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Systems in everyday life, technology
study, and engineering
Systems are everywhere in our daily discourse,
from the solar system to home entertainment systems, from water
treatment to national security systems. Railway networks and
electric power grids are systems, so is the internet and the world
wide web. Then there are the more intangible political, social, and
financial systems. “The system works,” people say when things go
smoothly; “the system fails,” when troubles strike. The commonsense
notion of system is familiar: the system is a whole comprising
interrelated parts with significant complexity; the system is not
merely the sum of its parts. There seems to be no restriction on
whether the related parts are animate of inanimate, people of
things, hardware or software, concrete or abstract. Nevertheless,
their interrelations, although complex, should be susceptible to
some rational understanding. A muddled mess or an incomprehensible
chaos is at best a failed system, if one at all.
“System” is a buzzword in technology studies,
although its significance beyond the commonsense notion is obscure.
Systems are said to be heterogeneous, but the heterogeneity often
turns out to be a jumbling miscellany and the system a sausage with
medley indifferentiable stuffing. Perhaps the most prominent notion
of “system” is expressed by the metaphor of a “seamless web.”
Introduced by Thomas Hughes, the prime promoter of “systems
thinking,” it conveys the notions of holism, perfection, and
resistance to analysis. It is popular in postmodern scholarship,
which is hostile to rationality.
Systems are central to engineering. Engineers
are responsible for designing and building transportation,
communication, and other systems that operate in the real world.
Their systems concepts are clear. Here I explore three: systems
theories, systems approach, and systems engineering. None comes
close to a seamless web. The systems approach in engineering, with
its emphasis on analysis and modularity, is opposite to the
anti-analytic postmodern “systems thinking.”
Systems versus seamless webs
In his book Science of the Artificial,
Herbert Simon gives the parable of two watchmakers, each designs a
watch with a hundred parts. In the first design, the hundred parts
are so thoroughly interrelated that the watch totally falls apart if
any one is removed. The watch is expensive because it must be
assembled in one breath and cannot be repaired by replacing parts.
The second watch has similar performances, but its parts are grouped
into ten modules, which can be replaced if defective. Because it is
simple to assemble and easy to modify, the second watchmaker is able
to offer his products at a lower price and drive the first
watchmaker out of business.
Simon’s first watch design is akin to a
seamless web; his second design, an engineering system. A seamless
web is good if perfect, but perfection is more often than not
illusory. Seamless webs are prone to disasters, because they can be
unraveled by the tiniest of loose ends. The effect of a tiny flaw
propagating unhindered and creating web-wide havoc is one of the
root causes of “normal accidents,” as Charles Perrow called it. To
control such disastrous scenarios is a major reason for
modularity. To limit potential damages, engineers are careful
to introduce seams and modular boundaries into their systems. An
electric power grid provides an example. Once electricity is fed
into the grid, it flows automatically according to physical laws and
the conditions of the entire grid. The risk of such seamless
configuration is well known. Effects of a small mishap, such a
lightning striking out a transformer, can cascade though the grid,
leading to power failures in large regions. The 1996 Northwest
power blackout and the 2003 Northeast blackout each cost damages in
excess of a billion dollars. Engineers tolerated the seamless web
not because they deemed it superior but because they could do little
about its faults. Until very recently, they had no way to switch
high-voltage currents on the grid in real time. Even so, they did
try their best to install gates between regions for damage control.
New England escaped the 2003 Northeast blackout because the gate
designed to disengage it from the grid worked in time – it was saved
by a seam.
A system can operate seamlessly without being a
seamless web. An example is the internet, which is a patchwork of
many networks: landline telephone networks, wireless computer
connections, satellite links, and more. All participating networks
use packet switching. Otherwise each has its peculiar internal
operating principles. A basic design principle of the internet is
to preserve as much as possible the autonomy of these internal
principles, so that each network can be individually modified and
improved. To tie disparate network together in the internet,
engineers design routers and protocols at the interface between two
networks, so that signals can pass smoothly between them. The
routers are seams, good seams. The superiority of an engineering
system lies not in seamlessness but in its good seams or good
interfaces between its parts.
Besides the illusion of perfection, the gist of
the seamless-web metaphor and associated “systems thinking” is the
holistic aura that shields it from critical analysis. The
resistance to analysis makes them look profound, but they are
basically obscure and muddled, which are major critiques of
postmodern studies. In stark contrast, the systematic approaches in
science and engineering strive for clarity.
