Thinking in Systems is a concise and crucial book
offering insight for problem-solving on scales ranging from
the personal to the global.
This essential primer brings systems thinking out of
the realm of computers and equations and into the tangible world,
showing readers how to develop the systems-thinking skills
that thought leaders across the globe consider critical
for 21st-century life.
While readers will learn the conceptual tools and methods
of systems thinking, the heart of the book is grander than methodology,
Donella H. Meadows was known as much for nurturing positive outcomes
as she was for delving into the science behind global dilemmas.
She reminds readers to pay attention to what is important,
not just what is quantifiable, to stay humble and to continue to learn.
In a world growing ever more complicated, crowded, and interdependent,
Thinking in Systems helps readers avoid confusion and helplessness,
the first step toward finding proactive and effective solutions.
A vital read for students, professionals and all those concerned
with economics, business, sustainability and the environment.
It is strange to see a book dedicated to the author. The reason here
is that this is a collection of notes, drafts and talks made by
Meadows before she died in 2001, subsequently edited into book form by
Diana Wright. As such it is probably less detailed and elaborated than
a “real” book would have been, but it does not suffer: it is
nicely distilled.
This is an introduction to and overview of Systems Theory: ways of
thinking about complex non-linear systems infested with flows,
feedback loops and information delays. That is, any real world system
of interest.
p2.
A system is a set of things—people,
cells, molecules, or whatever—interconnected in such a way that they
produce their own pattern of behavior over time. The system may be
buffeted, constricted, triggered, or driven by outside forces. But the
system’s response to these forces is characteristic of itself, and
that response is seldom simple in the real world.
![[fig 42]](../_misc/system.jpg)
Systems
are not easy to understand with linear textual descriptions, so
Meadows introduces a diagrammatic notation to map out the
relationships and feedbacks between the stocks and flows in the
system.
p5.
… there is a problem in discussing
systems only with words. Words and sentences must, by necessity, come
only one at a time in linear, logical order. Systems happen all at
once. They are connected not just in one direction, but in many
directions simultaneously. To discuss them properly, it is necessary
somehow to use a language that shares some of the same properties as
the phenomena under discussion.
Pictures work for this language
better than words, because you can see all the parts of a picture at
once.
Back to definitions, now with a highlighting of the three main types
of components of systems:
p11.
A system isn’t just any old collection
of things. A system is an interconnected set of elements that is
coherently organized in a way that achieves something. … a system
has three aspects: elements, interconnections, and a
function or purpose.
Meadows elaborates this definition with a couple of examples, of a
digestive system and a football team, showing how they fit the
definition. She then lists a bunch of other systems, including a tree,
a forest, the earth, a galaxy. As I find all too often with examples,
the easy ones are described in detail, while the harder ones are not,
despite not fitting so obviously. For example, Meadows suggests that a
galaxy is a system: but what is the “function or purpose” of
a galaxy? Having an answer to this question would help explore the
difficult corner cases.
Not everything is a system, of course
p12.
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
football players, or pieces of your digestive system, and you quickly
no longer have the same system.
Okay, let’s look for corner cases again. Football defined with one
fewer or one more player is certainly a different game. But you can
chop bits out of your digestive system, and still have a digestive
system: it has a degree of redundancy. And what about a tree-as-system
that loses a branch? or an anthill that loses several hundred ants?
Are they still the “same” system? There seems to be a
difference between designed and evolved systems, or possibly between “fragile”
and “robust” systems. In fact, the very next paragraph goes
on to explore this robustness and integrity of systems:
p12.
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. … 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.
The anthill seems to fit this vision of system better than the
football team. Certainly, it can be the “same” team even
after all the players have changed, but there are some changes that
can’t be made (like the number of players) and leave it a football
game (well, modulo designed-in sendings-off). However, this emphasis
on interrelationships and adaptability is where we want to be: the
interrelationships hold the elements of the system together, and allow
it to adapt. How can we “engineer” artificial systems to
have these properties, how can we make existing systems adapt in
helpful directions? The questions require us to be able to understand
such systems.
This requires understanding the three main aspects, but some are
harder to identify than others:
pp12-15.
The elements of a system are often the
easiest parts to notice, because man of them are visible, tangible
things. …
… It’s easier to learn about a
system’s elements than about its interconnections.
