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DownloadHow do you avoid wasted time, money, and resources from short-sighted decisions? When you think in systems, you can learn to recognize the relationship between structure and behavior to create better business decisions. This approach can help you understand any system to adjust and improve it.
In Thinking in Systems: A Primer, author Donella H. Meadows introduces simple explanations of what makes a system alongside the elements that drive its behavior. In addition to basic and complex system fundamentals, Meadows shares insights into a number of common traps to avoid when thinking in systems and how to escape them.
A system is defined as a set of independent things that are interconnected in a way that causes them to produce their own patterns over time. Nearly everything is a system, from our bodies to the universe and the computer you use to read this.
Systems are influenced by outside factors, but any system's patterns are largely internal. When a Slinky is extended, it bounces not because of the hand that holds it, but because of its system of coils.
A system consists of elements, interconnections, and functions. In the case of human-built systems, function could also be a purpose.
Stocks are the "foundation" of a system and are the element that you can see, feel, count, or measure. A feedback loop is formed when changes in stock affect the flows into or out of that same stock. A prime example of this concept is interest as it relates to the amount of money in a bank account. Likewise, if you see less money in your account, you might react and take more work and thus the cycle continues.
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Reinforcing feedback loops are found whenever a stock has the capacity to reproduce itself or grow as a constant fraction of itself. The more customers leave positive feedback about your company, the more people will try it and leave more feedback. Over time, your stock – in this case, customer satisfaction – will reproduce on its own.
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Negative reinforcing feedback loops are better known as "vicious cycles." If you're stressed, you might eat a tub of ice cream, which makes you feel guilty, which stresses you out, so you reach for more food.
Systems thinking would have you reflect on this cause and effect. If A causes B, is it possible that B also causes A?
A systems analyst can test several scenarios and observe what happens when the driving factors do different things. These dynamic systems studies are not typically designed to predict the future, however. Rather, they are designed to explore what would happen if a number of driving factors unfold in a range of different ways.
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When you test the value of a model, ask yourself:
Model utility depends not on whether the model's driving scenarios are realistic but on whether it responds with a realistic pattern of behavior.
One stock systems
A one-stock system is what it sounds like – a system with one stock that is constantly influenced by goal-seeking feedback loops. For the sake of simplicity, let's look at a room's thermostat and assume that power is unlimited.
In this case, our stock is the room's temperature, regulated by feedback loops – a furnace and an air conditioner. Other loops can be leaks to the outside through drafty windows or poor insulation. The temperature outside is another loop that influences our stock. If all loops operate at the same time (AC and heating included), the temperature will not be balanced.
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People have learned to accommodate their thermostat usage for feedback loops such as heat leakage through windows and doors, a small furnace, or a super-powerful AC unit that cools quickly.
Two stock systems
A two-stock system will have a renewable stock constrained by a nonrenewable stock, such as any industry that works with the environment – forestry, energy, cattle, etc. Any physical system of this type is bound to naturally occurring rules. Specifically, they must have at least one reinforcing loop that drives growth and a balancing loop that constrains it. No physical system can grow forever and will eventually run into constraints, temporary or permanent.
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A quantity that grows exponentially toward a constraint/limit reaches that limit in a surprisingly short amount of time. If you are an oil company that has identified a new drilling site, and the resource turns out to be much larger than geologists anticipated, you have a few options. You can increase extraction and see profits quickly but exhaust the resource faster. Alternatively, you can make less money but keep a steadier extraction for a longer period of time. With variables such as fuel demand and oil prices in constant flux, either choice is a gamble.
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Fisheries run into a similar problem. Overcrowding lowers reproduction rates, and rare fish that fetch a higher price reproduce less often. The balancing feedback of smaller harvests that reduce profits brings down the investment rate quickly enough to prevent the fleet of ships from growing so large that overfishing occurs.
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If a resource is depleted within a renewable resource system, three things can happen:
The constraints imposed on a renewable vs. non-renewable system differ based on stocks and flows. For example, non-renewable resources are stock-limited whereas renewable resources are flow-limited. If you extract a resource faster than it can regenerate, it will essentially create a non-renewable system. Whaling was one of the most prominent businesses in America before scientists understood the animals' long reproductive cycles. At the time, whales appeared to be an infinite resource but proved to be quite the opposite.
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The input that is most important to a system is the one that is most limited, such as oil or fish in the previous examples. These limits can easily be misidentified ("We'll harvest more each year if we double our fleet of ships"). Any physical entity with multiple inputs and outputs will be surrounded by layers of limits. These limits can be self-imposed such as a pace of harvest. If they aren't, they will be system-imposed, such as a finite resource that runs out completely.