Being profound is different from seeming
profound. Tackling with real-world complexity is different from
using the word “complexity” to decorate simplistic ideas dreamed up
in arm chairs. Simon’s parable is intended to illustrate an
approach for designing complex artificial systems that must operate
in the real world, but its wisdom applies equally to research in
natural science. In principle, the universe is a whole in which
everything is connected to all others. Gravity and
electromagnetism, the two forces that act between all things above
the nuclear level, have infinite ranges. However, if we must treat
the universe as a seamless web and grasp everything in it at once,
our tiny brains would be so overwhelmed that we would fail to
understand anything at all. None of our concepts would be valid,
because concepts invariably make distinctions and “carve nature at
its joints,” as Plato said.
Science is successful partly because scientists
are content to proceed one step at a time, to bite off what they can
chew, and refrain from confusing grandiloquent such as
seamlessness. Instead of trying to tackle the whole universe at
once, scientists take things apart and examine bits and pieces,
acknowledging their own limitations. The methods of Socrates were
said to be division and collection; Galileo, resolution and
composition. Descartes and Newton discusses analysis and
synthesis. Engineers practice functional decomposition and physical
integration in systems design.
Analysis clarifies. Analysis is also called
reduction, and "reductionism" to scientists means the importance of
analysis. However, "reductionism" has also become a philosophical
dogma asserting that a system is nothing but its
constituents, e.g., you are nothing but your genes or neurons.
Ideological reductionism, which slights synthesis, has engendered
much debate in the philosophy of science, debates that generate more
heat than light.
Holism rejects analysis and sees only the
whole. Reductionism rejects synthesis and sees only the parts. The
systems approach in engineering integrates analysis and synthesis.
It brings to relief the dictionary definition of a system as a whole
with interrelated parts, emphasizing internal structure.
Systems approach as
analysis-synthesis
Let us consider a familiar engineered system,
an automobile. When you are driving it, it works as a unitary
whole. Any trip to the garage will convince you that it is a system
composed of many interrelated subsystems: power train, transmission,
ignition, steering, braking, lubricating, suspension, and more.
Each of these subsystems is in turn made up of many parts, for
instance the clutch, stick shift, and gear box for transmission.
The gear box in turn consists many components, and so on down to
nuts and bolts.
Cars are designed by engineers. Their systems
approach is illustrated in many textbooks by the Vee model,
originally due to Forsberg and Mooz. The down stroke of the Vee
represents functional analysis, the upward stroke physical
synthesis.
 
When engineers want to design a car, they do
not start with a bunch of nuts and bolts. They start with the car
as a whole. Of course, the car does not yet exist. Thus they start
with a conception of the intended car, more specifically, a set of
functional requirements for it: what it is supposed to do,
what performances are expected of it. To ascertain satisfactory
functional requirements is usually an important and difficult task,
especially when the system is very complex. We will return to it
later in the context of systems engineering.
After design engineers have arrived at a
satisfactory conception of the intended car, they proceed to
functional analysis. They decompose the conceived car into
functional subsystems with proper interfaces, e.g., a subsystem
for power and a subsystem for transmission, and how the
two are to work together. Then they further analyze a subsystem
into its interrelated components, until they get manageable parts
that can be specified to the last details. They have reached the
bottom of the Vee. Turning the corner of the Vee, the
thousands of parts are manufactured to specifications. They are
then tested, brought together, and assembled into larger and larger
subsystems, finally into a car ready for test drive. We are back at
the top of the Vee, but this time we have a concrete system –
a real car – instead of a mere conception of it.
Through functional decomposition, detailed
design, and physical assembly, engineers know and specify the whole,
its parts, and their interrelations clearly and at all compositional
scales. The subsystems at intermediate levels are crucial for
managing complex systems, for they enable engineers to introduce
complex details one step at a time.
In short, the systems approach
integrates analysis and synthesis. It is most effective in treating
complex phenomena, for it:
- encompasses both the holistic and modular
views
- grasps the arching features of the
whole system
- analyzes it into parts with proper
interfaces
- synthesizes knowledge about the parts
to understand the whole
- grasps the system and its details in many
levels
- decomposes a subsystem into
sub-subsystems, and so on, to the last details
- changes focus to view different levels
so the mind is not overwhelmed by complexity
- abstracts and hides information to focus
on a task :
- simplifies the system by treating its
parts as black boxes except their interfaces
- hiding information is not
discarding it; a black box can be opened at will
- makes a complex system more tractable
- a part can be studied or designed with
minimal interference from other parts
- controls damage and improves safety
- confines most effects of a defect
within a subsystem, preventing a system-wide collapse
The systems approach, in which one analyzes the
details of parts in order to know the whole, works not only in
engineering but also in natural science. Consider for example
systems biology, which aspires to study organisms as wholes. The
idea appeared in the 1960s, but lied dormant because the properties
of the biological constituents remained in the dark. Meanwhile,
biologists analyzed organisms into organs and tissues and cells and
molecules. Now molecular biologists have deciphered the genome of
many organisms, including humans. They have reached the bottom of
the Vee and gained tremendous amount of knowledge. Yet they
found it falls far short for understanding organisms. Then they
turn to synthesis and investigate how genes function in cells and
organisms as wholes. As they do so, systems biology springs alive.