Some interconnections in systems
are actual physical flows … Many interconnections are flows of
information—signals that go to decision points or action points
within a system. These kinds of interconnections are often harder to
see …
If information-based relationships
are hard to see, functions or purposes are even
harder. A system’s function or purpose is not necessarily ….
expressed explicitly, except through 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.
… An important function of almost
every system is to ensure its own perpetuation.
System purposes need not be human
purposes and are not necessarily those intended by any single actor
within the system. … the purposes of subunits may add up to an
overall behaviour that no one wants.
This point that purposes at one level may not support those at
another is crucial:
pp15-16.
Systems can be nested within systems.
Therefore, there can be purposes within purposes. … Any of these
sub-purposes could come into conflict with the overall purpose …
Keeping sub-purposes and overall system purposes in harmony is an
essential feature of successful systems.
Of the three main aspects, some are more crucial to the system-ness
than others. Ironically, the ones easiest to identify are the ones
that have the least effect on the system.
pp16-17.
Changing elements usually has the least
effect on the system. … A system generally goes on being itself,
changing only slowly if at all, even with complete substitution of its
elements—as long as its interconnections and purposes remain intact.
If the interconnections change, the
system may be greatly altered. …
… A change in purpose changes the
system profoundly, even if every element and interconnection remains
the same.
To ask whether elements,
interconnections, or purposes are most important 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
behavior. 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 defining the unique characteristics of the system—unless
changing an element also results in changing relationships or purpose.
The elements are the stocks (stores of stuff within system) and
flows (that raise and lower stock levels). Interconnections connect
flows and stocks in a network of positive and negative feedback loops.
The presence of feedback introduces non-linearities, and change the
linear notion of causation.
p34.
The concept of feedback opens up the
idea that a system can cause its own behavior.
Systems are dynamic. Given a flow, it takes time to change a stock
level. And a flow out from one stock into another doesn’t happen
instantaneously. There are delays in the system. These size of these
delays are crucial to the overall behaviour of the system. For
example, they can cause oscillations (and, somewhat
counterintuitively, reducing a delay can sometimes amplify an
oscillation: think stock-market panics amplified by faster
computerised trading).
p58.
some delays can be powerful policy
levers. Lengthening or shortening them can produce major changes in
the behavior of systems.
Meadows identifies resilience, self-organisation,
and (possibly surprisingly if you think all this systems-speak is just
woolly hippy thinking) hierarchy as three key properties that
can be promoted and managed to help dynamics systems to work well.
Resilience allows a system to maintain itself, even in the face of
perturbations.
p76.
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
meta-meta-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
With self-organisation, the system is changing, adapting, growing,
in order to maintain itself. This “higher-level” resilience
might seem desirable, but it comes at a cost, mainly of
uncontrollability and unpredictability.
p79-80.
The most marvelous characteristic of some complex systems is
their ability to learn, diversify, complexify, evolve. …
This capacity for a system to make
its own structure more complex is called self-organization. …
Like resilience, self-organization
is often sacrificed for purposes of short-term productivity and
stability. …
Self-organization produces
heterogeneity and unpredictability. It is likely to come up with whole
new structures, whole new ways of doing things. It requires freedom
and experimentation, and a certain amount of disorder.
Hierarchy is a way of structuring a system, building a bigger system
out of smaller sub-systems. The hierarchy described here isn’t a
rigid, military-style, only up-and-down communication structure. There
are still sideways communications, but they are weaker.
p83-84.
Hierarchies are brilliant systems
inventions, not only because they give a system stability and
resilience, but also because they reduce the amount of information
that any part of the system has to keep track of.
In hierarchical systems
relationships within each subsystem are denser and stronger than
relationships between subsystems. Everything is still connected to
everything else, but not equally strongly. … If these differential
information links within and between each level of the hierarchy are
designed right, feedback delays are minimized. No level is overwhelmed
with information. The system works with efficiency and resilience.
Hierarchical systems are partially
decomposable. They can be taken apart and the subsystems with their
especially dense information links can function, at least partially,
as systems in their own right. When hierarchies break down, they
usually split along their subsystem boundaries. Much can be learned by
taking apart systems at different hierarchical levels … and
studying them separately. Hence, systems thinkers would say, the
reductionist dissection of regular science teaches us a lot. However,
one should not lose sight of the important relationships that bind
each subsystem to the others and to the higher levels of the
hierarchy, or one will be in form surprises.
