Thinking in systems: a primer pdf download

There is much, much more to systems thinking than is presented here, for you to discover if you are interested. One of my purposes is to make you interested. Another of my purposes, the main one, is to give you a basic ability to understand and to deal with complex systems, even if your formal systems training begins and ends with this book. In , Donella Dana Meadows completed a draft of the book you now hold. The manuscript was not published at the time, but circulated informally for years.

Dana died quite unexpectedly in —before she completed this book. In the years since her death, it became clear that her writings have continued to be useful to a wide range of readers. Dana was a scientist and writer, and one of the best communicators in the world of systems modeling. In , Dana was lead author of The Limits to Growth —a best-selling and widely translated book. The cautions she and her fellow authors issued then are recognized today as the most accurate warnings of how unsustainable patterns could, if unchecked, wreak havoc across the globe.

That book made headlines around the world for its observations that continual growth in population and consumption could severely damage the ecosystems and social systems that support life on earth, and that a drive for limitless economic growth could eventually disrupt many local, regional, and global systems.

The findings in that book and its updates are, once again, making front-page news as we reach peak oil, face the realities of climate change, and watch a world of 6. In short, Dana helped usher in the notion that we have to make a major shift in the way we view the world and its systems in order to correct our course.

Today, it is widely accepted that systems thinking is a critical tool in addressing the many environmental, political, social, and economic challenges we face around the world. Systems, big or small, can behave in similar ways, and understanding those ways is perhaps our best hope for making lasting change on many levels.

Dana was writing this book to bring that concept to a wider audience, and that is why I and my colleagues at the Sustainability Institute decided it was time to publish her manuscript posthumously.

Will another book really help the world and help you, the reader? I think so. Perhaps you are working in a company or own a company and are struggling to see how your business or organization can be part of a shift toward a better world.

Does the size of the population affect those economic and environmental and social factors? Of course, the answer to all of these questions is yes. Fertility and mortality are governed by feedback loops too. At least some of those feedback loops are themselves affected by the size of the population.

At the heart of the economy is another reinforcing-loop-plusbalancing-loop system—the same kind of structure, with the same kinds investment depreciation capital stock B R investment fraction capital lifetime annual output output per unit capital Figure Like a living population, economic capital has a reinforcing loop investment of output governing growth and a balancing loop depreciation governing decline.

The greater the stock of physical capital machines and factories in the economy and the efficiency of production output per unit of capital , the more output goods and services can be produced each year. The more output that is produced, the more can be invested to make new capital. This is a reinforcing loop, like the birth loop for a population. The investment fraction is equivalent to the fertility.

The greater the fraction of its output a society invests, the faster its capital stock will grow. Physical capital is drained by depreciation—obsolescence and wearingout. The balancing loop controlling depreciation is equivalent to the death loop in a population. The longer the lifetime, the smaller the fraction of capital that must be retired and replaced each year.

If this system has the same structure as the population system, it must have the same repertoire of behaviors. Over recent history world capital, like world population, has been dominated by its reinforcing loop and has been growing exponentially.

Whether in the future it grows or stays constant or dies off depends on whether its reinforcing growth loop remains stronger than its balancing depreciation loop. If a constant fraction of output is reinvested in the capital stock and the efficiency of that capital its ability to produce output is also constant, the capital stock may decline, stay constant, or grow, depending on the lifetime of the capital. The lines in Figure 28 show systems with different average capital lifetimes. With a relatively short lifetime, the capital wears out faster than it is replaced.

Reinvestment does not keep up with depreciation and the economy slowly declines. When depreciation just balances investment, the economy is in dynamic equilibrium. With a long lifetime, the capital stock grows exponentially. The longer the lifetime of capital, the faster it grows. Growth in capital stock with changes in the lifetime of the capital.