Several universities, including Harvard here, are establishing
departments of systems biology. Four decades of analyzing organic
constituents turn systems biology from philosophy into science.
Strategic purviews of systems
engineering
Analysis and synthesis focus on internal
structures. A system in this focus is often, but not always,
regarded as closed and isolated from the rest of the world.
Idealization of closed systems is common in natural science;
controlled experiments are designed to realize the ideals as much as
possible. On the other hand, the ultimate purposes of engineering
systems, be they cars or bridges, are to provide services to some
external communities. Thus even when engineers focus on the
internal structures of a system, they leave at least a crack at the
door. The openings are represented in the system’s functions,
input, and output.
Given a set of functional requirements as ends,
the systems approach as analysis-synthesis searches for means to
best serve the ends. Systems engineering goes one step further to
engage in analysis of ends to determine what functions are
required of the system they are charged to design and build. The
ends are ultimately decided by various social groups that hold
interests in the intended system – clients, as engineers briefly
call them. To help their clients in defining feasible and desirable
ends, systems engineers negotiate and form working partnerships with
them.
Systems engineering arose after World War II to
design, develop, and manage complex technological systems with wide
societal impacts. Its purview is strategic, taking into account not
only a system’s life cycle from womb to tomb, but also the people
who are involved in various stages of the cycle. Among its missions
are:
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ends analysis: requirements engineering,
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life-cycle analysis: concurrent
engineering,
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multidisciplinary teamwork: organization
for development and management.
Functional requirements: What exactly do you want?
Many engineers avow that the most difficult
task of a complex engineering project is to get the requirements
right. The task depends crucially on the cooperation between
systems engineers and their clients. For complex systems involving
novel technologies, people are often uncertainty about what they
want, or they are unrealistic in their expectations. When the
clients divide into several groups, their desires often conflict.
It is incumbent on systems engineers to help their clients to
clarify their objectives: What exactly do you want? Can you afford
it? If not, what options do you have?
Systems engineers consider both technical and
contextual conditions. Consider an engineering project, the
development of a passenger jetliner. (There are good accounts, by
engineers and journalists, on the role of systems engineering in the
development of the Boeing 777 jetliner in the 1990s). The major
clients are the large airlines, each has its own requirements on
range, speed, payload, price, fuel economy, and so on. Besides
them, systems engineers also have to consult government aviation
policies, environmental regulations, airport facilities, economic
and demographic forecasts, and so on. To ensure that the designed
airplane can be manufactured cost effectively, traveled on
comfortably, operated safely, and maintained easily, systems
engineers elicit opinions from manufacturers, passengers, pilots,
flight attendants, and maintenance crews. They then have to make
tradeoff among various requirements, for instance between ease of
maintenance and lower manufacturing costs. Finally, having
coordinated relevant requirements, they design into the airplane
affordability, manufacturability, reliability, maintainability, user
friendless, disposability, and a host of other -abilities –
dispositional properties that they anticipate will function some
time in its lifespan. They design time into being,
which is the gist of concurrent engineering.
Stages of system
development
- elicitation of cradle-to-grave functional
requirements from stake holders
- definition of a system concept that
satisfies the life-long requirements
- successive functional modularization of
the system concept
- physical integration of components to
build the system
An airplane, or your car, is an inanimate
thing. But it is more than that, because it also carries interfaces
with its intended users. The gentle shape of your car’s seat, the
clean layout of its dashboard, are all designed with you in mind.
The philosopher Martin Heidegger once contemplated Van Gogh’s
painting of a pair of peasant’s shoe and saw in it the meaning of
human existence. Use and readiness-to-hand also shine in engineered
systems.
