sustainable

LONDON • STERLING, VA

Thinking in Systems —— A Primer ——

Donella H. Meadows

Edited by Diana Wright, Sustainability Institute

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First published by Earthscan in the UK in 2009

Copyright © 2008 by Sustainability Institute.

All rights reserved

ISBN: 978-1-84407-726-7 (pb) ISBN: 978-1-84407-725-0 (hb)

Typeset by Peter Holm, Sterling Hill Productions Cover design by Dan Bramall

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Earthscan publishes in association with the International Institute for Environment and Development

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At Earthscan we strive to minimize our environmental impacts and carbon footprint through reducing waste, recycling and offsetting our CO

2 emissions, including those created through

publication of this book. For more details of our environmental policy, see www.earthscan.co.uk.

This book was printed in the UK by TJ International Ltd, an ISO 14001 accredited company. The paper used is FSC certifi ed.

Part of this work has been adapted from an article originally published under the title “Whole Earth Models and Systems” in Coevolution Quarterly (Summer 1982). An early version of Chapter 6 appeared as “Places to Intervene in a System” in Whole Earth Review (Winter 1997) and later as an expanded paper published by the Sustainability Institute. Chapter 7, “Living in a World of Systems,” was originally published as “Dancing with Systems” in Whole Earth Review (Winter 2001).

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PART ONE System Structure and Behavior

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— ONE —

The Basics I have yet to see any problem, however complicated, which, when looked at in the right way, did not become still more complicated.

—Poul Anderson1

More Than the Sum of Its Parts

A system isn’t just any old collection of things. A system* is an intercon- nected set of elements that is coherently organized in a way that achieves something. If you look at that defi nition closely for a minute, you can see that a system must consist of three kinds of things: elements, interconnec- tions, and a function or purpose.

For example, the elements of your digestive system include teeth, enzymes, stomach, and intestines. They are interrelated through the physi- cal fl ow of food, and through an elegant set of regulating chemical signals. The function of this system is to break down food into its basic nutrients and to transfer those nutrients into the bloodstream (another system), while discarding unusable wastes.

A football team is a system with elements such as players, coach, fi eld, and ball. Its interconnections are the rules of the game, the coach’s strat- egy, the players’ communications, and the laws of physics that govern the motions of ball and players. The purpose of the team is to win games, or have fun, or get exercise, or make millions of dollars, or all of the above.

A school is a system. So is a city, and a factory, and a corporation, and a national economy. An animal is a system. A tree is a system, and a forest is a larger system that encompasses subsystems of trees and animals. The earth

* Defi nitions of words in bold face can be found in the Glossary.

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12 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

is a system. So is the solar system; so is a galaxy. Systems can be embedded in systems, which are embedded in yet other systems.

Is there anything that is not a system? Yes—a conglomeration without any particular interconnections or function. Sand scattered on a road by happenstance is not, itself, a system. You can add sand or take away sand and you still have just sand on the road. Arbitrarily add or take away foot- ball players, or pieces of your digestive system, and you quickly no longer have the same system.

When a living creature dies, it loses its “system-ness.” The multiple interrelations that held it together no longer function, and it dissipates,

although its material remains part of a larger food-web system. Some people say that an old city neighborhood where people know each other and communicate regularly is a social system, and that a new apartment block full of strangers is not—not until new relationships arise and a system forms.

You can see from these examples that there is an integrity or wholeness about a system and an

active set of mechanisms to maintain that integrity. Systems can change, adapt, respond to events, seek goals, mend injuries, and attend to their own survival in lifelike ways, although they may contain or consist of nonliving things. Systems can be self-organizing, and often are self-repairing over at least some range of disruptions. They are resilient, and many of them are evolutionary. Out of one system other completely new, never-before- imagined systems can arise.

Look Beyond the Players to the Rules of the Game

You think that because you understand “one” that you must there- fore understand “two” because one and one make two. But you forget that you must also understand “and.”

—Sufi teaching story

The elements of a system are often the easiest parts to notice, because many of them are visible, tangible things. The elements that make up a tree are roots, trunk, branches, and leaves. If you look more closely, you

A system is more than the sum of its parts. It may exhibit adaptive, dynamic, goal-seeking, self-preserv- ing, and sometimes evolu- tionary behavior.

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CHAPTER ONE: THE BASICS 13

see specialized cells: vessels carrying fl uids up and down, chloroplasts, and so on. The system called a university is made up of buildings, students, professors, administrators, libraries, books, computers—and I could go on and say what all those things are made up of. Elements do not have to be physical things. Intangibles are also elements of a system. In a university, school pride and academic prowess are two intangibles that can be very important elements of the system. Once you start listing the elements of a system, there is almost no end to the process. You can divide elements into sub-elements and then sub-sub-elements. Pretty soon you lose sight of the system. As the saying goes, you can’t see the forest for the trees.

Before going too far in that direction, it’s a good idea to stop dissecting out elements and to start looking for the interconnections, the relationships that hold the elements together.

The interconnections in the tree system are the physical fl ows and chemical reactions that govern the tree’s metabolic processes—the signals that allow one part to respond to what is happening in another part. For example, as the leaves lose water on a sunny day, a drop in pressure in the water-carrying vessels allows the roots to take in more water. Conversely, if the roots experience dry soil, the loss of water pressure signals the leaves to close their pores, so as not to lose even more precious water.

As the days get shorter in the temperate zones, a deciduous tree puts forth chemical messages that cause nutrients to migrate out of the leaves into the trunk and roots and that weaken the stems, allowing the leaves to

THINK ABOUT THIS How to know whether you are looking at a system or just a bunch of stu! : A) Can you identify parts? . . . and B) Do the parts a! ect each other? . . . and C) Do the parts together produce an e! ect that is di! er-

ent from the e! ect of each part on its own? . . . and perhaps

D) Does the e! ect, the behavior over time, persist in a variety of circumstances?

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14 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

fall. There even seem to be messages that cause some trees to make repel- lent chemicals or harder cell walls if just one part of the plant is attacked by insects. No one understands all the relationships that allow a tree to do what it does. That lack of knowledge is not surprising. It’s easier to learn about a system’s elements than about its interconnections.

In the university system, interconnections include the standards for admission, the requirements for degrees, the examinations and grades, the budgets and money fl ows, the gossip, and most important, the communi- cation of knowledge that is, presumably, the purpose of the whole system.

Some interconnections in systems are actual physi- cal fl ows, such as the water in the tree’s trunk or the students progressing through a university. Many inter- connections are fl ows of information—signals that go to decision points or action points within a system. These kinds of interconnections are often harder to see, but the system reveals them to those who look. Students may use informal information about the probability of getting a good grade to decide what

courses to take. A consumer decides what to buy using information about his or her income, savings, credit rating, stock of goods at home, prices, and avail- ability of goods for purchase. Governments need information about kinds and quantities of water pollution before they can create sensible regulations to reduce that pollution. (Note that information about the existence of a prob- lem may be necessary but not suffi cient to trigger action—information about resources, incentives, and consequences is necessary too.)

If information-based relationships are hard to see, functions or purposes are even harder. A system’s function or purpose is not necessarily spoken, written, or expressed explicitly, except through the operation of the system. The best way to deduce the system’s purpose is to watch for a while to see how the system behaves.

If a frog turns right and catches a fl y, and then turns left and catches a fl y, and then turns around backward and catches a fl y, the purpose of the frog has to do not with turning left or right or backward but with catching fl ies. If a government proclaims its interest in protecting the environment but allocates little money or effort toward that goal, environmental protec- tion is not, in fact, the government’s purpose. Purposes are deduced from behavior, not from rhetoric or stated goals.

Many of the interconnec- tions in systems operate through the fl ow of infor- mation. Information holds systems together and plays a great role in determining how they operate.

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CHAPTER ONE: THE BASICS 15

The function of a thermostat-furnace system is to keep a building at a given temperature. One function of a plant is to bear seeds and create more plants. One purpose of a national economy is, judging from its behavior, to keep growing larger. An important function of almost every system is to ensure its own perpetuation.

System purposes need not be human purposes and are not necessar- ily 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. No one intends to produce a society with rampant drug addiction and crime, but consider the combined purposes and consequent actions of the actors involved:

• desperate people who want quick relief from psychological pain

• farmers, dealers, and bankers who want to earn money • pushers who are less bound by civil law than are the police

who oppose them • governments that make harmful substances illegal and use

police power to interdict them • wealthy people living in close proximity to poor people • nonaddicts who are more interested in protecting themselves

than in encouraging recovery of addicts

Altogether, these make up a system from which it is extremely diffi cult to eradicate drug addiction and crime.

Systems can be nested within systems. Therefore, there can be purposes within purposes. The purpose of a university is to discover and preserve knowledge and pass it on to new generations. Within the university, the purpose of a student may be to get good grades, the purpose of a professor

A NOTE ON LANGUAGE The word function is generally used for a nonhuman system, the word purpose for a human one, but the distinction is not abso- lute, since so many systems have both human and nonhuman elements.

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16 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

may be to get tenure, the purpose of an administrator may be to balance the budget. Any of those sub-purposes could come into confl ict with the overall purpose—the student could cheat, the professor could ignore the students in order to publish papers, the administrator could balance the budget by fi ring professors. Keeping sub-purposes and overall system purposes in harmony is an essential function of successful systems. I’ll get back to this point later when we come to hierarchies.

You can understand the relative importance of a system’s elements, interconnections, and purposes by imagining them changed one by one. Changing elements usually has the least effect on the system. If you change all the players on a football team, it is still recognizably a football team. (It may play much better or much worse—particular elements in a system can indeed be important.) A tree changes its cells constantly, its leaves

every year or so, but it is still essentially the same tree. Your body replaces most of its cells every few weeks, but it goes on being your body. The univer- sity has a constant fl ow of students and a slower fl ow of professors and administrators, but it is still a university. In fact it is still the same univer- sity, distinct in subtle ways from others, just as

General Motors and the U.S. Congress somehow maintain their identities even though all their members change. A system generally goes on being itself, changing only slowly if at all, even with complete substitutions of its elements—as long as its interconnections and purposes remain intact.

If the interconnections change, the system may be greatly altered. It may even become unrecognizable, even though the same players are on the team. Change the rules from those of football to those of basketball, and you’ve got, as they say, a whole new ball game. If you change the interconnections in the tree—say that instead of taking in carbon dioxide and emitting oxygen, it does the reverse—it would no longer be a tree. (It would be an animal.) If in a university the students graded the professors, or if arguments were won by force instead of reason, the place would need a different name. It might be an interesting organization, but it would not be a university. Changing intercon- nections in a system can change it dramatically.

Changes in function or purpose also can be drastic. What if you keep the players and the rules but change the purpose—from winning to losing, for example? What if the function of a tree were not to survive and repro-

The least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior.

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CHAPTER ONE: THE BASICS 17

duce but to capture all the nutrients in the soil and grow to unlimited size? People have imagined many purposes for a university besides disseminat- ing knowledge—making money, indoctrinating people, winning football games. A change in purpose changes a system profoundly, even if every element and interconnection remains the same.

To ask whether elements, interconnections, or purposes are most impor- tant in a system is to ask an unsystemic question. All are essential. All inter- act. All have their roles. But the least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behav- ior. Interconnections are also critically important. Changing relationships usually changes system behavior. The elements, the parts of systems we are most likely to notice, are often (not always) least important in defi ning the unique characteristics of the system—unless changing an element also results in changing relationships or purpose.

