Richmond, Barry, "Systems Thinking: A Critical Set of Critical Thinking Skills for the 90’s and Beyond", 1990

Systems Thinking: A critical set of Critical Thinking Skills
for the 90's and beyond

Barry Richmond

High Performance Systems, Inc.
13 Dartmouth College Highway
Lyme, NH 03768

Abstract

The problems we are facing at all levels in the world today are growing more intractable. In par-
ticular, our problems are becoming increasingly resistant to unilateral solutions. I will argue that
this growing resistance and intractability result from the fact that while the evolving web of inter-
dependencies, of which we all are part, is rapidly tightening, the development of our capacity for
thinking in terms of dynamic interdependencies has not kept pace. As the gap between the nature
of our problems, and our ability to grok this nature grows, the planet will face increasing peril on
a multitude of fronts. System Dynamics and Systems Thinking -- the larger framework of which
it is a subset -- are an important part of an effective strategy for closing the gap between chal-
lenge and capacity for addressing challenge: Unfortunately, we as System Dynamicists and Sys-
tems Thinkers have been woefully inadequate in transferring our framework, skills and technolo-
gies to the population at large. Although we have "seen the light" for some thirty years now, we
have not opened the door to our inner sanctum wide enough to let others share in our insight-
generation capabilities with respect to the inner workings of closed-loop systems. In order to be
more effective in transferring our very valuable capabilities to a broader swath of humanity, we
need to see more clearly precisely what these capabilities really are, and also to understand the
forces driving the evolution of the education system into which these capabilities -- if they are to
be transferred on a broad scale -- must be assimilated. My purpose in writing this paper is to
shed some (hopefully new) light on both what it is we have to bestow, and also on where the edu-
.cational system that is to receive our bounty is headed. My intended audience therefore is both
Systems Thinkers and educators. My highest hope for the paper is that it will serve to further
eradicate the distinction between the two.

Introduction: The coupling is growing tighter The problems that we currently face have
been stubbornly resistant to solution, particularly unilateral solution. As we are painfully discov-
ering, there is no way to unilaterally solve the problem of CO2 buildup which is steadily and in-
exorably raising temperature around the globe. The problems of crack cocaine, ozone depletion,
the proliferation of nuclear armaments, world hunger, poverty and homelessness, rain forest de-
struction, and political self-determination, also fall into the category of "resistant to unilateral so-
lution". Why is it no longer possible for some world power to pull out a big stick and beat a nas-
ty problem into submission? The answer is that it probably never was good enough! It's simply
that the coupling between the various sub-systems conspiring to manifest the problem was less
tight. As such, it was possible to score a pyhrric victory by essentially pushing the problem ei-
ther "off into the future" or "off into someone else's backyard". Unfortunately, as our esteemed
colleague Dana Meadows is fond of saying, "There is less and less away to throw things into”.
By "away", she means both space and time! We have less and less space remaining to serve as
receptacle for our "garbage". And, we have less and less time before we must endure "the morn-
ing after". Both are artifacts of sustained material growth in our finite earthly dominion. Every
generation of human beings has been playing by these rules. It's just that our generation is the
first to have to begin taking them seriously!
System Dynamics '90 935

System Dynamics/Systems Thinking to the Rescue? If you accept the argument that the pri-
mary source of the growing intractability of the problems that we face is a tightening of the
couplings between the various physical and social sub-systems that make up our reality, then
you'll agree that System Dynamics/Systems Thinking holds great promise as an approach for aug-
menting our solution-generation capacity. The systems thinker's forte is interdependencies!
Their specialty is understanding the dynamics generated by systems composed of closed-loop re-
lationships. Systems Thinkers use diagramming languages to visually depict the feedback struc-
ture of these systems. They then use simulation to play out the associated dynamics. These tools
give people the ability to "see" a neighbor's backyard -- even if that backyard is thousands of
miles away. They also give people the ability to "experience" the morning after -- even if the
morning after is tens or hundreds of years hence.