In a system with output per unit capital ratio of and an investment fraction of 20 percent, capital with a lifetime of 15 years just keeps up with depreciation. A shorter lifetime leads to a declining stock of capital. Just as many factors influence the fertility and mortality of a population, so many factors influence the output ratio, investment fraction, and the lifetime of capital—interest rates, technology, tax policy, consumption habits, and prices, to name just a few.

Population itself influences investment, both by contributing labor to output, and by increasing demands on consumption, thereby decreasing the investment fraction. Economic output also feeds back to influence population in many ways.

A richer economy usually has better health care and a lower death rate. A richer economy also usually has a lower birth rate. In fact, just about any long-term model of a real economy should link together the two structures of population and capital to show how they affect each other. The central question of economic development is how to keep the reinforcing loop of capital accumulation from growing more slowly than the reinforcing loop of population growth—so that people are getting richer instead of poorer.

But from a systems point of view these systems, so dissimilar in many ways, have one important thing in common: their feedback-loop structures. Both have a stock governed by a reinforcing growth loop and a balancing death loop. Both also have an aging process. Steel mills and lathes and turbines get older and die just as people do.

One of the central insights of systems theory, as central as the observation that systems largely cause their own behavior, is that systems with similar feedback structures produce similar dynamic behaviors, even if the outward appearance of these systems is completely dissimilar. A population is nothing like an industrial economy, except that both can reproduce themselves out of themselves and thus grow exponentially.

And both age and die. A coffee cup cooling is like a warmed room cooling, and like a radioactive substance decaying, and like a population or industrial economy aging and dying. Each declines as the result of a balancing feedback loop.

A System with Delays—Business Inventory Picture a stock of inventory in a store—a car dealership—with an inflow of deliveries from factories and an outflow of new car sales.

By itself, this stock of cars on the dealership lot would behave like the water in a bathtub. Inventory at a car dealership is kept steady by two competing balancing loops, one through sales and one through deliveries.

Customers make purchases that are unpredictable on a day-to-day basis. The car dealer also needs to provide herself with some extra inventory a buffer in case deliveries from suppliers are delayed occasionally. So, higher sales mean higher perceived sales, which means a higher discrepancy between inventory and desired inventory, which means higher orders, which will bring in more deliveries, which will raise inventory so it can comfortably supply the higher rate of sales.

This system is a version of the thermostat system—one balancing loop of sales draining the inventory stock and a competing balancing loop maintaining the inventory by resupplying what is lost in sales.

Figure 30 shows the not very surprising result of an increase in consumer demand of 10 percent. In Figure 31, I am putting something else into this simple model—three delays that are typical of what we experience in the real world.

First, there is a perception delay, intentional in this case. Before she makes ordering decisions, cars on the lot 0 0 10 20 30 40 50 days 60 70 80 90 Figure Inventory at a car dealership with three common delays now included in the picture—a perception delay, a response delay, and a delivery delay.

Second, there is a response delay. Rather, she makes up one-third of any shortfall with each order. Another way of saying that is, she makes partial adjustments over three days to be extra sure the trend is real. Third, there is a delivery delay. It takes five days for the supplier at the factory to receive an order, process it, and cars on the lot 0 0 10 20 30 40 50 days 60 70 80 90 Figure Response of inventory to a percent increase in sales when there are delays in the system.

Look at what happens, for example, as shown in Figure 32, when the business experiences the same permanent percent jump in sales from an increase in customer demand. A single step up in sales causes inventory to drop. The car dealer watches long enough to be sure the higher sales rate is going to last.

Then she begins to order more cars to both cover the new rate of sales and bring the inventory up. But it takes time for the orders to come in. Eventually, the larger volume of orders starts arriving, and inventory recovers—and more than recovers, because during the time of uncertainty about the actual trend, the owner has ordered too much. She now sees her mistake, and cuts back, but there are still high past orders coming in, so she orders even less.

