lecture 3 : systems and ST
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this lecture introduces basic systems and
systems thinking concepts:
what is the method used in science and should
OR/IE use the same method?
subject – object duality
decision making in complex situations
efficacy – effectiveness - efficiency
what is a system?
what kinds of systems are there?
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what method is used in science?
Aristotle looked at the world as a living being:
parts of the world could only be understood in
terms of their relationship with each other and
with the whole
• this is a holistic (ie. systemic) and also a
teleological view of the world (teleology is the
doctrine that says there is a purpose to everything;
it explains phenomena by the purpose they serve
rather than by postulated causes)
• so ST dominated Western thought for more than
2000 years, even though it was not called ST
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• then came the Enligthenment that marked the
beginning of modernity, the modern and presnt
period of history
• modernity started with the emergence of
empirical sciences, when teleology was
replaced by Cartesian mechanism, based on
the philosophy of René Descartes (1596-1650)
• Isaac Newton (1642-1727) developed and
carried this type of thinking to great success;
he has had the greatest influence on the
shaping of the modern world, on how we live
and understand the world
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• the Newtonian perspective of the world is
reductive rather than holistic; it assumes that
analysis is the only means to gain knowledge
• reductionism is the reduction of all phenomena
to simple, unidirectional causal relationships
between variables rather than interactions that
can only be explained in terms of the
functioning of the whole
• the Oxford Dictionary says: reduction is the practice
of analysing and describing a complex phenomenon,
especially a mental, social, or biological
phenomenon, in terms of its simple or fundamental
constituents
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• this Newtonian, or the mechanistic
paradigm dominated science until the
middle of the 20th century and is still
alive
• the Oxford Dictionary says: paradigm is a
world view underlying the theories and methodology
of a particular scientific subject
• we can also define a paradigm as the set
of shared and formal assumptions about
the nature of reality and our knowledge
of it
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• thus, as quoted in Jackson (1):
“Each of us lives and works in organisations designed
from Newtonian images of the universe.
We manage by separating things into parts, we believe
that influence occurs as a direct result of force exerted
from one person to another,
we engage in complex planning for a world that we
keep expecting to be predictable, and we search
continually for better methods of objectively
perceiving the world (Wheatley, 1992, p. 6).”
•Newtonian thinking emphasises cause-and-effect
thinking, in contrast to systems thinking
•it is a hard view of the world and is based on the
assumption of subject-object duality
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subject-object duality
• Newtonian science is founded on the possibility of
objective knowledge acquired through independent
observation
• the central idea is that of subject-object dualism
• this refers to the separation of the observer (subject)
from the observed (object); if the observer can observe
independently of the observed, then observation can be
objective
• positivism and disciplinary sciences in general are
strongly rooted in the assumption of objectivity
• thus, positivism claims that scientific inquiry can be
objective, ie. strictly free of personal preferences and
value judgements and therefore able to provide true
knowledge
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• in the 20th century, positivist science and the
Newtonian paradigm came under attack, as in the
course of complexity studies
• for example:
– in quantum mechanics subatomic particles do not
exist as independent things; they come into being
and are observed only in relationship to something
else; observation influences the observed
– in biology it was understood that organisms are
adaptive and co-evolutionary rather than
mechanistic
– in chemistry, disorder in dissipative systems is now
seen as the source of new order, and growth is found
in disequilibrium, not in balance
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• these “underlying currents are a movement toward
holism, toward understanding the system as a
system and emphasising the relationships that exist
among seemingly discrete parts
• when we view systems from this perspective, we
enter an entirely new landscape of connections, of
phenomena that cannot be reduced to simple causeand-effect, and of the constant flux of dynamic
processes” (1)
• it also means that in systems, the observer inevitably
influences the observed since he must be part of the
system; absolute objectivity is therefore impossible,
subject-object dualism breaks down
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increased complexity
• despite these developments, the positivist – objectivist
- disciplinary attitude still persists in science
• by contrast, OR had an interdisciplinary character
when it was first practiced and quickly became aware
of the impossibility of value-free inquiry
• today even everyday decision making faces complexity:
– “Today's world has thus increased in complexity and
interdependence to a point where the traditional methods of
problem solving based on the cause-and-effect model cannot
cope any longer.” (2)
• the following examples illustrate the complexity that
cause unexpected and undesirable consequences to
outweigh expected benefits
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1.
the Aswan High Dam in Egypt
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increased incidence of schistosomiasis
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loss of fertile silt and the need for fertilisers
salinisation
loss of land and decline of sardine fisheries
2.
deterioration of urban transport
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3.
suburbanisation and increased car ownership
extension of the road network
reduced demand for public transport
increasing fares, declining service quality
further shift toward private transportation
traffic congestion
assessment of unit production costs
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maximising efficiency of each machine centre
Can result in high inventory carrying costs
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efficacy–effectiveness–efficiency
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efficacy : does the means work? e.g. adding
another machine to the assembly line may or may not
increase efficacy
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efficiency : is resource use minimised? ie.