Changing just one leader at the top—from a Brezhnev to a Gorbachev, or from a Carter to a Reagan—may or may not turn an entire nation in a new direction, though its land, factories, and hundreds of millions of people remain exactly the same. A leader can make that land and those factories and people play a different game with new rules, or can direct the play toward a new purpose.

And conversely, because land, factories, and people are long-lived, slowly changing, physical elements of the system, there is a limit to the rate at which any leader can turn the direction of a nation.

Bathtubs 101—Understanding System Behavior over Time

Information contained in nature . . . allows us a partial reconstruc- tion of the past. . . . The development of the meanders in a river, the increasing complexity of the earth’s crust . . . are information-stor- ing devices in the same manner that genetic systems are. . . . Storing information means increasing the complexity of the mechanism.

—Ramon Margalef 2

A stock is the foundation of any system. Stocks are the elements of the system that you can see, feel, count, or measure at any given time. A system stock is just what it sounds like: a store, a quantity, an accumulation of

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18 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

material or information that has built up over time. It may be the water in a bathtub, a population, the books in a bookstore, the wood in a tree, the money in a bank, your own self-confi dence. A stock does not have to be

physical. Your reserve of good will toward others or your supply of hope that the world can be better are both stocks.

Stocks change over time through the actions of a fl ow. Flows are fi lling and draining, births and

deaths, purchases and sales, growth and decay, deposits and withdrawals, successes and failures. A stock, then, is the present memory of the history of changing fl ows within the system.

For example, an underground mineral deposit is a stock, out of which comes a fl ow of ore through mining. The infl ow of ore into a mineral deposit is minute in any time period less than eons. So I have chosen to draw (Figure 2) a simplifi ed picture of the system without any infl ow. All system diagrams and descriptions are simplifi ed versions of the real world.

Water in a reservoir behind a dam is a stock, into which fl ow rain and river water, and out of which fl ows evaporation from the reservoir’s surface as well as the water discharged through the dam.

A stock is the memory of the history of changing fl ows within the system.

Figure 1. How to read stock-and-fl ow diagrams. In this book, stocks are shown as boxes, and fl ows as arrow-headed “pipes” leading into or out of the stocks. The small T on each fl ow signi- fi es a “faucet;” it can be turned higher or lower, on or o! . The “clouds” stand for wherever the fl ows come from and go to—the sources and sinks that are being ignored for the purposes of the present discussion.

outflowinflow stock

mining

mineral deposit

Figure 2. A stock of minerals depleted by mining.

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CHAPTER ONE: THE BASICS 19

The volume of wood in the living trees in a forest is a stock. Its infl ow is the growth of the trees. Its outfl ows are the natural deaths of trees and the harvest by loggers. The logging harvest fl ows into another stock, perhaps an inventory of lumber at a mill. Wood fl ows out of the inventory stock as lumber sold to customers.

If you understand the dynamics of stocks and fl ows—their behavior over time—you understand a good deal about the behavior of complex systems. And if you have had much experience with a bathtub, you understand the dynamics of stocks and fl ows.

Imagine a bathtub fi lled with water, with its drain plugged up and its faucets turned off—an unchanging, undynamic, boring system. Now

river inflow discharge

rain evaporation

water in reservoir

Figure 3. A stock of water in a reservoir with multiple infl ows and outfl ows.

tree growth

lumber sales

lumber inventory

wood in living

trees

logging

tree deaths

Figure 4. A stock of lumber linked to a stock of trees in a forest.

outflowinflow water in tub

Figure 5. The structure of a bathtub system—one stock with one infl ow and one outfl ow.

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20 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

mentally pull the plug. The water runs out, of course. The level of water in the tub goes down until the tub is empty.

Now imagine starting again with a full tub, and again open the drain, but this time, when the tub is about half empty, turn on the infl ow faucet so the rate of water fl owing in is just equal to that fl owing out. What happens?

50

40

30

20

10

0

stock of water in the tub

0 2 4 6 8 10

ga llo

ns

minutes

Figure 6. Water level in a tub when the plug is pulled.

A NOTE ON READING GRAPHS OF BEHAVIOR OVER TIME

Systems thinkers use graphs of system behavior to understand trends over time, rather than focusing attention on individual events. We also use behavior-over-time graphs to learn whether the system is approaching a goal or a limit, and if so, how quickly.

The variable on the graph may be a stock or a fl ow. The pattern—the shape of the variable line—is important, as are the points at which that line changes shape or direction. The precise numbers on the axes are often less important.

The horizontal axis of time allows you to ask questions about what came before, and what might happen next. It can help you focus on the time horizon appropriate to the question or problem you are investigating.

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CHAPTER ONE: THE BASICS 21

The amount of water in the tub stays constant at whatever level it had reached when the infl ow became equal to the outfl ow. It is in a state of dynamic equilibrium—its level does not change, although water is contin- uously fl owing through it.

Imagine turning the infl ow on somewhat harder while keeping the outfl ow constant. The level of water in the tub slowly rises. If you then turn the infl ow

outflow

10

8

6

4

2

0 0 2 4 6 8 10

ga llo

ns /m

inu te

minutes

inflow

stock of water in the tub 50

40

30

20

10

0 0 2 4 6 8 10

ga llo

ns

minutes

Figure 7. Constant outfl ow, infl ow turned on after 5 minutes, and the resulting changes in the stock of water in the tub.

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22 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

faucet down again to match the outfl ow exactly, the water in the tub will stop rising. Turn it down some more, and the water level will fall slowly.

This model of a bathtub is a very simple system with just one stock, one infl ow, and one outfl ow. Over the time period of interest (minutes), I have assumed that evaporation from the tub is insignifi cant, so I have not included that outfl ow. All models, whether mental models or mathemati- cal models, are simplifi cations of the real world. You know all the dynamic possibilities of this bathtub. From it you can deduce several important principles that extend to more complicated systems:

• As long as the sum of all infl ows exceeds the sum of all outfl ows, the level of the stock will rise.

• As long as the sum of all outfl ows exceeds the sum of all infl ows, the level of the stock will fall.

• If the sum of all outfl ows equals the sum of all infl ows, the stock level will not change; it will be held in dynamic equilib- rium at whatever level it happened to be when the two sets of fl ows became equal.

The human mind seems to focus more easily on stocks than on fl ows. On top of that, when we do focus on fl ows, we tend to focus on infl ows more easily than on outfl ows. Therefore, we sometimes miss seeing that we can

fi ll a bathtub not only by increasing the infl ow rate, but also by decreasing the outfl ow rate. Everyone understands that you can prolong the life of an oil- based economy by discovering new oil deposits. It seems to be harder to understand that the same result can be achieved by burning less oil. A break- through in energy effi ciency is equivalent, in its effect on the stock of available oil, to the discovery

of a new oil fi eld—although different people profi t from it. Similarly, a company can build up a larger workforce by more hiring, or

it can do the same thing by reducing the rates of quitting and fi ring. These two strategies may have very different costs. The wealth of a nation can be boosted by investment to build up a larger stock of factories and machines. It also can be boosted, often more cheaply, by decreasing the rate at which factories and machines wear out, break down, or are discarded.

A stock can be increased by decreasing its outfl ow rate as well as by increas- ing its infl ow rate. There’s more than one way to fi ll a bathtub!

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CHAPTER ONE: THE BASICS 23

You can adjust the drain or faucet of a bathtub—the fl ows—abruptly, but it is much more diffi cult to change the level of water—the stock— quickly. Water can’t run out the drain instantly, even if you open the drain all the way. The tub can’t fi ll up immediately, even with the infl ow faucet on full blast. A stock takes time to change, because fl ows take time to fl ow. That’s a vital point, a key to understanding why systems behave as they do. Stocks usually change slowly. They can act as delays, lags, buffers, ballast, and sources of momentum in a system. Stocks, espe- cially large ones, respond to change, even sudden change, only by gradual fi lling or emptying.

People often underestimate the inherent momentum of a stock. It takes a long time for populations to grow or stop growing, for wood to accumulate in a forest, for a reservoir to fi ll up, for a mine to be depleted. An economy cannot build up a large stock of functioning factories and highways and electric plants overnight, even if a lot of money is available. Once an economy has a lot of oil-burning furnaces and automobile engines, it cannot change quickly to furnaces and engines that burn a different fuel, even if the price of oil suddenly changes. It has taken decades to accumulate the strato- spheric pollutants that destroy the earth’s ozone layer; it will take decades for those pollutants to be removed.

Changes in stocks set the pace of the dynamics of systems. Industrialization cannot proceed faster than the rate at which factories and machines can be constructed and the rate at which human beings can be educated to run and maintain them. Forests can’t grow overnight. Once contaminants have accumulated in groundwater, they can be washed out only at the rate of groundwater turnover, which may take decades or even centuries.

The time lags that come from slowly changing stocks can cause problems in systems, but they also can be sources of stability. Soil that has accumulated over centuries rarely erodes all at once. A population that has learned many skills doesn’t forget them immediately. You can pump groundwater faster than the rate it recharges for a long time before the aquifer is drawn down far enough to be damaged. The time lags imposed by stocks allow room to maneuver, to experiment, and to revise policies that aren’t working.

If you have a sense of the rates of change of stocks, you don’t expect things to happen faster than they can happen. You don’t give up too soon.

Stocks generally change slowly, even when the fl ows into or out of them change suddenly. Therefore, stocks act as delays or bu# ers or shock absorbers in systems.

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24 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

You can use the opportunities presented by a system’s momentum to guide it toward a good outcome—much as a judo expert uses the momentum of an opponent to achieve his or her own goals.

There is one more important principle about the role of stocks in systems, a principle that will lead us directly to the concept of feedback. The pres-

ence of stocks allows infl ows and outfl ows to be independent of each other and temporarily out of balance with each other.

It would be hard to run an oil company if gaso- line had to be produced at the refi nery at exactly the rate the cars were burning it. It isn’t feasible to harvest a forest at the precise rate at which the

trees are growing. Gasoline in storage tanks and wood in the forest are both stocks that permit life to proceed with some certainty, continuity, and predictability, even though fl ows vary in the short term.

Human beings have invented hundreds of stock-maintaining mecha- nisms to make infl ows and outfl ows independent and stable. Reservoirs enable residents and farmers downriver to live without constantly adjust- ing their lives and work to a river’s varying fl ow, especially its droughts and fl oods. Banks enable you temporarily to earn money at a rate different from how you spend. Inventories of products along a chain from distributors to wholesalers to retailers allow production to proceed smoothly although customer demand varies, and allow customer demand to be fi lled even though production rates vary.

Most individual and institutional decisions are designed to regulate the levels in stocks. If inventories rise too high, then prices are cut or advertis- ing budgets are increased, so that sales will go up and inventories will fall. If the stock of food in your kitchen gets low, you go to the store. As the stock of growing grain rises or fails to rise in the fi elds, farmers decide whether to apply water or pesticide, grain companies decide how many barges to book for the harvest, speculators bid on future values of the harvest, cattle growers build up or cut down their herds. Water levels in reservoirs cause all sorts of corrective actions if they rise too high or fall too low. The same can be said for the stock of money in your wallet, the oil reserves owned by an oil company, the pile of woodchips feeding a paper mill, and the concentration of pollutants in a lake.

People monitor stocks constantly and make decisions and take actions

Stocks allow infl ows and outfl ows to be decoupled and to be independent and temporarily out of balance with each other.

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CHAPTER ONE: THE BASICS 25

designed to raise or lower stocks or to keep them within acceptable ranges. Those decisions add up to the ebbs and fl ows, successes and problems, of all sorts of systems. Systems thinkers see the world as a collection of stocks along with the mechanisms for regulating the levels in the stocks by manipulating fl ows.