Given that systems tend to perpetuate themselves, it is maybe not
surprising that hierarchies tend to “forget” their larger
purpose.
p84-85.
Hierarchies evolve from the lowest level
up—from the pieces to the whole … The original purpose of a
hierarchy is always to help its originating subsystems to do their
jobs better. This is something, unfortunately, that both the higher
and the lower levels of a greatly articulated hierarchy easily can
forget.
To be a highly functional system,
hierarchy must balance the welfare, freedoms, and responsibilities of
the subsystems and total system—there must be enough central control
to achieve coordination toward the large-system goal, and enough
autonomy to keep all subsystems flourishing, functioning, and
self-organizing.
Systems are pervasive, and we need to develop a systems worldview
just to navigate through the complexities of the modern day world.
Systems with their interconnected feedback loops are not simple,
intuitive, and readily understandable.
p87.
our mental models fail to take into
account the complications of the real world … You can’t navigate
well in an interconnected, feedback-dominated world unless you take
your eyes off short-term events and look for long-term behavior and
structure; unless you are aware of false boundaries and bounded
rationality; unless you take into account limiting factors,
nonlinearities and delays. You are likely to mistreat, misdesign, or
misread systems if you don’t respect their properties of resilience,
self-organization, and hierarchy.
So how do we go about understanding systems? First, we need to move
up a level, from looking at events (isolated static points in
time) to behaviours (dynamic sequences of linked events).
p88.
Systems fool us by presenting
themselves—or we fool ourselves by seeing the world—as a series of
events. … Events are the outputs, moment by moment, from the black
box of the system.
… Like the tip of an iceberg
rising above the water, events are the most visible aspect of a larger
complex-but not always the most important.
We are less likely to be surprised
if we can see how events accumulate into dynamic patterns of behavior.
…
The behavior of a system is its
performance over time—its growth, stagnation, decline, oscillation,
randomness, or evolution. If the news did a better job of putting
events into historical context, we would have better behavior-level
understanding, which is deeper than event-level understanding.
This understanding of dynamics is crucial. A picture of the elements
and interconnects is just a static diagram. We need to understand the
dynamics of how stocks change, up, down, oscillatory; what dominates,
what is secondary, to the behaviours.
p89.
Systems thinking goes back and forth
constantly between structure (diagrams of stacks, flows, and feedback)
and behavior (time graphs).
Understanding both structure and behaviour are essential. Behaviour
alone is not enough.
p90. [econometric]
behavior-based models are more useful than event-based ones, but they
still have fundamental problems. First, they typically overemphasize
system flows and underemphasize stocks. Economists follow the behavior
of flows, because that’s where the interesting variations and most
rapid changes in systems show up. … But without seeing how stocks
affect their related flows through feedback processes, one cannot
understand the dynamics of economic systems or the reasons for their
behavior.
Second, and more seriously, in
trying to find statistical links that relate flows to each other,
econometricians are searching for something that does not exist.
There’s no reason to expect any flow to bear a stable relationship to
any other flow. Flows go up and down, on and off, in all sorts of
combinations, in response to stocks, not to other flows.
Nonlinearities skew our intuition. There’s the obvious case of just
because a little of what you fancy does you good, doesn’t mean that a
lot of what you fancy does you better. But non-linearity combined in a
complicated network of dynamic feedback loops goes one better.
p92.
Nonlinearities are important not only
because they confound our expectations about the relationship between
action and response. They are even more important because they change
the relative strengths of feedback loops. They can flip a system
from one mode of behavior to another.
Again, the route to understanding is raising the description and
focus up a level.
p102.
There are layers of limits around every
[growth process].
Insight comes not only from recognizing which factor is limiting, but
from seeing that growth itself depletes or enhances limits and
therefore changes what is limiting. … Whenever one factor ceases to
be limiting, growth occurs, and the growth itself changes the relative
scarcity of factors until another becomes limiting. To shift attention
from the abundant factors to the next potential limiting factor is to
gain real understanding of, and control over, the growth process.
If we want to modify, engineer, control, or just guide such systems,
we need to have good measures of what we want from them. Get the wrong
measure, and we’ll get the wrong result.
p140.