Although the quality of the "seeing" and the "experiencing" underwritten by current systems
thinking tools is improving, these tools remain quite primitive today. In three years, they will be
much less so. In ten, available tools will be capable of effectively compressing space and time so
as to produce "virtual realities". In these electronic realities, people will be able to participate in
creating powerful, visceral experiences for themselves. But no matter how sexy the technology
gets, it always will be only part of the solution. If people are to make sense of their experiences
in "virtual realities", they must have the capacity for understanding the underlying closed-loop
framework that is generating these experiences. They must be capable of thinking both systemi-
cally and dynamically. In short, they must be Systems Thinkers! This, in turn, brings us right
back, face to face with a long unanswered question. This question has plagued the systems field
from its outset some thirty years ago at that venerable little technical university on the Charles
tiver in Cambridge. The question is: How can the framework, process and technologies of sys-
tems thinking be transferred to the rest of the world in an amount of time that is considerably less
than what it currently takes to get a Master's or PhD degree in our field?

I will argue that to successfully answer this question, it is necessary to confront two aspects of the
transfer process. We first must better understand the evolution of the education system into
which the transfer must be made (this system offers the best potential for large-scale transfer).
Second, we must better understand the "thing" we are seeking to transfer. Specifically, we must
recognize that this "thing" is multi-faceted. As such, for people to swallow and digest it, it must
be broken down into more consumable pieces. I now will examine both aspects of the transfer
process in turn.

Aspect 1: The evolution of the Education System As with any viable system, our system of
formal education is evolving over time. The last several decades have seen numerous innovative
experiments in educational pro-
cess and technology. Open
classrooms, computer-aided in-
Process struction, and interdisciplinary
course offerings are but a few
of the initiatives that have, and
are, being tried. It is my per-
ception that the time now is ripe
for three evolutionary threads to
“come together", fusing to form
a new learning gestalt. The
three threads, as illustrated at

left, are: educational process,
pee Tecanionles thinking paradigm, and learn-
ing tools.

936 System Dynamics '90

The evolutionary fusing of these three threads can successfully create a permanent change in the
way people learn. The evolution of each thread, taken independently, can not.

Thread 1: Educational process 1 will refer to the newly emerging educational process as
Learner-directed learning. 1 like this phrase because it positions the process in sharp relief
against the process which has dominated for at least the last two hundred years. This, currently
dominant, process I'll refer to as teacher-directed learning.

We are all, to varying degrees, products of a teacher-directed learning process. In this process,
the classroom is arranged with students facing front -- be they arrayed in rows, or nested "U's".
At the front is “Herr Professor". Herr Professor's job is to transmit what he or she "knows" to the
students. The students' job is to "take in" as much of this knowledge transmission as possible.
This is why it's important for students to “be quiet" and pay attention in the classroom. A sche-
matic representation of the teacher-directed process appears below.

di : It's important to reveal the implicit assumption
Teacher “directed . about learning which underlies a teacher-
directed educational process. It is that learning
o) is primarily an assimilation process. This as-
sumption, in turn, defines appropriate roles for

both teacher and student. Teacher is transmit-
ter, or content dispenser. Student is receiver, or
content receptacle. The objective of the educa-

tional process then also becomes easy to define.
O It is for the teacher to "fill up” the student.

Measuring performance in this system is

straight-forward. Simply ask the student to re-
fe) transmit what has been previously transmitted

by the teacher. If the student can "dump" a full
load, they fe Desfornain i ig ells I's snseresiing
to note that the teacher-di process tacitly
eo) (e) (e) (e) assumes that the students do not have much to
=~! contribute to each other's learning experience. ~~
Otherwise, they would not be arrayed in a physical arrangement in which they face the back of
each other's heads.

1@) ie)
1@) te)

- - Contrast the teacher-directed process
Learner-directed: with what unfolds in a learner-directed
Teaching by wandering around approach, illustrated at left. The learn-

er-directed approach is founded on the
assumption that learning is fundamental-
(e) @) fe) ly a constructive, rather than an assimi-
ro) lative, process. This means that to learn,
the seudent iat reconstruct what is be-
ing “taken in". Meaning and under-
° io] o ° standing are "making" Irocesses, not
Oo oO "imbibing" processes. Extending the as-
sumption leads to the conclusion that
because there are many strategies for
oO fe) ie) eo) "making", learning can not be standard-
re) ized. People construct in different ways,
ie) at different paces and in different se-
quences. Construction also is an active
process. Being quiet and listening often can be antithetical to constructionist activity. Both

System Dynamics '90 937

teacher and learner, in this process, have new roles. Teacher now is charged with providing ma-
terials and alternative strategies for "constructing". In a sense, they create the building environ-
ment. Once the “building process" begins, they "wander around" playing the role of project man-
ager -- keeping the process on track, but not doing the construction. Students are the construction
workers. They design and then construct the knowledge edifice. And, like construction crews,
they often can accomplish more, reaping more enjoyment in the process, by working in teams
rather then alone.