That means system thinkers see the world as a collection of “feedback processes.”

How the System Runs Itself—Feedback

Systems of information-feedback control are fundamental to all life and human endeavor, from the slow pace of biological evolu- tion to the launching of the latest space satellite. . . . Everything we do as individuals, as an industry, or as a society is done in the context of an information-feedback system.

—Jay W. Forrester3

When a stock grows by leaps and bounds or declines swiftly or is held within a certain range no matter what else is going on around it, it is likely that there is a control mechanism at work. In other words, if you see a behavior that persists over time, there is likely a mechanism creating that consistent behavior. That mechanism operates through a feedback loop. It is the consistent behavior pattern over a long period of time that is the fi rst hint of the existence of a feedback loop.

A feedback loop is formed when changes in a stock affect the fl ows into or out of that same stock. A feedback loop can be quite simple and direct. Think of an interest-bearing savings account in a bank. The total amount of money in the account (the stock) affects how much money comes into the account as interest. That is because the bank has a rule that the account earns a certain percent interest each year. The total dollars of interest paid into the account each year (the fl ow in) is not a fi xed amount, but varies with the size of the total in the account.

You experience another fairly direct kind of feedback loop when you get your bank statement for your checking account each month. As your level of available cash in the checking account (a stock) goes down, you may decide to work more hours and earn more money. The money entering

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26 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

your bank account is a fl ow that you can adjust in order to increase your stock of cash to a more desirable level. If your bank account then grows very large, you may feel free to work less (decreasing the infl ow). This kind of feedback loop is keeping your level of cash available within a range that is acceptable to you. You can see that adjusting your earnings is not the only feedback loop that works on your stock of cash. You also may be able to adjust the outfl ow of money from your account, for example. You can imagine an outfl ow-adjusting feedback loop for spending.

Feedback loops can cause stocks to maintain their level within a range or grow or decline. In any case, the fl ows into or out of the stock are adjusted because of changes in the size of the stock itself. Whoever or whatever is monitoring the stock’s level begins a corrective process, adjusting rates of infl ow or outfl ow (or both) and so changing the stock’s level. The stock level feeds back through a chain of signals and actions to control itself.

outflow

inflow

stock

stock

Figure 8. How to read a stock-and-fl ow diagram with feedback loops. Each diagram distin- guishes the stock, the fl ow that changes the stock, and the information link (shown as a thin, curved arrow) that directs the action. It emphasizes that action or change always proceeds through adjusting fl ows.

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CHAPTER ONE: THE BASICS 27

Not all systems have feedback loops. Some systems are relatively simple open-ended chains of stocks and fl ows. The chain may be affected by outside factors, but the levels of the chain’s stocks don’t affect its fl ows. However, those systems that contain feedback loops are common and may be quite elegant or rather surprising, as we shall see.

Stabilizing Loops—Balancing Feedback

One common kind of feedback loop stabilizes the stock level, as in the checking-account example. The stock level may not remain completely fi xed, but it does stay within an acceptable range. What follows are some more stabilizing feedback loops that may be familiar to you. These exam- ples start to detail some of the steps within a feedback loop.

If you’re a coffee drinker, when you feel your energy level run low, you may grab a cup of hot black stuff to perk you up again. You, as the coffee drinker, hold in your mind a desired stock level (energy for work). The purpose of this caffeine-delivery system is to keep your actual stock level near or at your desired level. (You may have other purposes for drinking coffee as well: enjoying the fl avor or engaging in a social activity.) It is the

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 depen- dent on the level of the stock, and back again through a fl ow to change the stock.

discrepancy

energy expenditure

energy available for work

stored energy in body

Bco!ee intake

metabolic mobilization

of energy

desired energy level

Figure 9. Energy level of a co! ee drinker.

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28 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

gap, the discrepancy, between your actual and desired levels of energy for work that drives your decisions to adjust your daily caffeine intake.

Notice that the labels in Figure 9, like all the diagram labels in this book, are direction-free. The label says “stored energy in body” not “low energy level,” “coffee intake” not “more coffee.” That’s because feedback loops often can operate in two directions. In this case, the feedback loop can correct an oversupply as well as an undersupply. If you drink too much coffee and fi nd yourself bouncing around with extra energy, you’ll lay off the caffeine for a while. High energy creates a discrepancy that says “too much,” which then causes you to reduce your coffee intake until your energy level settles down. The diagram is intended to show that the loop works to drive the stock of energy in either direction.

I could have shown the infl ow of energy coming from a cloud, but instead I made the system diagram slightly more complicated. Remember—all system diagrams are simplifi cations of the real world. We each choose how much complexity to look at. In this example, I drew another stock—the stored energy in the body that can be activated by the caffeine. I did that to indicate that there is more to the system than one simple loop. As every coffee drinker knows, caffeine is only a short-term stimulant. It lets you run your motor faster, but it doesn’t refi ll your personal fuel tank. Eventually the caffeine high wears off, leaving the body more energy-defi cient than it was before. That drop could reactivate the feedback loop and gener- ate another trip to the coffee pot. (See the discussion of addiction later in this book.) Or it could activate some longer-term and healthier feedback responses: Eat some food, take a walk, get some sleep.

This kind of stabilizing, goal-seeking, regulating loop is called a balanc- ing feedback loop, so I put a B inside the loop in the diagram. Balancing feedback loops are goal-seeking or stability-seeking. Each tries to keep a stock at a given value or within a range of values. A balancing feedback loop opposes whatever direction of change is imposed on the system. If you push a stock too far up, a balancing loop will try to pull it back down. If you shove it too far down, a balancing loop will try to bring it back up.

Here’s another balancing feedback loop that involves coffee, but one that works through physical law rather than human decision. A hot cup of coffee will gradually cool down to room temperature. Its rate of cooling depends on the difference between the temperature of the coffee and the temperature of the room. The greater the difference, the faster the coffee

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CHAPTER ONE: THE BASICS 29

will cool. The loop works the other way too—if you make iced coffee on a hot day, it will warm up until it has the same temperature as the room. The function of this system is to bring the discrepancy between coffee’s temperature and room’s temperature to zero, no matter what the direction of the discrepancy.

Starting with coffee at different temperatures, from just below boiling to just above freezing, the graphs in Figure 11 show what will happen to the temperature over time (if you don’t drink the coffee). You can see here the “homing” behavior of a balancing feedback loop. Whatever the initial value of the system stock (coffee temperature in this case), whether it is above or below the “goal” (room temperature), the feedback loop brings it toward

discrepancy

co!ee temperature

B

cooling

room temperature discrepancy

room temperature

co!ee temperature

B

heating

Figure 10. A cup of co! ee cooling (left) or warming (right).

iced co!ee warming

100

80

60

40

20

0 0 2 4 6 8

te m

pe rat

ur e (

ºC )

minutes

room temperature = 18ºC

hot co!ee cooling

Figure 11. Co! ee temperature as it approaches a room temperature of 18°C.

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30 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

the goal. The change is faster at fi rst, and then slower, as the discrepancy between the stock and the goal decreases.

This behavior pattern—gradual approach to a system-defi ned goal— also can be seen when a radioactive element decays, when a missile fi nds its target, when an asset depreciates, when a reservoir is brought up or down to its desired level, when your body adjusts its blood-sugar concentration, when you pull your car to a stop at a stoplight. You can think of many more

examples. The world is full of goal-seeking feedback loops. The presence of a feedback mechanism doesn’t necessarily mean that the

mechanism works well. The feedback mechanism may not be strong enough to bring the stock to the desired level. Feedbacks—the interconnections, the information part of the system—can fail for many reasons. Information can arrive too late or at the wrong place. It can be unclear or incomplete or hard to interpret. The action it triggers may be too weak or delayed or resource- constrained or simply ineffective. The goal of the feedback loop may never be reached by the actual stock. But in the simple example of a cup of coffee, the drink eventually will reach room temperature.

Runaway Loops—Reinforcing Feedback

I’d need rest to refresh my brain, and to get rest it’s necessary to travel, and to travel one must have money, and in order to get money you have to work. . . . I am in a vicious circle . . . from which it is impossible to escape.

—Honoré Balzac,4 19th century novelist and playwright

Here we meet a very important feature. It would seem as if this were circular reasoning; profi ts fell because investment fell, and investment fell because profi ts fell.

—Jan Tinbergen,5 economist

The second kind of feedback loop is amplifying, reinforcing, self-multiply- ing, snowballing—a vicious or virtuous circle that can cause healthy growth

Balancing feedback loops are equilibrating or goal-seeking structures in systems and are both sources of stability and sources of resistance to change.

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CHAPTER ONE: THE BASICS 31

or runaway destruction. It is called a reinforcing feedback loop, and will be noted with an R in the diagrams. It generates more input to a stock the more that is already there (and less input the less that is already there). A reinforc- ing feedback loop enhances whatever direction of change is imposed on it.

For example:

• When we were kids, the more my brother pushed me, the more I pushed him back, so the more he pushed me back, so the more I pushed him back.

• The more prices go up, the more wages have to go up if people are to maintain their standards of living. The more wages go up, the more prices have to go up to maintain profi ts. This means that wages have to go up again, so prices go up again.

• The more rabbits there are, the more rabbit parents there are to make baby rabbits. The more baby rabbits there are, the more grow up to become rabbit parents, to have even more baby rabbits.

• The more soil is eroded from the land, the less plants are able to grow, so the fewer roots there are to hold the soil, so the more soil is eroded, so less plants can grow.

• The more I practice piano, the more pleasure I get from the sound, and so the more I play the piano, which gives me more practice.

Reinforcing loops are found wherever a system element has the abil- ity to reproduce itself or to grow as a constant fraction of itself. Those elements include populations and economies. Remember the example of

money in bank account

Rinterest rate

interest added

Figure 12. Interest-bearing bank account.

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32 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

the interest-bearing bank account? The more money you have in the bank, the more interest you earn, which is added to the money already in the bank, where it earns even more interest.

Figure 13 shows how this reinforcing loop multiplies money, starting with $100 in the bank, and assuming no deposits and no withdrawals over a period of twelve years. The fi ve lines show fi ve different interest rates, from 2 percent to 10 percent per year.

This is not simple linear growth. It is not constant over time. The growth of the bank account at lower interest rates may look linear in the fi rst few years. But, in fact, growth goes faster and faster. The more is there, the more is added. This kind of growth is called “exponential.” It’s either good news or bad news, depending on what is growing—money in the bank, people

with HIV/AIDS, pests in a cornfi eld, a national economy, or weapons in an arms race.

In Figure 14, the more machines and factories (collectively called “capital”) you have, the more goods and services (“output”) you can produce. The more output you can produce, the more you can invest in new machines and factories. The more you make, the more capacity you have to make even more. This reinforcing feedback loop is the central engine of growth in an economy.

350

300

250

200

150

100

50

0 0 3 6 9 12

do lla

rs

years

$313.84

8% interest

10% interest

$126.82

$160.10

$201.22

$251.82

6% interest

4% interest

2% interest

Figure 13. Growth in savings with various interest rates.

Reinforcing feedback loops are self-enhancing, leading to exponential growth or to runaway collapses over time. They are found when- ever a stock has the capac- ity to reinforce or reproduce itself.

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CHAPTER ONE: THE BASICS 33

By now you may be seeing how basic balancing and reinforcing feedback loops are to systems. Sometimes I challenge my students to try to think of any human decision that occurs without a feedback loop—that is, a deci- sion that is made without regard to any information about the level of the stock it infl uences. Try thinking about that yourself. The more you do, the more you’ll begin to see feedback loops everywhere.