Inventory gets too low again. And so forth, through a series of oscillations around the new desired inventory level. As Figure 33 illustrates, what a difference a few delays make! This situation of information insufficiency and physical delays is very common. Oscillations like these are frequently encountered in inventories and in many other systems. The response of orders and deliveries to an increase in demand.

B shows the resulting ordering pattern, tracked by the actual deliveries from the factory. I can use this inventory system to show you why. Not much happens when the car dealer shortens her perception delay. If anything the oscillations in the inventory of cars on the lot are a bit worse. And if, instead of shortening her perception time, the car dealer tries shortening her reaction time—making up perceived shortfalls in two days instead of three—things get very much worse, as shown in Figure Something has to change and, since this system has a learning person cars 0 0 10 20 30 40 50 days 60 70 80 90 Figure The response of inventory to the same increase in demand with a shortened perception delay.

The response of inventory to the same increase in demand with a shortened reaction time. Acting faster makes the oscillations worse! This perverse kind of result can be seen all the time—someone trying to fix a system is attracted intuitively to a policy lever that in fact does have a strong effect on the system.

And then the well-intentioned fixer pulls the lever in the wrong direction! This is just one example of how we can be surprised by the counterintuitive behavior of systems when we start trying to change them. Part of the problem here is that the car dealer has been reacting not too slowly, but too quickly.

Given the configuration of this system, she has been overreacting. Things would go better if, instead of decreasing her response delay from three days to two, she would increase the delay from three days to six, as illustrated in Delays are pervasive in systems, Figure Changing damped with this change, and the system finds the length of a delay may or may not, depending on the its new equilibrium fairly efficiently.

The most important delay in this system is type of delay and the relative the one that is not under the direct control of lengths of other delays make a large change in the behavthe car dealer. But even without the ability to change that part of her system, the dealer can learn to manage inventory quite well. The response of inventory to the same increase in demand with a slowed reaction time. You can see why system thinkers are somewhat fanatic on the subject of delays.

And we are aware that some delays can be powerful policy levers. Lengthening or shortening them can produce major changes in the behavior of systems. But imagine that the inventory is that of all the unsold automobiles in America. Orders for more or fewer cars affect production not only at assembly plants and parts factories, but also at steel mills, rubber and glass plants, textile producers, and energy producers.

Everywhere in this system are perception delays, production delays, delivery delays, and construction delays. Now consider the link between car production and jobs—increased production increases the number of jobs allowing more people to buy cars. Put in another reinforcing loop, as speculators buy and sell shares in the auto and autosupply companies based on their recent performance, so that an upsurge in production produces an upsurge in stock price, and vice versa.

That very large system, with interconnected industries responding to each other through delays, entraining each other in their oscillations, and being amplified by multipliers and speculators, is the primary cause of business cycles. Economies are extremely complex systems; they are full of balancing feedback loops with delays, and they are inherently oscillatory.

The thermostat-furnace system never ran out of oil. But any real physical entity is always surrounded by and exchanging things with its environment. A corporation needs a constant supply of energy and materials and workers and managers and customers.

A growing corn crop needs water and nutrients and protection from pests. Any entity that is using energy and processing materials needs a place to put its wastes, or a process to carry its wastes away. Therefore, any physical, growing system is going to run into some kind of constraint, sooner or later.

That constraint will take the form of a balancing loop that in some way shifts the dominance of the reinforcing loop driving the growth behavior, either by strengthening the outflow or by weakening the inflow.

Whenever we see a growing entity, whether it be a population, a corporation, a bank account, a rumor, an epidemic, or sales of a new product, we look for the reinforcing loops that are driving it and for the balancing loops that ultiIn physical, exponentially mately will constrain it.

Even a hot new product growth and at least one will saturate the market eventually. A chain reac- balancing loop constraintion in a nuclear power plant or bomb will run out ing the growth, because of fuel. A virus will run out of susceptible people to no physical system can infect. Like resources that supply the inflows to a stock, a pollution constraint can be renewable or nonrenewable. The limits on a growing system may be temporary or permanent.