In order for a learner-directed approach to work, it is essential that both teachers and students re-
think their roles and respective contributions to the learning process. Teachers must be willing to
abdicate their position as "all knowing" fonts of knowledge and wisdom. Students, in tirn, must
be willing to take personal responsibility for their learning process. Students must also learn to
cooperate with each other as learning partners, rather than viewing fellow students as competitors
in a zero sum grade-game. These are easy words to write. The shifts in perspective and process
needed to bring these changes about, however, are quite profound! Fortunately, the benefits that
appear to be achievable -- from looking at the results of some experiments in several learner-
directed processes -- promise to be equally profound.

To begin with, the echo of the age-old question -- why do we have to learn this? -- is likely to
cease reverberating through the halls of our institutions of formal learning. The active learning
process will provide an outlet for the inherent need that all humans have for activity. The cooper-
ation involved will model the very processes needed to live in an increasingly interdependent
world community. And, all these gains can be achieved with no necessary forfeiture of content
assimilation, because when students can see the why, content assimilation becomes a means to an
end -- rather than an end in itself. There are thus many "free lunches” to be eaten here. We have
only to avail ourselves of the opportunity. Availing, however, will require not only the profound
shifts in role, administrative stucture, and performance measurement already alluded to, but in ad-
dition that the two other threads -- thinking paradigm and learning tools -- also “come together".
No mean feat. Let's now look briefly at the other two threads.

Thread 2: Thinking Paradigm The first of the two is thinking paradigm. It's very difficult to

see what you use to see. But that's what is involved in confronting your thinking paradigm. It's

the water you swim in, so pervasive it's completely transparent. To bring it to front, try answer-
ing the following question: What is causing the overpopulation problem being faced in so many
countries .in the world today? Take a moment to jot down a few thoughts before proceeding.

If you took the time to record your thoughts, I'll bet they took the form of a list. If you reflect on
the structure of the mental modeling process that generated this "laundry list", I think you'll find
that it looks something like this:

poverty

lack of education

inadequate birth control info — Overpopulation
religious sanctions

etc

938 System Dynamics '90

I like to refer to the mental modeling process that produces such lists as "laundry list thinking". I
think you'll find it to be the dominant thinking paradigm in most of the Western world today. If
you ask most Westerners (and a good number of Easterners, too) a "what causes what?" type
question, you are very likely to get a laundry list of causal "factors" in response. Implicitly, peo-
ple also are weighting each of the factors in the list. This one is most important. This is second,
and so on. This brand of mental modeling has been given analytical expression as a multiple re-
gression equation. Many of us are familiar with this type of expression:

y = f(ayX, +a Xq +... a,Xj)
where:
y__ is the dependent variable
Xj are the independent variables
a; are the coefficients (or weighting factors)
for each of the independent variables

Notice that the implicit assumptions in the laundry list thinking process are that: (1) each factor
contributes as a cause to the "effect"; i.e., causality runs "one way", (2) each factor acts indepen-
dently, (3) the weighting of each factor is fixed, and (4) the way in which each factor works to
cause the effect is left implicit (represented only by the sign of the "alphas"; i.e., this factor has a
positive or a negative influence).

The Systems Thinking paradigm offers alternatives to each of these assumptions. First, accord-
ing to this paradigm, each of the "causes" is linked in a circular process to both the "effect" as
well as to each of the other "causes". Systems Thinkers refer to such circular processes as "feed-
back loops". The diagram shown below illustrates a few such loops.

The shift from one-way to circular causality, and from independent factors to interdependent rela-

poverty’

Overpopulation
lack of education

tionships, is a profound one. In effect, it is a shift from viewing the world as a set of static, stim-
ulus/response relationships, to viewing it as an ongoing, interdependent, self-sustaining, dynamic
process. Yes, that's a mouthful. It also will cause a students to think in a very different way
about what is going on in the world around them.

The third assumption implicit in the laundry list paradigm is that the "factors" have a static
weighting. By contrast, in the systems thinking view, as the diagram at right suggests, the
strength of the closed-loop relationships is assumed to wax and wane over time. Some loops will
dominate at first. Other loops then will "take over". And, so on. Therefore, addressing a prob-
lem is not seen as a one-shot deal. Rather it is considered necessary to think in terms of ongoing,
interdependent relationships whose strengths are varying over time -- in part in response to inter-
ventions that may have been implemented into the system.