The most common “non-feedback” decisions students suggest are falling in love and committing suicide. I’ll leave it to you to decide whether you think these are actually decisions made with no feedback involved.

Watch out! If you see feedback loops everywhere, you’re already in danger of becoming a systems thinker! Instead of seeing only how A causes B, you’ll begin to wonder how B may also infl uence A—and how A might reinforce or reverse itself. When you hear in the nightly news that the Federal Reserve

capital

Rfraction of output invested

output

investment

Figure 14. Reinvestment in capital.

HINT ON REINFORCING LOOPS AND DOUBLING TIME

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.

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34 PART ONE: SYSTEM STRUCTURE AND BEHAVIOR

Bank has done something to control the economy, you’ll also see that the economy must have done something to affect the Federal Reserve Bank. When someone tells you that population growth causes poverty, you’ll ask yourself how poverty may cause population growth.

You’ll be thinking not in terms of a static world, but a dynamic one. You’ll stop looking for who’s to blame; instead you’ll start asking, “What’s the system?” The concept of feedback opens up the idea that a system can cause its own behavior.

So far, I have limited this discussion to one kind of feedback loop at a time. Of course, in real systems feedback loops rarely come singly. They are linked together, often in fantastically complex patterns. A single stock is likely to have several reinforcing and balancing loops of differing strengths pulling it in several directions. A single fl ow may be adjusted by the contents of three or fi ve or twenty stocks. It may fi ll one stock while it drains another and feeds into decisions that alter yet another. The many feedback loops in a system tug against each other, trying to make stocks grow, die off, or come into balance with each other. As a result, complex systems do much more than stay steady or explode exponentially or approach goals smoothly—as we shall see.

THINK ABOUT THIS: If A causes B, is it possible that B also causes A?

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PART THREE Creating Change—in Systems

and in Our Philosophy

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— SIX —

Leverage Points— Places to Intervene in a System IBM . . . announced 25,000 new job cuts and a large reduction in spending on research. . . . Spending on development research is to be lowered by $1 billion next year. . . . Chairman John K. Akers . . . said IBM was still a world and industry leader in research but felt it could do better by “shifting to areas for growth,” meaning services, which need less capital but also return less profi t in the long run.

—Lawrence Malkin, International Herald Tribune, 19921

So, how do we change the structure of systems to produce more of what we want and less of that which is undesirable? After years of working with corporations on their systems problems, MIT’s Jay Forrester likes to say that the average manager can defi ne the current problem very cogently, identify the system structure that leads to the problem, and guess with great accuracy where to look for leverage points—places in the system where a small change could lead to a large shift in behavior.

This idea of leverage points is not unique to systems analysis—it’s embedded in legend: the silver bullet; the trimtab; the miracle cure; the secret passage; the magic password; the single hero who turns the tide of history; the nearly effortless way to cut through or leap over huge obstacles. We not only want to believe that there are leverage points, we want to know where they are and how to get our hands on them. Leverage points are points of power.

But Forrester goes on to point out that although people deeply involved in a system often know intuitively where to fi nd leverage points, more often than not they push the change in the wrong direction.

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146 PART THREE: CREATING CHANGE—IN SYSTEMS AND IN OUR PHILOSOPHY

The classic example of that backward intuition was my own introduc- tion to systems analysis, the World model. Asked by the Club of Rome—an international group of businessmen, statesmen, and scientists—to show how major global problems of poverty and hunger, environmental destruction, resource depletion, urban deterioration, and unemployment are related and how they might be solved, Forrester made a computer model and came out with a clear leverage point: growth.2 Not only popu- lation growth, but economic growth. Growth has costs as well as benefi ts, and we typically don’t count the costs—among which are poverty and hunger, environmental destruction, and so on—the whole list of prob- lems we are trying to solve with growth! What is needed is much slower growth, very different kinds of growth, and in some cases no growth or negative growth.

The world’s leaders are correctly fi xated on economic growth as the answer to virtually all problems, but they’re pushing with all their might in the wrong direction.

Another of Forrester’s classics was his study of urban dynamics, published in 1969, which demonstrated that subsidized low-income housing is a leverage point.3 The less of it there is, the better off the city is—even the low-income folks in the city. This model came out at a time when national policy dictated massive low-income housing projects, and Forrester was derided. Since then, many of those projects have been torn down in city after city.

Counterintuitive—that’s Forrester’s word to describe complex systems. Leverage points frequently are not intuitive. Or if they are, we too often use them backward, systematically worsening whatever problems we are trying to solve.

I have come up with no quick or easy formulas for fi nding leverage points in complex and dynamic systems. Give me a few months or years and I’ll fi gure it out. And I know from bitter experience that, because they are so counterintuitive, when I do discover a system’s leverage points, hardly anybody will believe me. Very frustrating—especially for those of us who yearn not just to understand complex systems, but to make the world work better.

It was in just such a moment of frustration that I proposed a list of places to intervene in a system during a meeting on the implications of global- trade regimes. I offer this list to you with much humility and wanting to

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CHAPTER SIX: LEVERAGE POINTS—PLACES TO INTERVENE IN A SYSTEM 147

leave room for its evolution. What bubbled up in me that day was distilled from decades of rigorous analysis of many different kinds of systems done by many smart people. But complex systems are, well, complex. It’s danger- ous to generalize about them. What you read here is still a work in prog- ress; it’s not a recipe for fi nding leverage points. Rather, it’s an invitation to think more broadly about system change.

As systems become complex, their behavior can become surprising. Think about your checking account. You write checks and make depos- its. A little interest keeps fl owing in (if you have a large enough balance) and bank fees fl ow out even if you have no money in the account, thereby creating an accumulation of debt. Now attach your account to a thousand others and let the bank create loans as a function of your combined and fl uctuating deposits, link a thousand of those banks into a federal reserve system—and you begin to see how simple stocks and fl ows, plumbed together, create systems way too complicated and dynamically complex to fi gure out easily.

That’s why leverage points are often not intuitive. And that’s enough systems theory to proceed to the list.

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

Think about the basic stock-and-fl ow bathtub from Chapter One. The size of the fl ows is a matter of numbers and how quickly those numbers can be changed. Maybe the faucet turns hard, so it takes a while to get the water fl owing or to turn it off. Maybe the drain is blocked and can allow only a small fl ow, no matter how open it is. Maybe the faucet can deliver with the force of a fi re hose. Some of these kinds of parameters are physically locked in and unchangeable, but many can be varied and so are popular intervention points.

Consider the national debt. It may seem like a strange stock; it is a money hole. The rate at which the hole deepens is called the annual defi cit. Income from taxes shrinks the hole, government expenditures expand it. Congress and the president spend most of their time arguing about the many, many parameters that increase (spending) and decrease (taxing) the size or depth of the hole. Since those fl ows are connected to us, the voters,

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148 PART THREE: CREATING CHANGE—IN SYSTEMS AND IN OUR PHILOSOPHY

these are politically charged parameters. But, despite all the fi reworks, and no matter which party is in charge, the money hole has been deepening for years now, just at different rates.

To adjust the dirtiness of the air we breathe, the government sets param- eters called ambient-air-quality standards. To ensure some standing stock of forest (or some fl ow of money to logging companies), it sets allowed annual cuts. Corporations adjust parameters such as wage rates and prod- uct prices, with an eye on the level in their profi t bathtub—the bottom line.

The amount of land we set aside for conservation each year. The mini- mum wage. How much we spend on AIDS research or Stealth bombers. The service charge the bank extracts from your account. All of these are parameters, adjustments to faucets. So, by the way, is fi ring people and getting new ones, including politicians. Putting different hands on the faucets may change the rate at which the faucets turn, but if they’re the same old faucets, plumbed into the same old system, turned according to the same old information and goals and rules, the system behavior isn’t going to change much. Electing Bill Clinton was defi nitely different from electing the elder George Bush, but not all that different, given that every president is plugged into the same political system. (Changing the way money fl ows in that system would make much more of a difference—but I’m getting ahead of myself on this list.)

Numbers, the sizes of fl ows, are dead last on my list of powerful interven- tions. Diddling with the details, arranging the deck chairs on the Titanic. Probably 90—no 95, no 99 percent—of our attention goes to parameters, but there’s not a lot of leverage in them.

It’s not that parameters aren’t important—they can be, especially in the short term and to the individual who’s standing directly in the fl ow. People care deeply about such variables as taxes and the minimum wage, and so fi ght fi erce battles over them. But changing these variables rarely changes the behavior of the national economy system. If the system is chronically stagnant, parameter changes rarely kick-start it. If it’s wildly variable, they usually don’t stabilize it. If it’s growing out of control, they don’t slow it down.

Whatever cap we put on campaign contributions, it doesn’t clean up poli- tics. The Fed’s fi ddling with the interest rate hasn’t made business cycles go away. (We always forget that during upturns, and are shocked, shocked by

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CHAPTER SIX: LEVERAGE POINTS—PLACES TO INTERVENE IN A SYSTEM 149

the downturns.) After decades of the strictest air pollution standards in the world, Los Angeles air is less dirty, but it isn’t clean. Spending more on police doesn’t make crime go away.

Since I’m about to get into some examples where parameters are lever- age points, let me stick in a big caveat here. Parameters become leverage points when they go into ranges that kick off one of the items higher on this list. Interest rates, for example, or birth rates, control the gains around reinforcing feedback loops. System goals are parameters that can make big differences.

These kinds of critical numbers are not nearly as common as people seem to think they are. Most systems have evolved or are designed to stay far out of range of critical parameters. Mostly, the numbers are not worth the sweat put into them.

Here’s a story a friend sent me over the Internet to makes that point:

When I became a landlord, I spent a lot of time and energy trying to fi gure out what would be a “fair” rent to charge.

I tried to consider all the variables, including the relative incomes of my tenants, my own income and cash-fl ow needs, which expenses were for upkeep and which were capital expenses, the equity versus the interest portion of the mortgage payments, how much my labor on the house was worth, etc.

I got absolutely nowhere. Finally I went to someone who specializes in giving money advice. She said: “You’re acting as though there is a fi ne line at which the rent is fair, and at any point above that point the tenant is being screwed and at any point below that you are being screwed. In fact, there is a large gray area in which both you and the tenant are getting a good, or at least a fair, deal. Stop worrying and get on with your life.”4

11. Bu% ers—The sizes of stabilizing stocks relative to their fl ows

Consider a huge bathtub with slow in- and outfl ows. Now think about a small one with very fast fl ows. That’s the difference between a lake and a river. You hear about catastrophic river fl oods much more often than

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150 PART THREE: CREATING CHANGE—IN SYSTEMS AND IN OUR PHILOSOPHY

catastrophic lake fl oods, because stocks that are big, relative to their fl ows, are more stable than small ones. In chemistry and other fi elds, a big, stabilizing stock is known as a buffer.

The stabilizing power of buffers is why you keep money in the bank rather than living from the fl ow of change through your pocket. It’s why stores hold inventory instead of calling for new stock just as customers carry the old stock out the door. It’s why we need to maintain more than the minimum breeding population of an endangered species. Soils in the eastern United States are more sensitive to acid rain than soils in the west, because they haven’t got big buffers of calcium to neutralize acid.