The system may find ways to get around them for a short while or a long while, but eventually there must come some kind of accommodation, the system adjusting to the constraint, or the constraint to the system, or both to each other.

In that accommodation come some interesting dynamics. Whether the constraining balancing loops originate from a renewable or nonrenewable resource makes some difference, not in whether growth can continue forever, but in how growth is likely to end. See Figure Economic capital, with its reinforcing growth loop constrained by a nonrenewable resource.

It builds itself up through investment of profits from oil extraction. So we see the reinforcing loop: More capital allows more resource extraction, creating more profits that can be reinvested.

Profit is income minus cost. Income in this simple representation is just the price of oil times the amount of oil the company extracts. Cost is equal to capital times the operating cost energy, labor, materials, etc. What is not assumed to be constant is the yield of resource per unit of capital. Because this resource is not renewable, as in the case of oil, the stock feeding the extraction flow does not have an input. As the resource is extracted—as an oil well is depleted—the next barrel of oil becomes harder to get.

The remaining resource is deeper down, or more dilute, or in the case of oil, under less natural pressure to force it to the surface. More and more costly and technically sophisticated measures are required to keep the resource coming. Here is a new balancing feedback loop that ultimately will control the growth of capital: the more capital, the higher the extraction rate. The higher the extraction rate, the lower the resource stock. The lower the resource stock, the lower the yield of resource per unit of capital, so the lower the profit with price assumed constant and the lower the investment rate—therefore, the lower the rate of growth of capital.

I could assume that resource depletion feeds back through operating cost as well as capital efficiency. In the real world it does both. In either case, the ensuing behavior pattern is the same—the classic dynamics of depletion see Figure The system starts out with enough oil in the underground deposit to supply the initial scale of operation for years. The greater the accumulation of capital, the faster the resource is depleted. At an investment rate of 10 percent per year, the capital stock and therefore the extraction rate both grow at 5 percent per year and so double in the first 14 years.

After 28 years, while the capital stock has quadrupled, extraction is starting to lag because of falling yield per unit of capital. By year 50 the cost of maintaining the capital stock has overwhelmed the income from resource extraction, so profits are no longer sufficient to keep investment ahead of depreciation. The operation quickly shuts down, as the capital stock declines.

Of course, that makes a huge difference in the total amount of oil that can be extracted from this field. But with the continued goal of 10 percent per year reinvestment producing 5 percent per year A quantity growing exponentially toward capital growth, each doubling of the resource makes a a constraint or limit difference of only about 14 years in the timing of the reaches that limit in a peak extraction rate, and in the lifetime of any jobs or surprisingly short time.

In the face of exponential growth of extraction or use, a doubling or quadrupling of the nonrenewable resource give little added time to develop alternatives. If your concern is to extract the resource and make money at the maximum possible rate, then the ultimate size of the resource is the most important number in this system. Here is a good example of the goal of a feedback loop being crucial to the behavior of a system. The real choice in the management of a nonrenewable resource is whether to get rich very fast or to get less rich but stay that way longer.

Extraction with two times or four times as large a resource to draw on. Each doubling of the resource makes a difference of only about fourteen years in the peak of extraction. The graph in Figure 40 shows the development of the extraction rate over time, given desired growth rates above depreciation varying from 1 percent annually, to 3 percent, 5 percent, and 7 percent.

Imagine the effects of this choice not only on the profits of the company, but on the social and natural environments of the region. Earlier I said I would make the simplifying assumption that price was constant.

In that case, as the resource gets scarce and price rises steeply, as shown in Figure The higher price gives the industry higher profits, so investment goes up, capital stock continues rising, and the more costly remaining resources can be extracted.

If you compare Figure 41 with Figure 38, where price was held constant, you can see that the main effect of rising price is to build the capital stock higher before it collapses. The consequence is that the resource C is depleted even faster at the end. We all know that individual mines and fossil fuel deposits and groundwater aquifers can be depleted. Resource companies understand this dynamic too. Well before depletion makes capital less efficient in one place, companies shift investment to discovery and development of another deposit somewhere else.