Although there is every reason to want a
thriving economy, there is no particular reason to want the GNP to go
up. …
If you define the goal of a society
as GNP, that society will do its best to produce GNP. It will not
produce welfare, equity, justice, or efficiency unless you define a
goal and regularly measure and report the state of welfare, equity,
justice, or efficiency. The world would be a different place if
instead of competing to have the highest per capita GNP, nations
competed to have the highest per capita stocks of wealth with the
lowest throughput, or the lowest infant mortality, or the greatest
political freedom, or the cleanest environment, or the smallest gap
between the rich and the poor.
… In seeking the wrong goal, the
system obediently follows the rule and produces its specified
result-which is not necessarily what anyone actually wants.
Despite their resilience, these complex systems can be affected.
There are “leverage points”, where a (relatively) small
change can have a (relatively) big effect. But they are hard to find,
and hard to understand.
p146.
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 finding leverage points in complex and dynamic
systems. … 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.
Because things are so non-intuitive, we have to be very careful in
how we go about understanding systems. But it can be worthwhile: the
process can lead to a change in understanding and action, a paradigm
shift in the way we think about certain systems.
p164.
we change paradigms by building a model
of the system, which takes us outside the system and forces us to see
it whole.
So how do we go about building a model? Meadows is very strong on
valuing facts above theory (not surprising if you are after a paradigm
shift in understanding, as this will require a new theory).
p170-2.
Before you disturb the system in any
way, watch how it behaves. … study its beat. … watch it work.
Learn its history. Ask people who’ve been around a long time to tell
you what has happened. … make a time graph of actual data from the
system …
… Starting with the behavior of
the system forces you to focus on facts, not theories. It keeps you
from falling too quickly into your own beliefs or misconceptions, or
those of others.
… Watching what really happens,
instead of listening to peoples’ theories of what happens, can explode
many careless causal hypotheses. …
Starting with the behavior of the
system directs one’s thoughts to dynamic, not static, analysis…
looking to the strengths of the system, one can ask “What’s
working well here?” Starting with the history of several
variables plotted together begins to suggest not only what elements
are in the system, but how they might be interconnected.
… starting with history
discourages the common and distracting tendency we all have to define
a problem not by the system’s actual behavior, but by the lack of our
favorite solution. … Listen to any discussion … and watch
people leap to solutions, usually solutions in “predict, control,
or impose your will” mode, without having paid any attention to
what the system is doing and why it’s doing it.
However, a focus on facts does not mean a inhumane neglect of
unquantifiable qualities.
p176.
Pretending that something doesn’t exist
if it’s hard to quantify leads to faulty models. … Human beings have
been endowed not only with the ability to count, but also with the
ability to assess quality. Be a quality detector.
But the main quality in a modeller is the willingness to admit
ignorance, and to learn.
p180.
The thing to do, when you don’t know, is
not to bluff and not to freeze, but to learn.
p183.
In spite of what you majored in, or what
the textbooks say, or what you think you’re an expert at, follow a
system wherever it leads. It will be sure to lead across traditional
disciplinary lines. …
Seeing systems whole requires more
than being “interdisciplinary,” if that word means, as it
usually does, putting together people from different disciplines and
letting them talk past each other. Interdisciplinary communication
works only if there is a real problem to be solved, and if the
representatives from the various disciplines are more committed to
solving the problem than to being academically correct. They will have
to go into learning mode. They will have to admit ignorance and be
willing to be taught, by each other and by the system.
It can be done. It’s very exciting
when it happens.
(I have been involved in interdisciplinary work -- the learning
kind, not the talking past kind. No-one is an expert in all areas of
the work: everyone has something to learn from someone else. This
tends to promote a degree of humility: no domineering “experts”,
no monster egos, just the excitement of building a shared
understanding. It’s not all queasily humble, though: continually
admitting ignorance takes a certain degree of confidence. It is
very exciting, and very refreshing.)
This is a great book, and I wanted to quote a lot more of it here.
Go and read it. What I need now is the second volume, that tells me in
more detail how to build these models, and what to do with them next.
Like all good books, it has an interesting bibliography, which I’m
typing into Amazon. Like all good books, reading it worsens my
backlog, by turning “unknown unreads” into “known
unreads”.