An airplane consists advanced materials, high
performance engines, onboard computers, navigation and
communications systems, and more. The development project of Boeing
777 alone involved some four thousand engineers with disparate
expertise from many companies. Just to coordinate their efforts is
a major management task for systems engineers. They are responsible
for assembling experts in many areas, organizing them into cohesive
multidisciplinary teams, integrating their knowledge, and bringing
them to bear on the project.
Project management is facilitated by the
systems approach of analysis-synthesis. In functional
decomposition, systems engineers specify only the functional
requirements of what a subsystem, for instance the onboard computer,
is supposed to do. Then they let software engineers to figure out
how to satisfy the requirements. In this way both parties can
utilize their expertise maximally and yet still corporate tightly.
They interact and negotiate on the requirements, but only where it
counts, so that they do not suffer from micromanagement and
counterproductive interferences.
Systems engineering combines technology
development with strategic management, which plans ahead with
long-term vision. Since the rise of large-scale industry and giant
corporations, engineers had been among leaders of strategic
management. They pioneered modern business administration in the
nineteenth century and occupy many top executive offices today.
Systems engineering attempts to articulate, rationalize, and develop
best managerial practices, especially in high-technology areas. It
originated in the Cold War and was revitalized in the 1980s, when
American industry faced stiff commercial competition from Japan.
The Japanese, especially Toyota Motors, are experts in systems
engineering. So Americans analyzed Japanese practices and
incorporated many of their innovations into systems engineering.
The abstraction of general principles from one culture and apply it
appropriately in another culture is a mark of scientific thinking,
which transcends narrow cultural bounds.
Systems engineering is thriving. MIT and
Stanford both created departments for it most recently.
Functional abstraction and systems
theories
Systems engineering, which stands at the
interface between engineering and management, is conspicuously
practical and down to earth. In contrast, systems theories, which
lie at the core of engineering science, are mathematical and rather
abstract. This in no way implies that systems theories are
impractical; they are practical in a general way. Connecting them
to systems engineering is the notion of function, through
which systems theories are applied to particular designs.
Adam Smith, whose Wealth of Nations
appeared in the same year as his friend Watt’s steam engine,
observed a relation between machines and abstract systems: “Systems
in many respects resemble machines. A machine is a little system,
created to perform, as well as to connect together, in reality,
those different movements and effects which the artist has occasion
for. A system is an imaginary machine invented to connect together
in fancy those different movements and effects which are already in
reality performed.” Smith’s remark adumbrated the abstraction from
concrete machineries prevalent in engineering systems theories.
In a purposive context, a thing has two kinds
of characteristic. The first are physical properties: its
materials, structures, and motions. The second are functional
characteristics: what it is for, what services it performs.
Systems theories abstract from physical properties and bring to
relief functional characteristics. Not surprisingly, systems
abstraction is absent from physical sciences and peculiar to
engineering, which aims to design systems that serve people.
We can distinguish two broad types of
concepts. Substantive concepts address what are, things and
their properties. Functional concepts address what for.
Functions tacitly point to a context, the larger environment in
which a system operates and provides services. Functional concepts
occur less in physics than in biology, especially evolutionary
biology, where natural selection selects adaptable functions.
Functional concepts are sort of controversial in natural sciences,
because they connote some sense of purpose. Evolutionary biologists
who evoke them often refer to engineering; there functional concepts
are intuitive, prominent, and successful. Among functional concepts
are dispositional properties, which indicate what a thing would do
under certain circumstances. Properties such as solubility and
flammability occur in physics. They are much more numerous in
engineering, whose cradle-to-grave considerations cover properties
such as affordability, reliability, durability, manufacturabiliy,
and a host of other abilities. Many of such dispositional
properties are kind of abstract and intangible. To treat them
property engineers have to introduce concepts to define them clearly
and performance metrics to measure them. Such concept formation is
a kind of scientific thinking.
Functions are paramount in engineering, because
engineered systems are designed for service. Thus it is no surprise
that they get the center stage of systems theory.
Information theory: an example of
systems theory
Examples of systems theories are control
theory, information theory, theories for estimation and signal
processing. Some systems theories, for example classical control,
existed before WWII. But like elsewhere in engineering, a turning
point occurred during the war. Mathematician Nobert Wiener
introduced two concepts into engineering, probability and
optimization, which revolutionized system theoretic thinking. His
monograph was called “yellow peril” by engineers, because it had a
yellow cover and because its difficult mathematics was overwhelming.
But one generation later, engineer scientists became totally
comfortable with abstract mathematics. Systems theories are highly
mathematical, and their theorem-proof format has more the flavor of
pure mathematics than the calculus and applied mathematics familiar
in physical theories.