But, if there are local limits, eventually will there be global ones? I will just point out that, according to the dynamics of depletion, the larger the stock of initial resources, the more new discoveries, the longer the growth loops elude the control loops, and the higher the capital stock and its extraction rate grow, and the earlier, faster, and farther will be the economic fall on the back side of the production peak.

Unless, perhaps, the economy can learn to operate entirely from renewable resources. Renewable Stock Constrained by a Renewable Stock—a Fishing Economy Assume the same capital system as before, except that now there is an inflow to the resource stock, making it renewable.

The renewable resource in this system could be fish and the capital stock could be fishing boats. It also could be trees and sawmills, or pasture and cows. Living renewable resources such as fish or trees or grass can regenerate themselves from themselves with a reinforcing feedback loop.

Nonliving renewable resources such as sunlight or wind or water in a river are regenerated not through a reinforcing loop, but through a steady input that keeps refilling the resource stock no matter what the current state of that stock might be. It spares its victims who are then able to catch another cold. Sales of a product people need to buy regularly is also a renewable resource system; the stock of potential customers is ever regenerated.

Likewise an insect infestation that destroys part but not all of a plant; the plant can regenerate and the insect can eat more. In all these cases, there is an input that keeps refilling the constraining resource stock as shown in Figure We will use the example of a fishery. Once again, assume that the lifetime of capital is 20 years and the industry will grow, if it can, at 5 percent per year.

As with the nonrenewable resource, assume that as the resource gets scarce it costs more, in terms of capital, to harvest it. Bigger fishing boats that can go longer distances and are equipped with sonar are needed to find the last schools of fish. Or miles-long drift nets are needed to catch them. Or on-board refrigeration systems are needed to bring them back to port from longer distances.

All this takes more capital. The regeneration rate of the fish is not constant, but is dependent on the number of fish in the area—fish density. If the fish are very dense, their reproduction rate is near zero, limited by available food and habitat. Economic capital with its reinforcing growth loop constrained by a renewable resource. But at some point the fish reproduction rate reaches its maximum.

If the population is further depleted, it breeds not faster and faster, but slower and slower. This system can produce many different sets of behaviors. Figure 43 shows one of them. In Figure 43, we see capital and fish harvest rise exponentially at first. For decades the resource can go on supplying an exponentially increasing harvest rate. Eventually, the harvest rises too far and the fish population falls low enough to reduce the profitability of the fishing fleet.

The balancing feedback of falling harvest reducing profits brings A: Harvest rate 0 B: Capital stock 0 25 50 75 years 0 25 50 75 years 0 25 50 75 years 0 C: Resource stock 0 Figure The result of leveling harvest is that the resource stock C also stabilizes.

Just a minor change in the strength of the controlling balancing feedback loop through yield per unit of capital, however, can make a surprisA: Harvest rate 0 B: Capital stock 0 25 50 75 years 0 25 50 75 years 0 25 50 75 years 0 C: Resource stock 0 Figure A slight increase in yield per unit of capital—increasingly efficient technology in this case—creates a pattern of overshoot and oscillation around a stable value in the harvest rate A , the stock of economic capital B , and in the resource stock.

An even greater increase in yield per unit of capital creates a patterns of overshoot and collapse in the harvest A , the economic capital B , and the resource C. Suppose that in an attempt to raise the catch in the fishery, the industry comes up with a technology to improve the efficiency of the boats sonar, for example, to find the scarcer fish. Figure 44 shows another case of high leverage, wrong direction! The entire stock instability. Oscillations appear! If the fishing technology gets even better, is available at once, and can be the boats can go on operating economically extracted at any rate limited even at very low fish densities.