The final assumption associated with laundry list thinking is that correlation is good enough for
System Dynamics '90 939

explaining how a system works. The systems thinking paradigm challenges this regression anal-
ysis approach, offering in its place operational models of how things work. Thus, for someone
steeped in the systems thinking paradigm, it would not be enough to identify the factors that are
correlated with "overpopulation". Instead, it would be necessary to actually offer an operational
explanation for how overpopulation is generated. The contrast between correlational and opera-
tional models of the overpopulation process is illustrated in the diagrams below.

A correlational model

Population

religious_sanctions

poverty

Tack_of_education

An operational model

Population

religious_sanctions

increase_in_avg Level_of_Poverty

incr_in_poverty

The Systems Thinking thinking paradigm, when combined with a learner-directed learning edu-
cational process will breed students who are hungry for understanding how things really work,
and who will continually be looking for how these workings might change over time as a conse-
quence of shifts in the relative strengths of the underlying dynamic relationships.

Thread 3: Learning Tools To fully meld a learner-directed learning process with the systems
thinking paradigm, it is essential to have the right set of learning tools available for classroom
and out-of-classroom use. The tools of a teacher-directed, laundry list process -- textbooks and
blackboards -- will play a smaller role in a non-transmit, active learning process. Textbooks op-
erate, in effect, as purveyors of "silent lectures". Students read them, for the most part, for the
same reason they currently go to class -- to assimilate content. On blackboards teachers can chart
940 System Dynamics '90

static relationships and display lists. However, blackboards are not well suited to analyzing a
system's dynamics. To support an inquiry-oriented, learner-directed learning process, textbooks
and blackboards must share the stage with an emerging tool: the personal computer. The per-
sonal computer, with its rapidly expanding sound and graphic animation capabilities, holds the
potential for effectively compressing space and time. As such, these devices can serve as person-
al theaters in which the aforedescribed "virtual realities" can be played out. Students literally can
have the experience of wandering around in both space and time, stashing content -- which has
been embedded in appropriate nooks within the electronically-based learning environment -- into
their "intellectual knapsacks" as they go. And, the content needn't be limited to "facts". Video
segments, sounds, animations, puzzles, and all other forms of intellectually stimulating challenge
are fair game. What's more, the wandering needn't be choreographed by a teacher. Both the pace
and sequence of discovery can be left under the control of the individual learner, or group of
learners.

In order to elevate a learning environment above the status of "video game", it is essential that it
be equipped with the capacity for enabling learners to "understand why". Without this capacity,
the interplay between learner and computer can too easily deteriorate into "beat the machine". It
is encouraging to see that even with today's relatively primitive software tools (Richmond; Peter-
son; Vescuso, 1987; Peterson, 1990), a few truly excellent learning environments have been
created, and are now in use (Draper, 1990; Peterson, 1990). The results have been extremely
promising! Students who previously had “gotten off the bus", tended to "get back on". The op-
portunity to design something (like, for example, a mammal, a state park, or a policy for manag-
ing an ecosystem) in a learning environment, in effect, seemed to reset the counters -- giving all
students a chance to succeed once again. Motivation was high, and hence disciplinary issues for
the most part evaporated. Students assimilated content at higher rates, in several cases doing "re-
search" (on their own!) in order to be able to do a better job in their design project. At the same
time, depth of understanding of the concepts increased, and students’ capacity for "critical think-
ing" was enhanced. Students began to think in terms of the long-run, as well as the immediate-
tun, implications of their decisions and actions. They began to anticipate the second and third-
order impacts of their choices.

These results begin to suggest what is possible when a new learning gestalt comes together. But,
even when all three threads -- educational process, thinking paradigm, and learning tools -- with-
in a particular educational setting are ripe for fusion, there remains the issue of how to bring
teachers up to speed in the underlying framework, process, and technologies of Systems Think-
ing. Let's begin by emphasizing that is not reasonable to expect teachers, on a wide scale, to stop
what they're doing and move en masse to one or more of the institutions of higher learning that
offer formal degrees in System Dynamics. Teachers, like most other people, are very busy. And,
many could not secure the financial resources, even if they did have the time. Furthermore, there
is not sufficient System Dynamics teaching capacity to process the demand! So, what then can
be done to facilitate the fusion process when things are ready to fuse?

