt = 1 year

Run time = 100 years

Stock: capital(t) = capital(t – dt) + (investment – depreciation) × dt

Initial stock values: capital = 5 Inflow: investment = minimum of profit or growth goal

Outflow: depreciation = capital / capital lifetime

Converters: capital lifetime = 20 years

profit = (price × extraction) – (capital × 10%) growth goal = capital × 10% . . .

for Figures 30–40; capital × 6%, 8%, 10%, and 12% . . . for four comparative model runs in Figure 40

price = 3 . . . for Figures 38, 39, and 40; for Figure 41, price starts at 1.2

when yield per unit capital is high and rises to 10 as yield per unit capital falls, as shown in graph

yield per unit capital starts at 1 when resource stock is high and falls to 0 as the resource stock declines, as shown in graph

A Renewable Stock Constrained by a Renewable Resource—for Figures 42–45

Stock: resource(t) = resource(t – dt) + (regeneration – harvest) × dt

Initial stock value: resource = 1000

Inflow: regeneration = resource × regeneration rate

Outflow: harvest = capital × yield per unit capital

t = years

dt = 1 year

Run time = 100 years

Stock: capital(t) = capital(t – dt) + (investment – depreciation) × dt

Initial stock value: capital = 5

Inflow: investment = minimum of profit or growth goal

Outflow: depreciation = capital / capital lifetime

Converters: capital lifetime = 20

growth goal = capital × 10%

profit = (price × harvest) – capital

price starts at 1.2 when yield per unit capital is high and rises to 10 as

yield per unit capital falls. This is the same nonlinear relationship for

price and yield as in the previous model.

regeneration rate is 0 when the resource is either fully stocked or
completely depleted. In the middle of the resource range, regeneration rate peaks near 0.5.

yield per unit capital starts at 1 when the resource is fully stocked, but falls (non-linearly) as the resource stock declines. Yield per unit capital increases overall from least efficient in Figure 43, to slightly more efficient in Figure 44, to most efficient in Figure 45.

Bibliography of Systems Resources

_____________

In addition to the works cited in the Notes, the items listed here are jumping off points—places to start your search for more ways to see and learn about systems. The fields of systems thinking and system dynamics are now extensive, reaching into many disciplines. For more resources, see also www.ThinkingInSystems.org

Systems Thinking and Modeling

Books

Bossel, Hartmut. Systems and Models: Complexity, Dynamics, Evolution, Sustainability. (Norderstedt, Germany: Books on Demand, 2007). A comprehensive textbook presenting the fundamental concepts and approaches for understanding and modeling the complex systems shaping the dynamics of our world, with a large bibliography on systems.

Bossel, Hartmut. System Zoo Simulation Models. Vol. 1: Elementary Systems, Physics, Engineering; Vol. 2: Climate, Ecosystems, Resources; Vol. 3: Economy, Society, Development. (Norderstedt, Germany: Books on Demand, 2007). A collection of more than 100 simulation models of dynamic systems from all fields of science, with full documentation of models, results, exercises, and free simulation model download.

Forrester, Jay. Principles of Systems. (Cambridge, MA: Pegasus Communications, 1990). First published in 1968, this is the original introductory text on system dynamics.

Laszlo, Ervin. A Systems View of the World. (Cresskill, NJ: Hampton Press, 1996).

Richardson, George P. Feedback Thought in Social Science and Systems Theory. (Philadelphia: University of Pennsylvania Press, 1991). The long, varied, and fascinating history of feedback concepts in social theory.

Sweeney, Linda B. and Dennis Meadows. The Systems Thinking Playbook. (2001). A collection of 30 short gaming exercises that illustrate lessons about systems thinking and mental models.

Organizations, Websites, Periodicals, and Software

Creative Learning Exchange—an organization devoted to developing “systems citizens” in K–12 education. Publisher of The CLE Newsletter and books for teachers and students. www.clexchange.org isee systems, inc.—Developer of STELLA and iThink software for modeling dynamic systems. www.iseesystems.com

Pegasus Communications—Publisher of two newsletters, The Systems Thinker and Leverage Points, as well as many books and other resources on systems thinking. www.pegasuscom.com

System Dynamics Society—an international forum for researchers, educators, consultants, and practitioners dedicated to the development and use of systems thinking and system dynamics around the world. The Systems Dynamics Review is the official journal of the System Dynamics Society. www.systemdynamics.org

Ventana Systems, Inc.—Developer of Vensim software for modeling dynamic systems. vensim.com

Systems Thinking and Business

Senge, Peter. The Fifth Discipline: The Art and Practice of the Learning Organization. (New York: Doubleday, 1990). Systems thinking in a business environment, and also the broader philosophical tools that arise from and complement systems thinking, such as mental-model flexibility and visioning.

Sherwood, Dennis. Seeing the Forest for the Trees: A Manager’s Guide to Applying Systems Thinking. (London: Nicholas Brealey Publishing, 2002).

Sterman, John D. Business Dynamics: Systems Thinking and Modeling for a Complex World. (Boston: Irwin McGraw Hill, 2000).

Systems Thinking and Environment

Ford, Andrew. Modeling the Environment. (Washington, DC: Island Press, 1999.)