The result can mainly by extraction capital. But since the stock is not renewed, be a nearly complete wipeout both of the fish the faster the extraction rate, and of the fishing industry. The consequence the shorter the lifetime of the is the marine equivalent of desertification.

The fish have been turned, for all practical purposes, into a nonrenewable resource. If they population retains the potential to build its are extracted faster than they numbers back up again, once the capital driv- regenerate, they may eventually ing the harvest is gone. The whole pattern be driven below a critical threshold and become, for all practical is repeated, decades later.

Very long-term purposes, nonrenewable. But this is not true for all resource populations. More and more, increases in technology and harvest efficiency have the ability to drive resource populations to extinction. Whether a real renewable resource system can survive overharvest depends on what happens to it during the time when the resource is severely depleted. A very small fish population may become especially vulnerable to pollution or storms or lack of genetic diversity.

If this is a forest or grassland resource, the exposed soils may be vulnerable to erosion. Or the nearly empty ecological niche may be filled in by a competitor. Or perhaps the depleted resource can survive and rebuild itself again. Which outcome actually occurs depends on two things. The second is the rapidity and effectiveness of the balancing feedback loop that slows capital growth as the resource becomes depleted.

If the feedback is fast enough to stop capital growth before the critical threshold is reached, the whole system comes smoothly into equilibrium. If the balancing feedback is slower and less effective, the system oscillates. If the balancing loop is very weak, so that capital can go on growing even as the resource is reduced below its threshold ability to regenerate itself, the resource and the industry both collapse. Neither renewable nor nonrenewable limits to growth allow a physical stock to grow forever, but the constraints they impose are dynamically quite different.

The difference comes because of the difference between stocks and flows. The trick, as with all the behavioral possibilities of complex systems, is to recognize what structures contain which latent behaviors, and what conditions release those behaviors—and, where possible, to arrange the structures and conditions to reduce the probability of destructive behaviors and to encourage the possibility of beneficial ones.

If the biota, in the course of aeons, has built something we like but do not understand, then who but a fool would discard seemingly useless parts? To keep every cog and wheel is the first precaution of intelligent tinkering.

If pushed too far, systems may well fall apart or exhibit heretofore unobserved behavior. But, by and large, they manage quite well. And that is the beauty of systems: They can work so well. When systems work well, we see a kind of harmony in their functioning.

Think of a community kicking in to high gear to respond to a storm. Consider the properties of highly functional systems—machines or human communities or ecosystems—which are familiar to you.

Chances are good that you may have observed one of three characteristics: resilience, self-organization, or hierarchy. Holling,2 ecologist Resilience has many definitions, depending on the branch of engineering, ecology, or system science doing the defining.

The ability to recover strength, spirits, good humor, or any other aspect quickly. The opposite of resilience is brittleness or rigidity. Resilience arises from a rich structure of many feedback loops that can work in different ways to restore a system even after a large perturbation.

A single balancing loop brings a system stock back to its desired state. Resilience is provided by several such loops, operating through different mechanisms, at different time scales, and with redundancy—one kicking in if another one fails. A set of feedback loops that can restore or rebuild feedback loops is resilience at a still higher level—meta-resilience, if you will.

Even higher metameta-resilience comes from feedback loops that can learn, create, design, and evolve ever more complex restorative structures. Systems that can do this are self-organizing, which will be the next surprising system characteristic I come to.

The human body is an astonishing example of a resilient system. It can fend off thousands of different kinds of invaders, it can tolerate wide ranges of temperature and wide variations in food supply, it can reallocate blood supply, repair rips, gear up or slow down metaboThere are always limits to lism, and compensate to some extent for missing resilience.

Add to it a self-organizing intelligence that can learn, socialize, design technologies, and even transplant body parts, and you have a formidably resilient system—although not infinitely so, because, so far at least, no human body-plus-intelligence has been resilient enough to keep itself or any other body from eventually dying.