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Resilience arises from the dynamic structure of several feedback loops that have the ability to work in different ways to restore a system, even after a large setback. The key to this ability is redundancy – multiple feedback loops that operate through different mechanisms and time scales to accomplish the same goal. Make sure that no one feedback loop goes unsupported.
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Any system will have its own traps to avoid. Here are some common examples, as well as how to avoid them ̶ or if you find yourself trapped, how to escape.
Trap: policy resistance
"Too many cooks in the kitchen"
Any new effective policy pulls the stock further from the goals of other actors. When various actors try to pull a system stock toward various goals, the result can be policy resistance.
Escape:
The best way to combat policy resistance is to establish a sense of unity. Bring in all actors and seek out mutually satisfactory ways for all goals to be realized or shift everyone's focus toward larger and more important goals that everyone can get behind.
Trap: tragedy of the commons
"A failed honor system"
The phrase "tragedy of the commons" is credited to ecologist Garret Hardin, who in a 1968 paper described how shared resources ("commons") are inevitably destroyed. This trap occurs when all users benefit from commonly shared resources, but also suffer from the abuses of anyone else. This leads to overuse of the resource and erosion until it is unusable. If you have ever tried to leave Halloween candy on the porch with a sign that encourages a one piece limit, you are familiar with how other children miss out because one was greedy.
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Escape:
Educate and exhort the users so they understand the consequences of abuse. Restore or strengthen the missing feedback link through the privatization of the resource so accountability is felt by individuals or regulate the access of problem users.
Trap: escalation
"I know you are, but what am I?"
Since exponential growth cannot go on forever, a reinforcing feedback loop will eventually collapse. Like two children that try to one-up a punch from the other, both will end up in tears.
Escape:
The best defense for escalation is to prevent yourself from getting trapped in the first place. If caught in an escalating system, refuse to compete or negotiate a new system with balancing loops to control the escalation.
Trap: success to the successful
"The rich keep getting richer"
Another reinforcing feedback loop occurs when winners are systematically awarded with the means to win again. If allowed to continue, winners take all and losers are eliminated.
Escape:
Combat this loop through diversification (i.e. antitrust laws) or devise rewards for success that do not bias the next round of competition in favor of previous winners.
Trap: shift the burden to the intervenor
"Putting a Band-Aid on a bullet wound"
When a solution to a systematic problem merely disguises or reduces symptoms but does nothing to solve the underlying problem, the capacity of the original system to self-maintain begins to atrophy or erode, and a destructive feedback loop is set in motion. The system becomes more dependent on the intervention and less able to maintain its own desired state.
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Escape:
Intervene in a way as to strengthen the ability of the system to shoulder its own burdens, then remove yourself. Ask:
Take the focus off short-term relief and put it on a long-term restructure.
Trap: beat the system
"Rules are made to be broken"
If an attitude to "beat-the-system" is pervasive with users throughout your system, it's time to rethink your approach. From exploits in video games to government agencies that spend useless dollars to prevent a lower budget next year, "rule beating" is a common problem among various types of systems.
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Escape:
Treat these rule exploits as helpful feedback. Design or redesign rules to encourage creativity in how the purpose of the rules is achieved. Focus on the "spirit of the law" rather than the "letter of the law." Ask yourself if there is a better way to achieve your goal.
Trap: seek the wrong goal
"There is no A for effort"
If the goals are defined inaccurately or incompletely, the system may obediently work to produce a result contrary to what its operators actually intended in the first place.
Escape:
Specify indicators and goals that reflect the real welfare of the system. Do not confuse effort with result. Otherwise, you will be left with a system that produces effort, not outcomes.
Trap: drift to low performance
"If you're not growing, you're shrinking"
If you allow performance standards to be influenced by past performance, it sets up a reinforcing feedback loop that erodes goals and sends your system towards low performance.
Escape:
Set standards according to the best actual performances instead of being discouraged by the worst. This pattern will reverse the flow of your feedback loop toward growth.
"If a revolution destroys a government, but the systematic patterns of thought that produced that government are left intact, then those patterns will repeat themselves… There's so much talk about the system. And so little understanding." - Robert Pirsig, Zen and the Art of Motorcycle Maintenance
Those who are deeply involved in a system often intuitively know where to find leverage points, but frequently push change in the wrong direction. MIT's Jay Forester published a study of urban dynamics in 1969 that identified low-income housing as a leverage point in an economy.
What he found was that the less low-income housing there was in a city, the better off it was. The idea is counter-intuitive, and Forester was derided for his findings during a time when national policy dictated a slew of such projects across the country. Since then, many such projects have been torn down.
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As systems become more complex, their behavior can become surprising.
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