You can often stabilize a system by increasing the capacity of a buffer.5 But if a buffer is too big, the system gets infl exible. It reacts too slowly. And big buffers of some sorts, such as water reservoirs or inventories, cost a lot to build or maintain. Businesses invented just-in-time inventories, because occasional vulnerability to fl uctuations or screw-ups is cheaper (for them, anyway) than certain, constant inventory costs—and because small-to- vanishing inventories allow more fl exible response to shifting demand.

There’s leverage, sometimes magical, in changing the size of buffers. But buffers are usually physical entities, not easy to change. The acid absorp- tion capacity of eastern soils is not a leverage point for alleviating acid rain damage. The storage capacity of a dam is literally cast in concrete. So I haven’t put buffers very high on the list of leverage points.

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

The plumbing structure, the stocks and fl ows and their physical arrange- ment, can have an enormous effect on how the system operates. When the Hungarian road system was laid out so all traffi c from one side of the nation to the other had to pass through central Budapest, that determined a lot about air pollution and commuting delays that are not easily fi xed by pollution control devices, traffi c lights, or speed limits.

The only way to fi x a system that is laid out poorly is to rebuild it, if you can. Amory Lovins and his team at Rocky Mountain Institute have done wonders on energy conservation by simply straightening out bent pipes and enlarging ones that are too small. If we did similar energy retrofi ts on

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CHAPTER SIX: LEVERAGE POINTS—PLACES TO INTERVENE IN A SYSTEM 151

all the buildings in the United States, we could shut down many of our electric power plants.

But often physical rebuilding is the slowest and most expensive kind of change to make in a system. Some stock-and-fl ow structures are just plain unchangeable. The baby-boom swell in the U.S. population fi rst caused pressure on the elementary school system, then high schools, then colleges, then jobs and housing, and now we’re supporting its retirement. There’s not much we can do about it, because fi ve-year-olds become six-year-olds, and sixty-four-year-olds become sixty-fi ve-year-olds predictably and unstop- pably. The same can be said for the lifetime of destructive CFC molecules in the ozone layer, for the rate at which contaminants get washed out of aquifers, for the fact that an ineffi cient car fl eet takes ten to twenty years to turn over.

Physical structure is crucial in a system, but is rarely a leverage point, because changing it is rarely quick or simple. The leverage point is in proper design in the fi rst place. After the structure is built, the leverage is in under- standing its limitations and bottlenecks, using it with maximum effi ciency, and refraining from fl uctuations or expansions that strain its capacity.

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

Delays in feedback loops are critical determinants of system behavior. They are common causes of oscillations. If you’re trying to adjust a stock (your store inventory) to meet your goal, but you receive only delayed information about what the state of the stock is, you will overshoot and undershoot your goal. The same is true if your information is timely, but your response isn’t. For example, it takes several years to build an electric power plant that will likely last thirty years. Those delays make it impos- sible to build exactly the right number of power plants to supply rapidly changing demand for electricity. Even with immense effort at forecasting, almost every electricity industry in the world experiences long oscillations between overcapacity and undercapacity. A system just can’t respond to short-term changes when it has long-term delays. That’s why a massive central-planning system, such as the Soviet Union or General Motors, necessarily functions poorly.

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152 PART THREE: CREATING CHANGE—IN SYSTEMS AND IN OUR PHILOSOPHY

Because we know they’re important, we see delays wherever we look. For example, the delay between the time when a pollutant is dumped on the land and when it trickles down to the groundwater; or the delay between the birth of a child and the time when that child is ready to have a child; or the delay between the fi rst successful test of a new technology and the time when that technology is installed throughout the economy; or the time it takes for a price to adjust to a supply-demand imbalance.

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 amplifi ed by the jumpi- ness of the response. Delays that are too long cause damped, sustained, or exploding oscillations, depending on how much too long. Overlong delays in a system with a threshold, a danger point, a range past which irreversible damage can occur, cause overshoot and collapse.

I would list delay length as a high leverage point, except for the fact that delays are not often easily changeable. Things take as long as they take. You can’t do a lot about the construction time of a major piece of capital, or the maturation time of a child, or the growth rate of a forest. It’s usually easier to slow down the change rate, so that inevitable feedback delays won’t cause so much trouble. That’s why growth rates are higher up on the leverage- point list than delay times.

And that’s why slowing economic growth is a greater leverage point in Forrester’s World model than faster technological development or freer market prices. Those are attempts to speed up the rate of adjustment. But the world’s physical capital stock, its factories and boilers, the concrete manifestations of its working technologies, can change only so fast, even in the face of new prices or new ideas—and prices and ideas don’t change instantly either, not through a whole global culture. There’s more leverage in slowing the system down so technologies and prices can keep up with it, than there is in wishing the delays would go away.

But if there is a delay in your system that can be changed, changing it can have big effects. Watch out! Be sure you change it in the right direction! (For example, the great push to reduce information and money-transfer delays in fi nancial markets is just asking for wild gyrations.)

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

Now we’re beginning to move from the physical part of the system to the information and control parts, where more leverage can be found.

Balancing feedback loops are ubiquitous in systems. Nature evolves them and humans invent them as controls to keep important stocks within safe bounds. A thermostat loop is the classic example. Its purpose is to keep the system stock called “temperature of the room” fairly constant near a desired level. Any balancing feedback loop needs a goal (the thermostat setting), a monitoring and signaling device to detect deviation from the goal (the thermostat), and a response mechanism (the furnace and/or air conditioner, fans, pumps, pipes, fuel, etc.).

A complex system usually has numerous balancing feedback loops it can bring into play, so it can self-correct under different conditions and impacts. Some of those loops may be inactive much of the time—like the emergency cooling system in a nuclear power plant, or your ability to sweat or shiver to maintain your body temperature—but their presence is critical to the long-term welfare of the system.

One of the big mistakes we make is to strip away these “emergency” response mechanisms because they aren’t often used and they appear to be costly. In the short term, we see no effect from doing this. In the long term, we drastically narrow the range of conditions over which the system can survive. One of the most heartbreaking ways we do this is in encroaching on the habitats of endangered species. Another is in encroaching on our own time for personal rest, recreation, socialization, and meditation.

The strength of a balancing loop—its ability to keep its appointed stock at or near its goal—depends on the combination of all its parameters and links—the accuracy and rapidity of monitoring, the quickness and power of response, the directness and size of corrective fl ows. Sometimes there are leverage points here.

Take markets, for example, the balancing feedback systems that are all but worshipped by many economists. They can indeed be marvels of self- correction, as prices vary to moderate supply and demand and keep them in balance. Price is the central piece of information signaling both produc- ers and consumers. The more the price is kept clear, unambiguous, timely, and truthful, the more smoothly markets will operate. Prices that refl ect full

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costs will tell consumers how much they can actually afford and will reward effi cient producers. Companies and governments are fatally attracted to the price leverage point, but too often determinedly push it in the wrong direc- tion with subsidies, taxes, and other forms of confusion.

These modifi cations weaken the feedback power of market signals by twisting information in their favor. The real leverage here is to keep them from doing it. Hence, the necessity of antitrust laws, truth-in-advertising laws, attempts to internalize costs (such as pollution fees), the removal of perverse subsidies, and other ways of leveling market playing fi elds.

Strengthening and clarifying market signals, such as full-cost account- ing, don’t get far these days, because of the weakening of another set of balancing feedback loops—those of democracy. This great system was invented to put self-correcting feedback between the people and their government. The people, informed about what their elected representa- tives do, respond by voting those representatives in or out of offi ce. The process depends on the free, full, unbiased fl ow of information back and forth between electorate and leaders. Billions of dollars are spent to limit and bias and dominate that fl ow of clear information. Give the people who want to distort market-price signals the power to infl uence government leaders, allow the distributors of information to be self-interested partners, and none of the necessary balancing feedbacks work well. Both market and democracy erode.

The strength of a balancing feedback loop is important relative to the impact it is designed to correct. If the impact increases in strength, the feed- backs have to be strengthened too. A thermostat system may work fi ne on a cold winter day—but open all the windows and its corrective power is no match for the temperature change imposed on the system. Democracy works better without the brainwashing power of centralized mass communications. Traditional controls on fi shing were suffi cient until sonar spotting and drift nets and other technologies made it possible for a few actors to catch the last fi sh. The power of big industry calls for the power of big government to hold it in check; a global economy makes global regulations necessary.

Examples of strengthening balancing feedback controls to improve a system’s self-correcting abilities include:

• preventive medicine, exercise, and good nutrition to bolster the body’s ability to fi ght disease,

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• integrated pest management to encourage natural predators of crop pests,

• the Freedom of Information Act to reduce government secrecy,

• monitoring systems to report on environmental damage, • protection for whistleblowers, and • impact fees, pollution taxes, and performance bonds to recap-

ture the externalized public costs of private benefi ts.

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

A balancing feedback loop is self-correcting; a reinforcing feedback loop is self-reinforcing. The more it works, the more it gains power to work some more, driving system behavior in one direction. The more people catch the fl u, the more they infect other people. The more babies are born, the more people grow up to have babies. The more money you have in the bank, the more interest you earn, the more money you have in the bank. The more the soil erodes, the less vegetation it can support, the fewer roots and leaves to soften rain and runoff, the more soil erodes. The more high-energy neutrons in the critical mass, the more they knock into nuclei and generate more high-energy neutrons, leading to a nuclear explosion or meltdown.

Reinforcing feedback loops are sources of growth, explosion, erosion, and collapse in systems. A system with an unchecked reinforcing loop ulti- mately will destroy itself. That’s why there are so few of them. Usually a balancing loop will kick in sooner or later. The epidemic will run out of infectible people—or people will take increasingly stronger steps to avoid being infected. The death rate will rise to equal the birth rate—or people will see the consequences of unchecked population growth and have fewer babies. The soil will erode away to bedrock, and after a million years the bedrock will crumble into new soil—or people will stop overgrazing, put up check dams, plant trees, and stop the erosion.

In all those examples, the fi rst outcome is what will happen if the rein- forcing loop runs its course, the second is what will happen if there’s an intervention to reduce its self-multiplying power. Reducing the gain around a reinforcing loop—slowing the growth—is usually a more

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powerful leverage point in systems than strengthening balancing loops, and far more preferable than letting the reinforcing loop run.

Population and economic growth rates in the World model are lever- age points, because slowing them gives the many balancing loops, through technology and markets and other forms of adaptation (all of which have limits and delays), time to function. It’s the same as slowing the car when you’re driving too fast, rather than calling for more responsive brakes or technical advances in steering.

There are many reinforcing feedback loops in society that reward the winners of a competition with the resources to win even bigger next time—the “success to the successful” trap. Rich people collect interest; poor people pay it. Rich people pay accountants and lean on politicians to reduce their taxes; poor people can’t. Rich people give their kids inheri- tances and good educations. Antipoverty programs are weak balancing loops that try to counter these strong reinforcing ones. It would be much more effective to weaken the reinforcing loops. That’s what progressive income tax, inheritance tax, and universal high-quality public education programs are meant to do. If the wealthy can infl uence government to weaken, rather than strengthen, those measures, then the government itself shifts from a balancing structure to one that reinforces success to the successful!

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.

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

In Chapter Four, we examined the story of the electric meter in a Dutch housing development—in some of the houses the meter was installed in the basement; in others it was installed in the front hall. With no other differences in the houses, electricity consumption was 30 percent lower in the houses where the meter was in the highly visible location in the front hall.

I love that story because it’s an example of a high leverage point in the

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information structure of the system. It’s not a parameter adjustment, not a strengthening or weakening of an existing feedback loop. It’s a new loop, delivering feedback to a place where it wasn’t going before.