They can, given enough time, come up with whole new systems to take advantage of changing opportunities for life support. Resilience is not the same thing as being static or constant over time. Resilient systems can be very dynamic. Short-term oscillations, or periodic outbreaks, or long cycles of succession, climax, and collapse may in fact be the normal condition, which resilience acts to restore!

And, conversely, systems that are constant over time can be unresilient. This distinction between static stability and resilience is important. Resilience is something that may be very hard to see, unless you exceed its limits, overwhelm and damage the balancing loops, and the system structure breaks down.

Because resilience may not be obvious without a whole-system view, people often sacrifice resilience for stability, or for productivity, or for some other more immediately recognizable system property. Cattle breeding over centuries has done much the same thing but not to the same degree. The cost of increased production is lowered resilience. The cow is less healthy, less long-lived, more dependent on human management.

The just-in-time model also has made the production system more vulnerable, however, to perturbations in fuel supply, traffic flow, computer breakdown, labor availability, and other possible glitches. However, without multiple species interacting with each other and drawing and returning varying combinations of nutrients from the soil, these forests have lost their resilience. They seem to be especially vulnerable to a new form of insult: industrial air pollution.

Many chronic diseases, such as cancer and heart disease, come from breakdown of resilience mechanisms that repair DNA, keep blood vessels flexible, or control cell division. Ecological disasters in many places come from loss of resilience, as species are removed from ecosystems, soil chemistry and biology are disturbed, or toxins build up. Large organizations of all kinds, from corporations to governments, lose their resilience simply because the feedback mechanisms by which they sense and respond to their environment have to travel through too many layers of delay and distortion.

More on that in a minute, when we come to hierarchies. I think of resilience as a plateau upon which the system can play, performing its normal functions in safety. A resilient system has a big plateau, a lot of space over which it can wander, with gentle, elastic walls that will bounce it back, if it comes near a dangerous edge.

Systems need to be As a system loses its resilience, its plateau shrinks, managed not only for and its protective walls become lower and more productivity or stabil- rigid, until the system is operating on a knifeity, they also need to be edge, likely to fall off in one direction or another managed for resilience— whenever it makes a move.

Loss of resilience can the ability to recover from come as a surprise, because the system usually is perturbation, the ability to paying much more attention to its play than to its restore or repair themselves.

One day it does something it has done a hundred times before and crashes. That awareness is behind the encouragement of natural ecosystems on farms, so that predators can take on more of the job of controlling pests.

The discovery of these laws constitutes one of the most important tasks of the future. It is the ability of a single fertilized ovum to generate, out of itself, the incredible complexity of a mature frog, or chicken, or person. It is the ability of nature to have diversified millions of fantastic species out of a puddle of organic chemicals. It is the ability of a society to take the ideas of burning coal, making steam, pumping water, and specializing labor, and develop them eventually into an automobile assembly plant, a city of skyscrapers, a worldwide network of communications.

This capacity of a system to make its own structure more complex is called self-organization. You see self-organization in a small, mechanistic way whenever you see a snowflake, or ice feathers on a poorly insulated window, or a supersaturated solution suddenly forming a garden of crystals. You see self-organization in a more profound way whenever a seed sprouts, or a baby learns to speak, or a neighborhood decides to come together to oppose a toxic waste dump.

Self-organization is such a common property, particularly of living systems, that we take it for granted. Like resilience, self-organization is often sacrificed for purposes of short-term productivity and stability. Productivity and stability are the usual excuses for turning creative human beings into mechanical adjuncts to production processes.

Or for narrowing the genetic variability of crop plants. Or for establishing bureaucracies and theories of knowledge that treat people as if they were only numbers. Self-organization produces heterogeneity and unpredictability. It requires freedom and experimentation, and a certain amount of disorder. These conditions that encourage self-organization often can be scary for individuals and threatening to power structures. As a consequence, education systems may restrict the creative powers of children instead of stimulating those powers.