Aspect 2: Transferring Systems framework, process & tools 1 taught System Dynamics in the
Thayer School of Engineering at Dartmouth College for nine years. During this time, I experi-
enced considerable frustration at the fact that after three (or more) courses, even the good Mas-
ter's student ("good", in this case, being a pretty select breed!) often encountered considerable dif-
ficulty in constructing and analyzing a model from scratch come thesis time. This being the case,
what hope was there, I used to muse, for any widespread dissemination of Systems Thinking?

Since leaving Dartmouth three years ago, my colleagues and I at High Performance Systems have
embarked upon a mission designed to answer the question: just how far is it possible to go in
cutting the up-to-speed time for the serious, yet not whiz bang, pilgrim? Now, after offering
more than fifty Workshops for educators, business folk, and all manner in between -- both in the
System Dynamics '90 941

US and abroad -- I believe that I can begin to shed some light on an answer to this question. The
answer is: pretty damn far! In recent Workshops, after 21/2 days, participants had produced
models -- from scratch -- which addressed issues of their own choosing. The models were initial-
ized in steady-state, had been subjected to a rigorous testing program to establish robustness, and
in many cases did a credible job of replicating the observed behavior pattern of interest. The
quality of the better models, in terms of their "tightness" and insight-generation capacity, was
equivalent to what I used to receive from a good Master's thesis effort. How was this achieved?

First of all, over the three year period, we carefully monitored performance and continually fed
back the results. We maintained no "attachment" to what we had done before. Indeed, we turned
over our curriculum materials and process at least 50 times each (and continue to do so). My in-
tention here is not to summarize this closed-loop evolutionary process. Instead, I wish to stand
back from the process, and to focus on what we discovered to be the most fundamental gait to
learning productivity. Simply stated, it is cognitive overload.

What has become apparent over the course of the last three years' workshops is that doing good
Systems Thinking means operating on at least five thinking tracks simultaneously. This would be
difficult, even if these tracks were familiar ways of thinking. But, they're not! And the result, in
the vast majority of cases, is cognitive overload. Nevertheless, we've found that it is possible to
take certain steps which can prevent people from becoming overloaded. Specifically, you can:
(1) tell people that they're going to be asked to juggle multiple thinking tracks, simultaneously
(2) be explicit about what these tracks are, and (3) align the curricular progression so as to se-
quentially develop the skills associated with one thinking skill at a time.

It helps to begin by placing the five systems thinking skills into a broader context. That context,
in Education, seems most appropriately labeled "Critical Thinking Skills". The five tracks which
I would construe as constituting systems thinking skills are depicted in the diagram below.

Dynamic
Thinking

Generic
Thinking

Other
Critical Thinking
Skills

Structural
Thinking

Operational
Thinking

Scientific
Thinking

Critical Thinking Skills: The Systems Thinking Piece
942 System Dynamics '90

I now will briefly discuss each of the five systems thinking skills in turn.

Skill 1: Dynamic Thinking Dynamic thinking is the ability to see and to deduce behavior pat-
terns rather than focusing on, and seeking to predict, events. It's thinking about phenomena as re-
sulting from ongoing circular processes unfolding through time, rather than as the result of a set
of "factors". Dynamic thinking skills are honed by having people trace out patterns of behavior
which change over time, and by thinking through the underlying closed-loop processes which are
cycling to produce particular "events". Having students think about the processes that produce an
earthquake, or that generated the events in Tienneman Square, the smashing of the Berlin Wail,
or the fall of Nicolae Ceaucescu would be good exercises for developing dynamic thinking skills.
Causal loop diagrams are a nice technology to use in developing a person's ability to think dy-
namically. Also very helpful is the use of simple models in real-time exercises in which students
are asked to hypothesize what behavior pattern will result when a particular system is "disturbed"
in a particular way. As an illustration of this kind of exercise, consider the simple system depict-
ed below:

Maturation_Pipeline Mature_Trees

plant_a_sapling

enter_mature_stock harvesting

In this system, Mature Trees are harvested. Each time a Mature Tree is removed via harvesting, a
sapling is instantaneously planted to replace it. Saplings take exactly 6 time periods to pass
through the Maturation Pipeline (entering the Mature Tree stock). All saplings mature (none die,
all germinate). Given these structural assumptions, next assume the system is initially in steady-
state. This means that: (1) mature trees are being harvested at the same rate that they're being
planted (by definition, this is true), and (2) that the maturation process "pipeline" is primed up
such that trees are entering the Mature Tree stock at this same rate. Thus, both the stock of Ma-
ture Trees, and the number of trees in the Maturation Pipeline, are constant. Now, suppose that
the harvest rate all of a sudden "steps up" to a new higher level, and then remains there forever.
What pattern do you think the stock of Mature Trees will trace over time in response to this per-
manent step-increase in the harvest rate? Sketch your guess on the axis provided below.