Missing information fl ows is one of the most common causes of system malfunction. Adding or restoring information can be a powerful interven- tion, usually much easier and cheaper than rebuilding physical infrastruc- ture. The tragedy of the commons that is crashing the world’s commercial fi sheries occurs because there is little feedback from the state of the fi sh population to the decision to invest in fi shing vessels. Contrary to economic opinion, the price of fi sh doesn’t provide that feedback. As the fi sh get more scarce they become more expensive, and it becomes all the more profi table to go out and catch the last few. That’s a perverse feedback, a reinforc- ing loop that leads to collapse. It is not price information but population information that is needed.

It’s important that the missing feedback be restored to the right place and in compelling form. To take another tragedy of the commons example, it’s not enough to inform all the users of an aquifer that the groundwater level is dropping. That could initiate a race to the bottom. It would be more effective to set the cost of water to rise steeply as the pumping rate begins to exceed the recharge rate.

Other examples of compelling feedback are not hard to fi nd. Suppose taxpayers got to specify on their return forms what government services their tax payments must be spent on. (Radical democracy!) Suppose any town or company that puts a water intake pipe in a river had to put it immediately downstream from its own wastewater outfl ow pipe. Suppose any public or private offi cial who made the decision to invest in a nuclear power plant got the waste from that facility stored on his or her lawn. Suppose (this is an old one) the politicians who declare war were required to spend that war in the front lines.

There is a systematic tendency on the part of human beings to avoid accountability for their own decisions. That’s why there are so many miss- ing feedback loops—and why this kind of leverage point is so often popu- lar with the masses, unpopular with the powers that be, and effective, if you can get the powers that be to permit it to happen (or go around them and make it happen anyway).

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5. Rules—Incentives, punishments, constraints

The rules of the system defi ne its scope, its boundaries, its degrees of free- dom. Thou shalt not kill. Everyone has the right of free speech. Contracts are to be honored. The president serves four-year terms and cannot serve more than two of them. Nine people on a team, you have to touch every base, three strikes and you’re out. If you get caught robbing a bank, you go to jail.

Mikhail Gorbachev came to power in the Soviet Union and opened infor- mation fl ows (glasnost) and changed the economic rules (perestroika), and the Soviet Union saw tremendous change.

Constitutions are the strongest examples of social rules. Physical laws such as the second law of thermodynamics are absolute rules, whether we understand them or not or like them or not. Laws, punishments, incen- tives, and informal social agreements are progressively weaker rules.

To demonstrate the power of rules, I like to ask my students to imagine different ones for a college. Suppose the students graded the teachers, or each other. Suppose there were no degrees: You come to college when you want to learn something, and you leave when you’ve learned it. Suppose tenure were awarded to professors according to their ability to solve real- world problems, rather than to publish academic papers. Suppose a class got graded as a group, instead of as individuals.

As we try to imagine restructured rules and what our behavior would be under them, we come to understand the power of rules. They are high leverage points. Power over the rules is real power. That’s why lobby- ists congregate when Congress writes laws, and why the Supreme Court, which interprets and delineates the Constitution—the rules for writing the rules—has even more power than Congress. If you want to understand the deepest malfunctions of systems, pay attention to the rules and to who has power over them.

That’s why my systems intuition was sending off alarm bells as the new world trade system was explained to me. It is a system with rules designed by corporations, run by corporations, for the benefi t of corporations. Its rules exclude almost any feedback from any other sector of society. Most of its meetings are closed even to the press (no information fl ow, no feedback). It forces nations into reinforcing loops “racing to the bottom,” competing with each other to weaken environmental and social safeguards in order

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to attract corporate investment. It’s a recipe for unleashing “success to the successful” loops, until they generate enormous accumulations of power and huge centralized planning systems that will destroy themselves.

4. Self-Organization—The power to add, change, or evolve system structure

The most stunning thing living systems and some social systems can do is to change themselves utterly by creating whole new structures and behav- iors. In biological systems that power is called evolution. In human econo- mies it’s called technical advance or social revolution. In systems lingo it’s called self-organization.

Self-organization means changing any aspect of a system lower on this list—adding completely new physical structures, such as brains or wings or computers—adding new balancing or reinforcing loops, or new rules. The ability to self-organize is the strongest form of system resilience. A system that can evolve can survive almost any change, by changing itself. The human immune system has the power to develop new responses to some kinds of insults it has never before encountered. The human brain can take in new information and pop out completely new thoughts.

The power of self-organization seems so wondrous that we tend to regard it as mysterious, miraculous, heaven sent. Economists often model tech- nology as magic—coming from nowhere, costing nothing, increasing the productivity of an economy by some steady percent each year. For centu- ries people have regarded the spectacular variety of nature with the same awe. Only a divine creator could bring forth such a creation.

Further investigation of self-organizing systems reveals that the divine creator, if there is one, does not have to produce evolutionary miracles. He, she, or it just has to write marvelously clever rules for self-organization. These rules basically govern how, where, and what the system can add onto or subtract from itself under what conditions. As hundreds of self-organiz- ing computer models have demonstrated, complex and delightful patterns can evolve from quite simple sets of rules. The genetic code within the DNA that is the basis of all biological evolution contains just four different letters, combined into words of three letters each. That pattern, and the rules for replicating and rearranging it, has been constant for something

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like three billion years, during which it has spewed out an unimaginable variety of failed and successful self-evolved creatures.

Self-organization is basically a matter of an evolutionary raw mate- rial—a highly variable stock of information from which to select possi- ble patterns—and a means for experimentation, for selecting and testing new patterns. For biological evolution, the raw material is DNA, one source of variety is spontaneous mutation, and the testing mechanism is a changing environment in which some individuals do not survive to reproduce. For technology, the raw material is the body of understand- ing science has accumulated and stored in libraries and in the brains of its practitioners. The source of variety is human creativity (whatever that is) and the selection mechanism can be whatever the market will reward, or whatever governments and foundations will fund, or whatever meets human needs.

When you understand the power of system self-organization, you begin to understand why biologists worship biodiversity even more than econo- mists worship technology. The wildly varied stock of DNA, evolved and accumulated over billions of years, is the source of evolutionary potential, just as science libraries and labs and universities where scientists are trained are the source of technological potential. Allowing species to go extinct is a systems crime, just as randomly eliminating all copies of particular science journals or particular kinds of scientists would be.

The same could be said of human cultures, of course, which are the store of behavioral repertoires, accumulated over not billions, but hundreds of thousands of years. They are a stock out of which social evolution can arise. Unfortunately, people appreciate the precious evolutionary potential of cultures even less than they understand the preciousness of every genetic variation in the world’s ground squirrels. I guess that’s because one aspect of almost every culture is the belief in the utter superiority of that culture.

Insistence on a single culture shuts down learning and cuts back resil- ience. Any system, biological, economic, or social, that gets so encrusted that it cannot self-evolve, a system that systematically scorns experimenta- tion and wipes out the raw material of innovation, is doomed over the long term on this highly variable planet.

The intervention point here is obvious, but unpopular. Encouraging variability and experimentation and diversity means “losing control.” Let a thousand fl owers bloom and anything could happen! Who wants that?

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Let’s play it safe and push this lever in the wrong direction by wiping out biological, cultural, social, and market diversity!

3. Goals—The purpose or function of the system

Right there, the diversity-destroying consequence of the push for control demonstrates why the goal of a system is a leverage point superior to the self-organizing ability of a system. If the goal is to bring more and more of the world under the control of one particular central planning system (the empire of Genghis Khan, the Church, the People’s Republic of China, Wal-Mart, Disney), then everything further down the list, physical stocks and fl ows, feedback loops, information fl ows, even self-organizing behav- ior, will be twisted to conform to that goal.

That’s why I can’t get into arguments about whether genetic engineer- ing is a “good” or a “bad” thing. Like all technologies, it depends on who is wielding it, with what goal. The only thing one can say is that if corpora- tions wield it for the purpose of generating marketable products, that is a very different goal, a very different selection mechanism, a very different direction for evolution than anything the planet has seen so far.

As my little single-loop examples have shown, most balancing feedback loops within systems have their own goals—to keep the bathwater at the right level, to keep the room temperature comfortable, to keep inventories stocked at suffi cient levels, to keep enough water behind the dam. Those goals are important leverage points for pieces of systems, and most people realize that. If you want the room warmer, you know the thermostat setting is the place to intervene. But there are larger, less obvious, higher-leverage goals, those of the entire system.

Even people within systems don’t often recognize what whole-system goal they are serving. “To make profi ts,” most corporations would say, but that’s just a rule, a necessary condition to stay in the game. What is the point of the game? To grow, to increase market share, to bring the world (custom- ers, suppliers, regulators) more and more under the control of the corpo- ration, so that its operations becomes ever more shielded from uncertainty. John Kenneth Galbraith recognized that corporate goal—to engulf every- thing—long ago.6 It’s the goal of a cancer too. Actually it’s the goal of every living population—and only a bad one when it isn’t balanced by higher-

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level balancing feedback loops that never let an upstart power-loop-driven entity control the world. The goal of keeping the market competitive has to trump the goal of each individual corporation to eliminate its competitors, just as in ecosystems, the goal of keeping populations in balance and evolv- ing has to trump the goal of each population to reproduce without limit.

I said a while back that changing the players in the system is a low-level intervention, as long as the players fi t into the same old system. The excep- tion to that rule is at the top, where a single player can have the power to change the system’s goal. I have watched in wonder as—only very occa- sionally—a new leader in an organization, from Dartmouth College to Nazi Germany, comes in, enunciates a new goal, and swings hundreds or thousands or millions of perfectly intelligent, rational people off in a new direction.

That’s what Ronald Reagan did, and we watched it happen. Not long before he came to offi ce, a president could say “Ask not what government can do for you, ask what you can do for the government,” and no one even laughed. Reagan said over and over, the goal is not to get the people to help the government and not to get government to help the people, but to get government off our backs. One can argue, and I would, that larger system changes and the rise of corporate power over government let him get away with that. But the thoroughness with which the public discourse in the United States and even the world has been changed since Reagan is testi- mony to the high leverage of articulating, meaning, repeating, standing up for, insisting upon, new system goals.

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

Another of Jay Forrester’s famous systems sayings goes: It doesn’t matter how the tax law of a country is written. There is a shared idea in the minds of the society about what a “fair” distribution of the tax load is. Whatever the laws say, by fair means or foul, by complications, cheating, exemptions or deductions, by constant sniping at the rules, actual tax payments will push right up against the accepted idea of “fairness.”

The shared idea in the minds of society, the great big unstated assump- tions, constitute that society’s paradigm, or deepest set of beliefs about

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how the world works. These beliefs are unstated because it is unnecessary to state them—everyone already knows them. Money measures something real and has real meaning; therefore, people who are paid less are literally worth less. Growth is good. Nature is a stock of resources to be converted to human purposes. Evolution stopped with the emergence of Homo sapi- ens. One can “own” land. Those are just a few of the paradigmatic assump- tions of our current culture, all of which have utterly dumbfounded other cultures, who thought them not the least bit obvious.