Perhaps the most famous of systems theories is
the information theory introduced by Claude Shannon. He opened his
classic paper A mathematical theory of communication:
“The fundamental
problem of communication is that of reproducing at one point either
exactly or approximately a message selected at another point. We
wish to consider certain general problems involving communication
systems. To do this it is first necessary to represent the various
elements involved as mathematical entities, suitably idealized from
their physical counterparts.”
The paragraph highlights the essential
characteristics of a systems theory:
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Functionality: the theory is for
the sake of reliable communication. Reliability is a
purposive notion. Shannon gave a brief definition of it here,
and would introduce precise metric for it in the paper in terms
of probability of errors.
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Abstraction: the theory idealizes
from physical properties.
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Generality: the theory is concerned
not with particular communication systems but with general
principles underlying all kinds of reliable communication
systems. Focuses on the general and the particular distinguish
between engineering science and engineering design.
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Mathematical representation: the
functional relations among communication components are
represented in mathematical terms.
Take for example signal processing, a widely
applicable systems theory. A signal is something that carries
information, and signal processing is the transformation of signals
for efficient manipulation, transmission, or storage. Signals come
in a variety of physical media: acoustic, mechanic,
electromagnetic. Signal processors engage disparate physical
mechanisms: electric, electronic, piezoelectric. When you speak on
a phone, your information is carried in acoustic signals, which are
immediately transformed into electromagnetic signals, which go
through modulation, digitization, coding, multiplexing,
demultiplexing, decoding, and other signal processing procedures
before they are transformed back into acoustic signals heard by your
friend on the other end.
Signal processing theory abstracts from most
physical properties, retaining only a few essential features such as
the wavelength of signals. It aims to capture, in mathematical
terms, the functions of signal processors, how they transform
the forms of signals, for instance the function of
digitization transforms a signal from analog form to digital form.
Because it captures the fundamental principles covering a wide
variety of physical mechanisms, signal processing theory is
applicable everywhere from telecommunications to scientific
experiments.
Feedback control as an example of
functional abstraction
Because systems theories abstract from physical
properties, the functional relations they represent can be applied
to systems with disparate physical make up. Thus signal processing
theories apply equally to acoustic or electrical signals.
Mathematical representations enable engineers to manipulate abstract
forms, create novel functional relations, and realize them in
appropriate physical media.
We can see how system theories work in the case
of feedback control. Although the term “feedback control” was
introduced in the 20th century, feedback devices had been
made for many centuries. One famous example is Watt’s flyball
governor, which regulates the steam engine so that the flywheel
operates in a steady pace. In systems abstraction, we forget all
the physical mechanisms and concentrates on the functional relation
between a plant, the flywheel in this particular case, and the
controller, which is the flyball governor. The plant and the
controlled is each represented by a mathematical expression, F and
G.
Now let us add a twist, negative feedback,
where the feedback control is subtracted instead of added to
the input. Negative feedback was invented by Black for achieving
low distortion amplification of electrical signals, especially for
long distance telephony. Physically, it is totally different from
mechanical system. However, we can still represent the amplifier as
the plant, F, and a feedback control as G, the only difference is
that we have a negative sign here. And we get a simple algebraic
expression for the relation between the input and output through
negative feedback. The problem for signal amplification is that the
amplifier, F, is distortive. However, we can see that if the gain G
of the controller is larger, so that FG is much greater than one,
then the two Fs cancel out and the output is essentially independent
of behavior of the amplifier. When you put in different specific
forms of F and G, you can calculate the operational characteristics
of a specific feedback control system. But even without the
specifics, control theory can tell you a lot.
Employing different levels of abstraction to
represent complex phenomena, focusing on the topic of interest at
hand, is another common method in the natural science. It is used
here in engineering science.
Systems in engineering science,
design, and management
I have presented three notions of systems.
They play major roles in three engineering activities. Systems
theories with their systems abstraction are prominent in engineering
science, which seek general functional principles. The systems
approach as analysis-synthesis is widely practiced in engineering
design of particular systems. Systems engineering strives to
rationalize the management of large technical projects and their
interfaces with society at large. Their domains, however, are not
exclusive. Systems theories are routinely used in design and
management. Analysis-synthesis is a time-honored approach in
scientific research generally. The broad purviews of systems
engineering are sipping into many development projects.
Part of a talk presented at Department of the
History of Science, Harvard University
October 28, 2004.
by Sunny Y. Auyang
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