ongcree

aoorwW

Time
System Dynamics '90 943°

In our experience, with widely diverse audiences (across education levels, occupations, age, and
cultures), only about 20% of people who guess at the answer guess correctly! This says some-
thing about the level of our dynamic thinking skills. It also says something about the potential
for an extremely fruitful union of computer and human. Computers could never construct, or
"understand", the preceding illustration. However, 100% of the computer population will cor-
rectly deduce the dynamic pattern of behavior that the Mature Tree stock will trace in response to
the step-increase in the harvest rate. Combining the human being's ability for making meaningful
“structure”, with the computer's ability for correctly tracing out the dynamic behavior patterns im-
plied by that structure, holds great promise for leveraging our capacity for addressing the set of
intractable problems previewed at the outset of this paper.

The correct answer to the illustration, by the way, is that the Mature Tree stock will decline line-
arly for six time periods. It then will level off and remain at this lower level forever. If you are
having trouble understanding why this is true, I suggest that you trace out the pattern charted by
each of the three flows in the system, following the step-increase in harvesting. Then, think
about what will happen to the Mature Tree stock when this pattern of flows unfolds.

Skill 2: Generic Thinking Just as most people are captivated by “events”, they are similarly
generally locked into thinking in terms of specifics. Thus, for example, Gorbachev is seen as the
man who brought glasnost and peristroika to the Soviet Union. He's also the man who allowed.
freedom to ring out in many of the Soviet satellites. But is it Gorbachev, or is freedom an idea
whose time has come? Similarly, was it Hitler? Napolean? Joan of Arc? Martin Luther King?
And, so on. The notion of thinking generically rather than specifically does not apply only to his-
tory. Seeing the commonality in the underlying feedback loop relationships which generate a
predator-prey cycle, a manic-depressive swing, the oscillation in an L-C circuit, and a business
cycle, illustrates how generic thinking can be applied to virtually any substantive arena.

To develop generic thinking skills, people can work with the series of generic structures which
progress from those which generate simple exponential growth and decay, through S-shaped
growth, to overshoot/collapse and oscillation (Richmond; Peterson; Vescuso, 1987). They also
can do exercises with the classic "policy insensitivity" structures (e.g., Shifting the Burden to the
noe Goal, First Response in the Wrong Direction, Promotion Chain, etc) (Rich-
mond, 1 .

Skill 3: Structural Thinking Structural Thinking is a one of the most disciplined of the systems
thinking tracks. It's here that people must think
in terms of units-of-measure, or dimensions.
Physical conservation laws are rigorously ad-
hered to in this domain. The distinction be-
tween a stock and a flow is emphasized. Population

To catch a glimmer of the kind of skill being

developed here, consider the simple causal loop

diagram shown at right. The notion here is a ( + )

simple, intuitive one -- one that would work

pretty well if you were preceding along the Dy-

namic Thinking track. Beginning with births,

the diagram says simply that as births increase .
birth

Population increases. And, as Population in-
creases births follow suit. This is a simple pos-
itive feedback loop process. Left unchecked, it
will generate an exponential increase in the
population over time.

When the same two variables are represented

944 System Dynamics '90

using a structural diagram, a subtle, but important dynamic distinction between the two variables
becomes apparent. This distinction is depicted in

; the diagram which appears below. The same posi-
Population tive feedback process that was depicted in the cau-

sal loop diagram is shown here. And, once again,
we can see that if births increase, Population
would follow suit.

births However, now return to the causal loop diagram
and run the thought experiment in reverse. That
is, begin by decreasing births. According to the
causal loop diagram, a decrease in births would result in a decrease in Population. Clearly this is
not necessarily true. Population would only decrease, following a decrease in births, if births fell
toa level below deaths. The causal loop diagram, a tool for engaging in Dynamic Thinking, is

thus not well-suited to doing Structural Thinking. That's why the Structural Diagram was invent-
ed. As you can see by looking at the simple structural diagram above, a decrease in births will
only serve to slow the rate at which Population is increasing! When engaging in structural think-
ing, such subtle distinctions (which can be very important in understanding dynamics) must be

Liquid_in_vat
refill liquid liquid_into_bottles

=x x x.