Paradigms are the sources of systems. From them, from shared social agreements about the nature of reality, come system goals and information fl ows, feedbacks, stocks, fl ows, and everything else about systems. No one has ever said that better than Ralph Waldo Emerson:

Every nation and every man instantly surround themselves with a material apparatus which exactly corresponds to . . . their state of thought. Observe how every truth and every error, each a thought of some man’s mind, clothes itself with societ- ies, houses, cities, language, ceremonies, newspapers. Observe the ideas of the present day . . . see how timber, brick, lime, and stone have fl own into convenient shape, obedient to the master idea reigning in the minds of many persons. . . . It follows, of course, that the least enlargement of ideas . . . would cause the most striking changes of external things.7

The ancient Egyptians built pyramids because they believed in an afterlife. We build skyscrapers because we believe that space in downtown cities is enormously valuable. Whether it was Copernicus and Kepler showing that the earth is not the center of the universe, or Einstein hypothesizing that matter and energy are interchangeable, or Adam Smith postulating that the selfi sh actions of individual players in markets wonderfully accumulate to the common good, people who have managed to intervene in systems at the level of paradigm have hit a leverage point that totally transforms systems.

You could say paradigms are harder to change than anything else about a system, and therefore this item should be lowest on the list, not second- to-highest. But there’s nothing physical or expensive or even slow in the process of paradigm change. In a single individual it can happen in a

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millisecond. All it takes is a click in the mind, a falling of scales from the eyes, a new way of seeing. Whole societies are another matter—they resist challenges to their paradigms harder than they resist anything else.

So how do you change paradigms? Thomas Kuhn, who wrote the semi- nal book about the great paradigm shifts of science, has a lot to say about that.8 You keep pointing at the anomalies and failures in the old paradigm. You keep speaking and acting, loudly and with assurance, from the new one. You insert people with the new paradigm in places of public visibility and power. You don’t waste time with reactionaries; rather, you work with active change agents and with the vast middle ground of people who are open-minded.

Systems modelers say that we change paradigms by building a model of the system, which takes us outside the system and forces us to see it whole. I say that because my own paradigms have been changed that way.

1. Transcending Paradigms

There is yet one leverage point that is even higher than changing a para- digm. That is to keep oneself unattached in the arena of paradigms, to stay fl exible, to realize that no paradigm is “true,” that every one, including the one that sweetly shapes your own worldview, is a tremendously limited understanding of an immense and amazing universe that is far beyond human comprehension. It is to “get” at a gut level the paradigm that there are paradigms, and to see that that itself is a paradigm, and to regard that whole realization as devastatingly funny. It is to let go into not-knowing, into what the Buddhists call enlightenment.

People who cling to paradigms (which means just about all of us) take one look at the spacious possibility that everything they think is guaran- teed to be nonsense and pedal rapidly in the opposite direction. Surely there is no power, no control, no understanding, not even a reason for being, much less acting, embodied in the notion that there is no certainty in any worldview. But, in fact, everyone who has managed to entertain that idea, for a moment or for a lifetime, has found it to be the basis for radical empowerment. If no paradigm is right, you can choose whatever one will help to achieve your purpose. If you have no idea where to get a purpose, you can listen to the universe.

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It is in this space of mastery over paradigms that people throw off addic- tions, live in constant joy, bring down empires, get locked up or burned at the stake or crucifi ed or shot, and have impacts that last for millennia.

There is so much that could be said to qualify this list of places to intervene in a system. It is a tentative list and its order is slithery. There are exceptions to every item that can move it up or down the order of leverage. Having had the list percolating in my subconscious for years has not transformed me into Superwoman. The higher the leverage point, the more the system will resist changing it—that’s why societies often rub out truly enlightened beings.

Magical leverage points are not easily accessible, even if we know where they are and which direction to push on them. There are no cheap tickets to mastery. You have to work hard at it, whether that means rigorously analyzing a system or rigorously casting off your own paradigms and throwing yourself into the humility of not-knowing. In the end, it seems that mastery has less to do with pushing leverage points than it does with strategically, profoundly, madly, letting go and dancing with the system.

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Appendix

System Defi nitions: 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 infl ows and outfl ows. This is possible only when all infl ows equal all outfl ows.

Dynamics: The behavior over time of a system or any of its components. Feedback loop: The mechanism (rule or information fl ow or signal) that

allows a change in a stock to affect a fl ow into or out of that same stock. A closed chain of causal connections from a stock, through a set of deci- sions and actions dependent on the level of the stock, and back again through a fl ow 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 activ- ity 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 direc- tion of change. These are vicious cycles and virtuous circles.

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188 APPENDIX

Resilience: The ability of a system to recover from perturbation; the abil- ity 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 domi- nating at the expense of the total system’s goals.

System: A set of elements or parts that is coherently organized and inter- connected in a pattern or structure that produces a characteristic set of behaviors, often classifi ed 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

fl ow 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 fl ows within

the system. • If the sum of infl ows exceeds the sum of outfl ows, the stock

level will rise. • If the sum of outfl ows exceeds the sum of infl ows, the stock

level will fall. • If the sum of outfl ows equals the sum of infl ows, the stock

level will not change — it will be held in dynamic equilibrium. • A stock can be increased by decreasing its outfl ow rate as well

as by increasing its infl ow rate.

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APPENDIX 189

• Stocks act as delays or buffers or shock absorbers in systems. • Stocks allow infl ows and outfl ows to be de-coupled and inde-

pendent.

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 fl ow 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 infl ow- ing 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 fi rst 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.

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190 APPENDIX

• 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 fi nite environment.

• Nonrenewable resources are stock-limited. • Renewable resources are fl ow-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.

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APPENDIX 191

• 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 effec- tive, 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 redefi nitions 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 benefi ts

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 infl uenced by past perfor-

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192 APPENDIX

mance, especially if there is a negative bias in perceiving past performance, sets up a reinforcing feedback loop of eroding goals that sets a system drift- ing 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 feed- back loop carrying the system into an arms race, a wealth race, a smear campaign, escalating loudness, escalating violence. The escalation is expo- nential 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: Diversifi cation, 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 fi eld, removing some of the advantage of the stron- gest 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 solu-

tion 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

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APPENDIX 193

drug of choice interferes with the actions that could solve the real prob- lem.

If the intervention designed to correct the problem causes the self-main- taining capacity of the original system to atrophy or erode, then a destruc- tive 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 behav-

ior 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 defi ned 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 refl ect 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.

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194 APPENDIX

Places to Intervene in a System (in increasing order of e$ ectiveness)

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

11. Buffers: The sizes of stabilizing stocks relative to their fl ows 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 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, struc-

ture, 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 quantifi able. 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.

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APPENDIX 195

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 fi nd 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 abbrevi- ated (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) + (infl ow – outfl ow) x dt Initial stock value: water in tub = 50 gal t = minutes dt = 1 minute Run time = 10 minutes Infl ow: infl ow = 0 gal/min . . . for time 0 to 5; 5 gal/min . . . for time 6 to 10 Outfl ow: outfl ow = 5 gal/min

Coffee Cup Cooling or Warming—for Figures 10 and 11 Cooling Stock: coffee temperature(t) = coffee temperature(t – dt) – (cooling x dt)

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196 APPENDIX

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 Outfl ow: cooling = discrepancy x 10% Converters: discrepancy = coffee temperature – room temperature room temperature = 18°C

Warming Stock: coffee temperature(t) = coffee temperature(t – dt) + (heating x 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 Infl ow: heating = discrepancy x 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) + (inter-

est added x dt) Initial stock value: money in bank account = $100 t = years dt = 1 year Run time = 12 years Infl ow: interest added ($/year) = money in bank account x interest rate Converter: interest rate = 2%, 4%, 6%, 8%, & 10% annual interest . . . for

fi ve comparative model runs.

Chapter Two

Thermostat—For Figures 14-20 Stock: room temperature(t) = room temperature(t – dt) + (heat from

furnace – heat to outside) x dt

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APPENDIX 197

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 Infl ow: heat from furnace = minimum of discrepancy between desired and

actual room temperature or 5 Outfl ow: heat to outside = discrepancy between inside and outside tempera-

ture x 10% . . . for “normal” house; discrepancy between inside and outside temperature x 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) x dt Initial stock value: population = 6.6 billion people t = years

20

15

10

5

0

-5 0 6 12 18 24

te m

pe rat

ur e º

C

hour

68

59

50

41

32

23

te m

pe rat

ur e º

F

Outside temperature over 24-hour cycle

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198 APPENDIX

dt = 1 year Run time = 100 years Infl ow: births = population x fertility Outfl ow: deaths = population x 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

0.025

0.020

0.015

0.010

0.005

0 2000 2020 2040 2060 2080 2100 2120

Fertility for Figure 24

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APPENDIX 199

Stock: capital stock(t) = capital stock(t – dt) + (investment – depreciation) x dt Initial stock value: capital stock = 100 t = years dt = 1 year

Run time = 50 years Infl ow: investment = annual output x investment fraction Outfl ow: depreciation = capital stock / capital lifetime Converters: annual output = capital stock x 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) x dt Initial stock values: inventory of cars on the lot = 200 cars t = days dt = 1 day Run time = 100 days Infl ows: deliveries = 20 . . . for time 0 to 5; orders to factory (t – delivery

delay) . . . for time 6 to 100 Outfl ows: sales = minimum of inventory of cars on the lot or customer

demand

0.035

0.030

0.025

0.020

0.015

0.010

0.005

0.000 2000 2020 2040 2060 2080 2100 2120

Fertility for Figure 26

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200 APPENDIX

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 x 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 Non–Renewable Resource—for Figures 37–41 Stock: resource(t) = resource(t – dt) – (extraction x dt) Initial stock values: resource = 1000 . . . for Figures 38, 40, and 41; 1000,

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APPENDIX 201

2000, and 4000 . . . for three comparative model runs in Figure 39 Outfl ow: extraction = capital x yield per unit capital t = years dt = 1 year Run time = 100 years Stock: capital(t) = capital(t – dt) + (investment – depreciation) x dt Initial stock values: capital = 5 Infl ow: investment = minimum of profi t or growth goal Outfl ow: depreciation = capital / capital lifetime Converters: capital lifetime = 20 years profi t = (price x extraction) – (capital x 10%) growth goal = capital x 10% . . . for Figures 30-40; capital x 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 capi- tal 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

10

8

6

4

2

0 0 0.2 0.4 0.6 0.8 1.0

yield per unit capital

pr ice

Price relative to yield per unit of capital 1.0

0.8

0.6

0.4

0.2

0.0 0 250 500 750 1000

decreasing resource stocks

de cre

as ing

ing yi

eld

Yield e!ciency

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202 APPENDIX

Stock: resource(t) = resource(t – dt) + (regeneration – harvest) x dt Initial stock value: resource = 1000 Infl ow: regeneration = resource x regeneration rate Outfl ow: harvest = capital x yield per unit capital t = years dt = 1 year Run time = 100 years

Stock: capital(t) = capital(t – dt) + (investment – depreciation) x dt Initial stock value: capital = 5 Infl ow: investment = minimum of profi t or growth goal Outfl ow: depreciation = capital / capital lifetime

Converters: capital lifetime = 20 growth goal = capital x 10% profi t = (price x 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 non-linear 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

10

5

0 0.00 0.25 0.50 0.75 1.00

yield per unit capital

pr ice

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APPENDIX 203

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 effi cient in Figure 43, to slightly more effi - cient in Figure 44, to most effi cient in Figure 45.

1.00

0.75

0.50

0.25

0.00 0 500 1000

resource

re ge

ne rat

ion ra

te

1.00

0.75

0.50

0.25

0.00 0 500 1000

resource

yie ld

pe r u

nit ca

pit al

Fig. 43 Fig. 44

Fig. 45

increasing e!ciency

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Notes

Introduction 1. Russell Ackoff, “The Future of Operational Research Is Past,” Journal of the Operational

Research Society 30, no. 2 (February 1979): 93–104. 2. Idries Shah, Tales of the Dervishes (New York: E. P. Dutton, 1970), 25.