maintaining effectiveness with fewer resources used
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effectiveness : will longerm goals be
attained?
concern about efficiency should not conflict
with effectiveness (e.g. reducing inventories to
save costs might cause loss of sales and revenue )
•
efficiency should complement effectiveness
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a counterintutive production example
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produce more of the higher margin item:
3 units of A, 2 units of B;
total profit = 3 X 90 + 2 X 60 = 390
produce more of the lower margin item:
2 units of A, 4 units of B
total profit = 2 X 90 + 4 X 60 = 420
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systems
• systems are everywhere; some varieties are:
natural systems: e.g. the solar system, a frog, a girl, an ecosystem
abstract systems: e.g. an algebra, the number system
social systems: e.g. a pruduction system, a human activity system
• the ontological (out there) vs. the epistemological (inside us)
view; e.g.
– an electric power supply system appears to be out-there
– it will include generators, transmission and distribution lines,
transformer stations etc.
– but does it also include rivers and the electricity pricing system?
• what the system will include or exclude will depend on how we
define the system, on the purpose of inquiry
• this can change for the same person too; e.g. a river system is not
the same thing to an engineer when he is working and when he is
vacationing
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• although hard systems thinking can sometimes
regard systems as out-there, it is often more
useful to regard systems as inside-us, mental
constructs
• system definitions are therefore necessarily
subjective, because they are influenced by:
– the purposes and the interests of the observer
– the Weltanschauung of the individual (each person
interprets the world in terms of his/her own experiences and
biases, ie. repeated patterns of experience lead to a complex
set of beliefs and values through which we perceive the world;
hence W is the taken-for-granted outlook of the world; a
formalised W shared by a group of people is a paradigm)
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“Suppose, for example, you were asked by the International
Olympic Committee (IOC) to conduct a broad systems study
of the future of the Olympic Games (…) It would be quickly
apparent that there is no single account of the Games as the
"system of concern" which would be generally acceptable:
that "system" would be very differently described (and
hence so would system objectives) by the IOC itself, by the
host city, by would-be host cities, by athletes, by athletes'
coaches, by officials, by spectators, by hot-dog sellers, by
sponsors, by television companies, by television viewers who
have no interest in athletics(…) This list could go on and on,
and this is what happens as soon as you move outside
technically defined problem situations and into human
problem situations. (…) [This] illustrates that multiple
conflicting objectives from multiple stakeholders are the
norm in human situations.” Checkland and Holwell (2)
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• in other words, reality can only be known
through our perceptions of it, and these
perceptions are necessarily shaped by our world
view (ie. weltanschauung)
• this means that subject-object duality cannot
be assumed to hold
• hence the only valid type of objectivity must be
consensual, ie. socially decided
• critical philosophy, unlike positivism, says
that in science and in systems thinking, truth
and validity claims can only be settled through
a social process of negotiation
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Dällenbach defines a system as follows:
1.
2.
3.
4.
5.
6.
A system is an organized assembly of components. 'Organized'
means that there exist special relationships between the
components.
The system does something, ie. it exhibits behaviours that are
unique to the system.
Each component contributes towards the behaviour of the system
and its own behaviour is affected by being in the system. No
component has an independent effect on the system. (A part that
has an independent effect and is not affected by the system is an
input. See (5) below.) The behaviour of the system is changed if any
component is removed or leaves.
Groups of components within the system may by themselves have
properties (1), (2) and (3), ie. they may form subsystems.
The system has an outside - an environment - which provides
inputs into the system and receives outputs from the system.
The system has been identified by someone to be of special interest
for a given purpose.
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• the relevant environment
– provides inputs to the transformation process
– receives outputs from the transformation process that
do not affect the system
– inputs can be uncontrollable or controllable (ie.
decision variables)
– outputs include measures of performance
• identification of the relevant environment requires
setting boundaries
• boundary judgements or boundary setting is
perhaps the most critical step in ST
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• we define the environment as that part of the world
that lies beyond our control
• but human activity systems do not have any real
boundaries
• we have to set the boundaries somewhere, so long as
we don’t forget that those that are temporarily set,
must be broadened endlessly
• failure to do this will result in what Churchman (3) calls
an environmental fallacy
• an environmental fallacy will be committed if we focus
our attention on one part of the system only and forget
about the larger system of which it is a part
• efficiency and effectiveness are again relevant here
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• for example:
– if we maximise the efficiency of each department of a
hospital, it does not necessarily follow that the hospital will
work effectively
– a focus on minimising inventory costs will lead to a fallacy
since inventories cannot be decided independently of
production, of marketing, of procurement, of distribution,
of maintenance etc.