~O Lt es O Bottles_being Filled O 1 ined

new_empties bottles_on_conveyor filled_bottles Bottles

Another simple example will further illustrate the rigor associated with the Structural Thinking

track. Consider the following diagram:

—
Liquid_in_vat

refill liquid liquid_into_botties

liquid_per_bottle

Empty_Bottles Bottles_being_Filled Inventoryof_Filled
newempties bottles_on_conveyor filled_bottles Bottles

System Dynamics '90 945

In this alternative representation, notice that the flow of liquid and the flow of bottles are kept
distinct. This is not the case in the first, more intuitive representation. If you took a snapshot of
the actual process, the picture would more closely resemble the first diagram. The fact is that liq-
uid really does pour into bottles. However, the reality of the situation, from a units-of-measure
standpoint is that you still have two quantities: bottles and liquid. If you flowed liquid into bot-
tles in the model, you'd end up with a very strange quantity in the box labeled "Bottles being
Filled". That quantity would have the mixed units of measure, bottle-liters (or some such).

When engaging in Structural Thinking, it is essential to maintain units-of-measure integrity with-
in each Stock/flow sub-system. "Loose" notions, like "I put a lot of effort into that project", and
"T'll give you all my love" simply "don't compute" when doing Structural Thinking. Quantities
that flow into a stock must have the same units of measure as that stock. Maintaining unit integ-
tity ensures that physical conservation of all quantities is maintained. This, in turn, keeps you
from getting something for nothing. It also exerts a very strong discipline and preciseness on the
thinking process.

We have evolved a set of Stock/flow koans (Zen paradoxes) that are quite effective in facilitating
peoples’ attempts to internalize the distinction between stocks and flows, as well as providing
practice in maintaining unit-of-measure integrity.

Skill 4: Operational Thinking Operational Thinking goes hand in hand with Structural Think-
ing. Thinking operationally means thinking in terms of how things really work -- not how they
theoretically work, or how you might fashion a bit of algebra capable of generating realistic-
looking output behavior, but how they really work. One of my favorite examples of the distinc-
tion between operational and non-operational thinking is provided by the "universal soil loss
equation" -- this equation expresses a “fundamental law" in soil physics. The equation, which is
used to predict the volume of erosion that will occur on a given parcel of land, can be represented
as:

Erosion = RKLSCP
where:
Ris rainfall
Kis soil erodability
Lis slope length
S is slope gradient
C is vegetative coverage
P is erosion control practices

Now, no self-respecting soil particle solves this equation before it rolls on down the hill! In fact,
the erosion process, if you were to ask how it really works, probably would look more like this:

io

36 6

Water_on_Soll

erosion

3 eroston_contro!

practices

sotl_per_unit_oNrunoff

rainfall runoft erodability

Oo O aL oO

veaetative_cover infiltration percolation

946 System Dynamics ‘90

A second brief example should make it clear what Operational Thinking is all about. A popular
economic journal published the research of a noted economist who had developed a very sophisti-
cated econometric model designed to predict milk production in the US. The model contained a
raft of macroeconomic variables woven together in a set of complex equations. But nowhere in
that model did cows appear! If you ask how milk production actually is generated, you'll discov-
er that cows are absolutely essential to the process. If you were thinking operationally about milk
production, you'd be centering your thinking around cows. You'd focus on the real rhythms asso-
ciated with farmers' decisions to increase and decrease herd size, the relationships governing milk
productivity per cow, and so on.

Operational Thinking grounds students in reality. It also tends to be perceived as relevant -- be-
cause the student is thinking about it like it really is, rather than dealing with abstractions which
may bear little relationship to what's going on in reality. It's easy to create exercises that develop
Operational Thinking. Simply look around at real-world processes (like learning, becoming
friends, experiencing peer pressure, pollution, drug or alcohol addiction, etc) and ask: How do
these processes really work? Let the students diagram their resulting observations. Then, have
them challenge each other's depictions, asking: Is this really how it works?