Economic policies may lean toward supporting established, powerful enterprises rather than upstart, new ones. And many governments prefer their people not to be too self-organizing. Fortunately, self-organization is such a basic property of living systems that even the most overbearing power structure can never fully kill it, although in the name of law and order, self-organization can be suppressed for long, barren, cruel, boring periods.

Systems theorists used to think that self-organization was such a complex property of systems that it could never be understood. New discoveries, however, suggest that just a few simple organizing principles can lead to wildly diverse self-organizing structures. Imagine a triangle with three equal sides. Add to the middle of each side another equilateral triangle, one-third the size of the first one.

Add to each of the new sides another triangle, one-third smaller. And so on. The result is called a Koch snowflake. Its edge has tremendous length—but it can be contained within a circle. This structure is one simple example of fractal geometry—a realm of mathematics and art populated by elaborate shapes formed by relatively simple rules.

Similarly, the delicate, beautiful, intricate structure of a stylized fern can be generated by a computer with just a few simple fractal rules.

The Figure It is because of fractal geometry that the average human lung has enough surface area to cover a tennis court. Out of simple rules of self-organization can grow enormous, diversifying crystals of technology, physical structures, organizations, and cultures.

Science knows now that self-organizing systems can arise from simple rules. Science, itself a self-organizing system, likes to think that all the complexity of the world must arise, ultimately, from simple rules.

Whether that actually happens is something that science does not yet know. TIS final pgs 81 Systems often have the property of self-organization—the ability to structure themselves, to create new structure, to learn, diversify, and complexify. Even complex forms of self-organization may arise from relatively simple organizing rules—or may not.

The world, or at least the parts of it humans think they understand, is organized in subsystems aggregated into larger subsystems, aggregated into still larger subsystems. A cell in your liver is a subsystem of an organ, which is a subsystem of you as an organism, and you are a subsystem of a family, an athletic team, a musical group, and so forth.

These groups are subsystems of a town or city, and then a nation, and then the whole global socioeconomic system that dwells within the biosphere system. This arrangement of systems and subsystems is called a hierarchy.

Corporate systems, military systems, ecological systems, economic systems, living organisms, are arranged in hierarchies.

It is no accident that that is so. If subsystems can largely take care of themselves, regulate themselves, maintain themselves, and yet serve the needs of the larger system, while the larger system coordinates and enhances the functioning of the subsystems, a stable, resilient, and efficient structure results.

It is hard to imagine how any other kind of arrangement could have come to be. Both of them made fine watches, and they both had many customers. People dropped into their stores, and their phones rang constantly with new orders. Donella Meadows. Thinking in Systems, is a concise and crucial book offering insight forproblem solving on scales ranging from the personal to the global.

They cannot be solved by fixing one piece in isolation from the others, because even seeminglyminor details have enormous power to undermine the best efforts of too-narrow thinking. We live in a complex world of systems. For the full details, examples and tips, do get a copy of the book , or get a detailed overview with our complete book summary bundle.

A system is a set of interlinked elements organized to achieve a goal. Every animal, plant, organization and society is a complex system. While systems may be influenced by external forces, the way they respond to these forces tend to come from their inherent characteristics. This means that the economy will move in cycles regardless of what political leaders do. It may seem ironic to warn readers — in a series of articles discussing and summarizing cases — that they should be leery of placing too much trust in articles that purport to describe cases.

Yet, this article is about precisely that. While journalists and analysts — and MSPB attorneys — writing about employment cases can serve an important function in reaching an audience and drawing their attention to issues, the information is only as reliable as the second-hand author makes it.

The best way to know for certain what a case says is to read it yourself. If that is not practical, then get your information from the most reliable source you can and be careful about assuming that a source is reliable. Developed by renowned systems thinker Peter Senge , these five disciplines each enhance the ability of a person or organization to use learning effectively. U Process, also know as Theory U, is a useful methodology for collectively approaching difficult problems and developing innovative, appropriate solutions.