Systems Thinking, Society, and Social Change

Macy, Joanna. Mutual Causality in Buddhism and General Systems Theory. (Albany, NY: Stat University of New York Press, 1991).

Meadows, Donella H. The Global Citizen. (Washington, DC: Island Press, 1991).

Book notes

You people are different. You ask different kinds of questions. You see things I don’t see. Somehow you come at the world in a different way. How? Why?

The blind men and the matter of the elephant. The behavior of a system cannot be known just by knowing the elements of which the system is made.

System definitions: a glossary

System: a set of elements or parts coherently organized and interconnected in a pattern or structure that produces a characteristic set of behaviors, often classified as it’s “function” or “purpose”.

Purposes or deduced from behavior, not from rhetoric or stated goals.

An important function of almost every system is to ensure its own perpetuation.

A stock can be increased by decreasing its outflow rate as well as by increasing its inflow rate. These two strategies may have very different costs.

A stock takes time to change, because flows take time to flow. Stocks generally change slowly, even when the flows into or out of them change suddenly. Therefore, stocks act as delays or buffers or shock absorbers in systems.

Systems thinkers see the world as a collection of stocks along with the mechanisms for regulating the levels in the stocks by manipulating flows. Systems thinkers see the world as a collection of “feedback processes”.

If A causes B, is it possible that B also causes A?

Balancing feedback loops 

Reinforcing feedback loops

Because we bump into reinforcing loops so often, it is handy to know this shortcut: the time it takes for an exponentially growing stock to double in size, the “doubling time” equals approximately 70 divided by the growth rate (expressed as a percentage). 

Example: if you put $100 in the bank at 7% interest per year, you will double your money in 10 years (70/7=10). If you get only 5% interest, your money will take 14 years to double.

The information delivered by a feedback loop can only affect future behavior; it can’t deliver the information, and so it can’t have an impact fast enough to correct behavior that drove the current feedback. A person in the system who makes a decision based on feedback can’t change the behavior of the system that drove the current feedback; the decisions they make will affect only future behavior.

A flow can’t react instantly to a flow. It can react only to a change in a stock, and only after a slight delay to register the incoming information.

A stock-maintaining balancing feedback loop must have it’s goal set appropriately to compensate for draining or inflowing processes that affect the stock. Otherwise, the feedback processes will fall short of or exceed the target for the stock.

Questions for 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?

Self-organization produces heterogeneity and unpredictability. It is likely to come up with whole new structures, whole new ways of doing things. It requires freedom and experimentation, and a certain amount of disorder.

These conditions that encourage self-organization often can be scary for individuals and threatening to power structures. As a consequence, education systems may restrict the creative powers of children instead of stimulating those powers.

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.

Everything we think we know about the world is a model. Our models do have a strong congruence with the world. Our models fall far short of representing the real world fully.

The behavior of a system is its performance over time —its growth, stagnation, decline, oscillation, randomness, or evolution.

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.

The lesson of boundaries is hard even for systems thinkers to get. There is no single, legitimate boundary to draw around a system. We have to invent boundaries for clarity and sanity; and boundaries can produce problems when we forget that we’ve artificially created them.

At any given time, the input that is most important to a system is the one that is most limiting.

When there are long delays in feedback loops, some sort of foresight is essential. To act only when a problem becomes obvious is to miss an important opportunity to solve the problem.

What makes a difference is redesigning the system to improve the information, incentives, disincentives, goals, stresses, and constraints that have an effect on specific actors.

Problematic system archetypes

The most effective way of dealing with policy resistance is to find a way of aligning the various goals of the subsystems, usually by providing an overarching goal that allows all actors to break out of their bounded rationality.

There are three ways to avoid the tragedy of the commons.

There are two antidotes to eroding goals. One is to keep standards absolute, regardless of performance. Another is to make goals sensitive to the best performances of the past, instead of the worst. Set up a drift toward high performance.

The best way of out escalation is to avoid getting in it. Refuse to compete. Or negotiate a new system with balancing loops to control the escalation.

The success-to-success feedback loop can be kept under control by putting into place feedback loops that keep any competitor from taking over entirely. “Leveling the playing field” get out of the game and start a new one.

Shifting the burden to the intervenor, the best way out is to avoid getting in this trap. Beware of symptom-relieving or signal-denying policies or practices that don’t really address the problem. Take the focus off short-term relief and put it on long-term restructuring. Begin with a series of questions.

The way out of rule beating: Understand rule bearing as useful feedback, and revise, improve, rescind, or better explain the rules.

The way out to avoid seeking the wrong goal: specify indicators and goals that reflect the real welfare of the system. Be careful not to confuse effort with result or you will end up with a system that is producing effort, not result.

Chapter six: Leverage points: places to intervene in a system

It’s one thing to understand how to fix a system and quite another to wade in and fix it.

The idea of making a complex system do just what you want it to do can be achieve only temporarily, at best.

Systems can’t be controlled, but they can be designed and redesigned. We can’t surge forward with certainty into a world full of no surprises, but we can expect surprises and learn from them and even profit from them. We can’t impose our will on a system. We can listen to what the system tell us, and discover how it’s properties and our values can work together to bring forth something much better than could ever be produced by our will alone.

We can’t control systems or figure them out, but we can dance with them.

Learning to dance with systems.