Skill 5: Scientific Thinking The final component of Systems Thinking that we have identified is
Scientific Thinking. Let me begin by saying what Scientific Thinking is not. My definition of
Scientific Thinking has virtually nothing to do with absolute numerical measurement. Too often
science is taken to be synonymous with "measuring precisely". To me, Scientific Thinking has
something more to do with quantification than measurement. Again, the two are not synony-
mous. There are very few things that can be measured unambiguously. Length, width, height,
concentration, magnitude, and velocity offer a few examples. But think of all the things that can
not be measured precisely. How much wisdom you possess. How nice a person you are. What
the quality of life in a city or country is. How good you feel. What it's like to go to a particular
school. How hungry you are. How much you love someone. How much confidence or self es-
teem you have. How frustrated you feel. I think you'd agree that all of the aforementioned non-
measureables are important to you. Yet, no one can measure them in any absolute numerical
sense. But you can quantify all of them! It's simple, you pick a scale -- say, 0 - 100 -- and then
you assign a value. 0 means "the absence of". 100 means the "maximum possible amount". Be-
cause you've assigned a scale does mean that you could specify exactly what any of these values
are in the real system. It means only that you've established a rigorous convention for thinking
about the dynamics of the variable. You now can ask questions like : What keeps Seif Confii-
dence from rising above 100? Since you've defined 100 as the "max possible", then some pro-
cesses must exist in the real system which prevent this accumulation for overflowing! Because
you've been rigorous (scientific) about your quantification, you now have the opportunity to think
rigorously about the dynamics of the variable.

But quantification is not all there is to Scientific Thinking. Thinking scientifically also means be-
ing rigorous about testing hypotheses. This process begins by always ensuring that students in
fact have a hypothesis to test. Once again, in the absence of an a priori hypothesis, the experi-
mentation process can easily degrade into a "video game". People simply will flail away trying
to get one of the Super Mario Brothers to the Princess. Insisting upon having an explicit hypoth-
esis to test before engaging in any simulation activity helps to guard against the video game syn-
drome. The hypothesis testing process itself also needs to be informed by Scientific Thinking.
People thinking scientifically modify only one thing at a time -- holding all else constant. They
also test their models from steady-state, using idealized inputs to call forth “natural frequency re-
sponses". This set of rigorous, hypothesis-testing concepts really is at the heart of what I mean
by Scientific Thinking.

Exercises abound in which simple, already-constructed models are used as the basis for a disci-
plined testing regimen. These exercises can be used to bolster Scientific Thinking capacity.
System Dynamics ’90 947

The Five Track Melee When you become aware that doing good Systems Thinking entails
working on at least these five tracks simultaneously, it becomes a lot easier to understand why
people trying to learn this framework often go on overload! When these tracks are explicitly rec-
ognized, and separate attention is paid to developing each skill, the resulting bite-size makes the
fare much more digestible. You can consume quite a large chunk of food if you do it in a succes-
sion of small bites. You will gag if you try to swallow it whole! We've found that explicitly sep-
arating these five tracks, and then attending to skill development in each, greatly increases learn-
ing productivity.

Summary

The coupling between the various physical, social, and ecologic sub-systems which go to make
up our reality is tightening. There is indeed less and less away, both spatially and temporally, to
throw things into. Unfortunately, the evolution of our thinking capabilities has not kept pace
with this growing level of interdependency. The consequence is that the problems we now face
are stubbornly resistant to our interventions. To "get back in the footrace", we will need to coher-
ently evolve our educational system along three dimensions: educational process, thinking para-
digm, and learning tools. At the nexus of these three threads is a learner-directed learning pro-
cess in which students will use personal computer-based learning environments to build their
intuition and understanding for complex. interdependent systems by participating in "virtual reali-
ty"-based experiences. One of the principal factors gaiting this exciting evolution is the capacity
for transferring the Systems Thinking framework to educators, and then on to their students. By
seeing Systems Thinking as lying within the broader context of Critical Thinking Skills, and then
recognizing the multi-faceted nature of the thinking skills involved in Systems Thinking, we can
greatly reduce the time that it takes for people to internalize this framework. This is an extraordi-
narily exciting time to be in the education profession!

References

Draper, F. 1990 Systems Thinking and Learner-Directed Learning: The Orange Grove
Experience, to be published in the System Dynamics Review, Simmer, 1990.

Peterson, S. 1990. A User's Guide to STELLAStack (Second Edition) Lyme,N.H.: High
Performance Systems, Inc.

Richmond, B. 1985. Designing Effective Policy: A Conceptual Foundation Hanover, N.H.:
Thayer School of Engineering, Dartmouth College.

Richmond, B.; Peterson, S.; Vescuso, P. 1987. An Academic User's Guide to
STELLA™ Lyme, N.H.: High Performance Systems, Inc.