• Ackoff (4) defines environmental fallacy as follows:
“Recall Peter Drucker’s observation of the difference between
doing things right (efficiency) and doing the right thing
(effectiveness). This distinction is fundamental. The righter
we do the wrong thing, the wronger we become. If we made an
error doing the wrong thing and correct it, we become
wronger. (…) It is much better to do the right thing wrong than
the wrong thing right”
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examples of systems
• a traffic system
• a motorcar
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all parts fit together and interact
so, is a car a system out-there?
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its emergent property requires a driver, a purpose
and a road network
cars can be different things to different people: a
means of transportation, a prestige symbol, a collection
item etc.
so a car is more meaningful as a human
conceptualisation rather than a system out-there
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–
• a saw-mill
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two important decisions in
systems thinking
• when defining a system we have to decide:
1. where to set the boundary, this is called
boundary setting, or a boundary
judgement, or closure
2. what level of detail, or resolution, or scale
to adopt, this is called separation of scales
• these are the two most important decisions in
systems thinking
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boundary judgements
• if the boundary is set too tight, there is the danger of
losing sight of important system parts and
interrelations
• if the boundary is set too broad then there will be too
many components and interrelations that will be
difficult to handle and understand
• an error committed by setting the boundary too tight is
called an environmental fallacy, this is the most
common and serious mistake made in IE/OR
• for example: how do we asses an inventory holding
cost? what environmental fallacies are likely to be
committed?
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hierarchies
irrelevant environment
environment for system 2
environment for system 1
suppliers
system 1: narrow
system of interest:
marketing
& sales
cost minimising
operations
customers
system 2 : wider system of interest
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separation of scale
the concept of “state”
• this is a fundamental concept that cannot be defined
any more than the concept of a “set” can be defined in
mathematics
• consider a physical system that transforms the single
input represented by the time function v(t), into the
single output represented by the time function y(t)
• if we know the structures and processes that make up
this system, then complete knowledge of v(t) over the
interval (-∞,t] is sufficient to determine y(t) over the
same time interval
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• however if the input is known only over the
time interval [to,t] then we need information at
some time t1, where to ≤ t1 < t, in order to
determine the output y(t) over the time
interval [to,t]
• this information constitutes the state of the
system at time t1 ; it consists of the levels of
all (structural and process) variables, or the
state variables at time t1 , (e.g the state
variables in dynamic programming; or the number of
customers in a B&D queue)
• in this sense, the state of the system is related
to the memory of the system
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• for another example of system-state,
consider the solution of a linear differential
equation with constant coefficients for t ≥ to
• once the form of the complete solution is
obtained in terms of arbitrary constants,
these constants can be determined by the
fact that the system must satisfy “boundary
conditions” at time to; no other information
is required
• these boundary conditions can be termed
the state of the system at time to
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• intuitively, the state of a system
separates its future from the past, so the
state contains alI the relevant
information concerning the past
• the state of a system is represented by a
vector showing the values of all state
variables at time t
• hence if the state vector is given for time
t, then we have all the information there
is to know about the system
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• for an example consider a system of display
consisting of 7x100 light bulbs that can allow 20
letters to be written by lighting some of the
bulbs
• this system will have 2700 ≈ 10210 different states
(note that the number of atoms in the universe
≈ 1073 , which is infinitesimally smaller)
• having such a huge state space is one reason
why many systems are complex
• fortunately not all the states of a system need
be relevant for decision making, in many cases
knowledge of aggregates or averages is
sufficient
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• for example,
– when studying traffic problems, we don’t need to
know the exact position and speed of all vehicles
that are on the road-network at all times; often a
knowledge of time-averages is good enough
– a civil engineer designing a bridge does not need to
know the state vector of all molecules that make up
the bridge, knowing aggregate properties such as
tensile and ductile strength etc. is sufficient
• this reduction in the number of relevant system
states is achived by separation of scales
• that is, by deciding what level of detail is
relevant for decision making
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• sometimes, in extreme cases the scale can be
so large that the system is defined as a black
box when,
– not all the detail of the transformation process is
needed, and
– a black box representation is sufficient
– which shows only the i/p and the o/p
e.g.
logs
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emergence
• a good alternative definition of a system can be given
in terms of emergence:
a system is a set of interrelated
components with emergent properties
• an emergent property usually appears at higher levels
of scale as a result of interactions at lower levels
• human activity systems are often established in order
to produce desired emergent properties (eg. a car-androad network  transportation)
• though they can also produce undesired emergent
properties (eg. the same network  noise and
pollution)
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references
1.
M.C.Jackson (2000) “Systems approaches to
management” Kluwer ( available as an e-book in METU
Library)
2.
3.
4.
M. Pidd (2004) “Systems modelling” Wiley
W. Churchman (1979) “The systems approach and its
enemies” Basic Books
R. Ackoff; J. Pourdehnad (2001) “On misdirected
systems” Systems research and behavioral science,
18, pp:199-205
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