Chapter One 1. Poul Anderson, quoted in Arthur Koestler, The Ghost in the Machine (New York:

Macmillan, 1968), 59. 2. Ramon Margalef, “Perspectives in Ecological Theory,” Co-Evolution Quarterly

(Summer 1975), 49. 3. Jay W. Forrester, Industrial Dynamics (Cambridge, MA: The MIT Press, 1961), 15. 4. Honoré Balzac, quoted in George P. Richardson, Feedback Thought in Social Science

and Systems Theory (Philadelphia: University of Pennsylvania Press, 1991), 54. 5. Jan Tinbergen, quoted in ibid, 44.

Chapter Two 1. Albert Einstein, “On the Method of Theoretical Physics,” The Herbert Spencer Lecture,

delivered at Oxford (10 June 1933); also published in Philosophy of Science 1, no. 2 (April 1934): 163–69.

2. The concept of a “systems zoo” was invented by Prof. Hartmut Bossel of the University of Kassel in Germany. His three recent “System Zoo” books contain system descriptions and simulation-model documentations of more than 100 “animals,” some of which are included in modifi ed form here. Hartmut Bossel, 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).

3. For a more complete model, see the chapter “Population Sector” in Dennis L. Meadows et al., Dynamics of Growth in a Finite World, (Cambridge MA: Wright-Allen Press, 1974).

4. For an example, see Chapter 2 in Donella Meadows, Jørgen Randers, and Dennis Meadows, Limits to Growth: The 30-Year Update (White River Junction, VT: Chelsea Green Publishing Co., 2004).

5. Jay W. Forrester, 1989. “The System Dynamics National Model: Macrobehavior from Microstructure,” in P. M. Milling and E. O. K. Zahn, eds., Computer-Based Management of Complex Systems: International System Dynamics Conference (Berlin: Springer-Verlag, 1989).

TIS final pgs 204TIS final pgs 204 5/2/09 10:37:435/2/09 10:37:43

NOTES 205

Chapter Three 1. Aldo Leopold, Round River (New York: Oxford University Press, 1993). 2. C. S. Holling, ed., Adaptive Environmental Assessment and Management, (Chichester

UK: John Wiley & Sons, 1978), 34. 3. Ludwig von Bertalanffy, Problems of Life: An Evaluation of Modern Biological Thought

(New York: John Wiley & Sons Inc., 1952), 105. 4. Jonathan Swift, “Poetry, a Rhapsody, 1733.” In The Poetical Works of Jonathan Swift

(Boston: Little Brown & Co.,1959). 5. Paraphrased from Herbert Simon, The Sciences of the Artifi cial (Cambridge MA: MIT

Press, 1969), 90–91 and 98–99.

Chapter Four 1. Wendell Berry, Standing by Words (Washington, DC: Shoemaker & Hoard, 2005), 65. 2. Kenneth Boulding, “General Systems as a Point of View,” in Mihajlo D. Mesarovic,

ed., Views on General Systems Theory, proceedings of the Second Systems Symposium, Case Institute of Technology, Cleveland, April 1963 (New York: John Wiley & Sons, 1964).

3. James Gleick, Chaos: Making a New Science (New York: Viking, 1987), 23–24. 4. This story is compiled from the following sources: C. S. Holling, “The Curious

Behavior of Complex Systems: Lessons from Ecology,” in H. A. Linstone, Future Research (Reading, MA: Addison-Wesley, 1977); B. A. Montgomery et al., The Spruce Budworm Handbook, Michigan Cooperative Forest Pest Management Program, Handbook 82-7, November, 1982; The Research News, University of Michigan, April- June, 1984; Kari Lie, “The Spruce Budworm Controversy in New Brunswick and Nova Scotia,” Alternatives 10, no. 10 (Spring 1980), 5; R. F. Morris, “The Dynamics of Epidemic Spruce Budworm Populations,” Entomological Society of Canada, no. 31, (1963).

5. Garrett Hardin, “The Cybernetics of Competition: A Biologist’s View of Society,” Perspectives in Biology and Medicine 7, no. 1 (1963): 58-84.

6. Jay W. Forrester, Urban Dynamics (Cambridge, MA: The MIT Press, 1969), 117. 7. Václav Havel, from a speech to the Institute of France, quoted in the International

Herald Tribune, November 13, 1992, p. 7. 8. Dennis L. Meadows, Dynamics of Commodity Production Cycles, (Cambridge MA:

Wright-Allen Press, Inc., 1970). 9. Adam Smith, An Inquiry into the Nature and Causes of the Wealth of Nations, Edwin

Cannan, ed., (Chicago: University of Chicago Press, 1976), 477-8. 10. Herman Daly, ed., Toward a Steady-State Economy (San Francisco: W. H. Freeman and

Co., 1973), 17; Herbert Simon, “Theories of Bounded Rationality,” in R. Radner and C. B. McGuire, eds., Decision and Organization (Amsterdam: North-Holland Pub. Co., 1972).

11. The term “satisfi cing” (a merging of “satisfy” and “suffi ce”) was fi rst used by Herbert Simon to describe the behavior of making decisions that meet needs adequately, rather than trying to maximize outcomes in the face of imperfect information. H. Simon, Models of Man, (New York: Wiley, 1957).

12. Philip G. Zimbardo, “On the Ethics of Intervention in Human Psychological Research: With Special Reference to the Stanford Prison Experiment,” Cognition 2, no. 2 (1973): 243–56)

13. This story was told to me during a conference in Kollekolle, Denmark, in 1973.

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206 NOTES

Chapter Five 1. Paraphrased in an interview by Barry James, “Voltaire’s Legacy: The Cult of the

Systems Man,” International Herald Tribune, December 16, 1992, p. 24. 2. John H. Cushman, Jr., “From Clinton, a Flyer on Corporate Jets?” International Herald

Tribune, December 15, 1992, p. 11. 3. World Bank, World Development Report 1984 (New York: Oxford University Press,

1984), 157; Petre Muresan and Ioan M. Copil, “Romania,” in B. Berelson, ed., Population Policy in Developed Countries (New York: McGraw-Hill Book Company, 1974), 355-84.

4. Alva Myrdal, Nation and Family (Cambridge, MA: MIT Press, 1968). Original edition published New York: Harper & Brothers, 1941.

5. “Germans Lose Ground on Asylum Pact,” International Herald Tribune, December 15, 1992, p. 5.

6. Garrett Hardin, “The Tragedy of the Commons,” Science 162, no. 3859 (13 December 1968): 1243–48.

7. Erik Ipsen, “Britain on the Skids: A Malaise at the Top,” International Herald Tribune, December 15, 1992, p. 1.

8. Clyde Haberman, “Israeli Soldier Kidnapped by Islamic Extremists,” International Herald Tribune, December 14, 1992, p. 1.

9. Sylvia Nasar, “Clinton Tax Plan Meets Math,” International Herald Tribune, December 14, 1992, p. 15.

10. See Jonathan Kozol, Savage Inequalities: Children in America’s Schools (New York: Crown Publishers, 1991).

11. Quoted in Thomas L. Friedman, “Bill Clinton Live: Not Just a Talk Show,” International Herald Tribune, December 16, 1992, p. 6.

12. Keith B. Richburg, “Addiction, Somali-Style, Worries Marines,” International Herald Tribune, December 15, 1992, p. 2.

13. Calvin and Hobbes comic strip, International Herald Tribune, December 18, 1992, p. 22.

14. Wouter Tims, “Food, Agriculture, and Systems Analysis,” Options, International Institute of Applied Systems Analysis Laxenburg, Austria no. 2 (1984), 16.

15. “Tokyo Cuts Outlook on Growth to 1.6%,” International Herald Tribune, December 19-20, 1992, p. 11.

16. Robert F. Kennedy address, University of Kansas, Lawrence, Kansas, March 18, 1968. Available from the JFK Library On-Line, http://www.jfklibrary. org/Historical+Resources/Archives/Reference+Desk/Speeches/RFK/ RFKSpeech68Mar18UKansas.htm. Accessed 6/11/08.

17. Wendell Berry, Home Economics (San Francisco: North Point Press, 1987), 133.

Chapter Six 1. Lawrence Malkin, “IBM Slashes Spending for Research in New Cutback,” International

Herald Tribune, December 16, 1992, p. 1. 2. J. W. Forrester, World Dynamics (Cambridge MA: Wright-Allen Press, 1971). 3. Forrester, Urban Dynamics (Cambridge, MA: The MIT Press, 1969), 65. 4. Thanks to David Holmstrom of Santiago, Chile. 5. For an example, see Dennis Meadows’s model of commodity price fl uctuations:

Dennis L. Meadows, Dynamics of Commodity Production Cycles (Cambridge, MA: Wright-Allen Press, Inc., 1970).

6. John Kenneth Galbraith, The New Industrial State (Boston: Houghton Miffl in, 1967).

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NOTES 207

7. Ralph Waldo Emerson, “War,” lecture delivered in Boston, March, 1838. Reprinted in Emerson’s Complete Works, vol. XI, (Boston: Houghton, Miffl in & Co., 1887), 177.

8. Thomas Kuhn, The Structure of Scientifi c Revolutions (Chicago: University of Chicago Press, 1962).

Chapter Seven 1. G.K. Chesterton, Orthodoxy (New York: Dodd, Mead and Co., 1927). 2. For a beautiful example of how systems thinking and other human qualities can be

combined in the context of corporate management, see Peter Senge’s book The Fifth Discipline: The Art and Practice of the Learning Organization (New York: Doubleday, 1990).

3. Philip Abelson, “Major Changes in the Chemical Industry,” Science 255, no. 5051 (20 March 1992), 1489.

4. Fred Kofman, “Double-Loop Accounting: A Language for the Learning Organization,” The Systems Thinker 3, no. 1 (February 1992).

5. Wendell Berry, Standing by Words (San Francisco: North Point Press, 1983), 24, 52. 6. This story was told to me by Ed Roberts of Pugh-Roberts Associates. 7. Garrett Hardin, Exploring New Ethics for Survival: the Voyage of the Spaceship Beagle

(New York, Penguin Books, 1976), 107. 8. Donald N. Michael, “Competences and Compassion in an Age of Uncertainty,” World

Future Society Bulletin (January/February 1983). 9. Donald N. Michael quoted in H. A. Linstone and W. H. C. Simmonds. eds., Futures

Research (Reading, MA: Addison-Wesley, 1977), 98–99. 10. Aldo Leopold, A Sand County Almanac and Sketches Here and There (New York:

Oxford University Press, 1968), 224–25. 11. Kenneth Boulding, “The Economics of the Coming Spaceship Earth,” in H. Jarrett, ed.,

Environmental Quality in a Growing Economy: Essays from the Sixth Resources for the Future Forum (Baltimore, MD: Johns Hopkins University Press, 1966), 11-12.

12. Joseph Wood Krutch, Human Nature and the Human Condition (New York: Random House, 1959).

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Bibliography of Systems Resources

In addition to the works cited in the Notes, the items listed here are jump- ing off points—places to start your search for more ways to see and learn about systems. The fi elds 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 fi elds 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).

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BIBLIOGRAPHY 209

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 model- ing 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, educa- tors, consultants, and practitioners dedicated to the development and use of systems thinking and system dynamics around the world. The Systems Dynamics Review is the offi cial 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 busi- ness environment, and also the broader philosophical tools that arise from and complement systems thinking, such as mental-model fl exibil- ity 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).

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210 BIBLIOGRAPHY

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).

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