Transcript lecture 2: systems thinking - Middle East Technical University

lecture 3 : systems and ST
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this lecture introduces basic concepts of
systems and systems thinking :
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 viewed the world as a living entity:
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 teleological thinking dominated Western
thought for more than 2000 years, even though
it was not called systems thinking
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• then came the Enligthenment which marked
the beginning of modernity, or “the age of
reason”
• modernity started with the emergence of
modern science, and epecially empirical
sciences, when teleology was replaced by
Cartesian mechanism, based on the philosophy
of René Descartes (1596-1650)
• Isaac Newton (1642-1727) developed this type
of thinking with great success; his work shaped
the modern world, it determined 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 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, even in the everyday
language we use
• the Oxford Dictionary says: paradigm is a
world view underlying the theories and
methodology of a particular scientific
subject
• alternatively, 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”
•Newtonian thinking emphasises cause-andeffect thinking, in contrast to systems thinking
•it is a hard view of the world and is based on
the assumption that objectivity is possible
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subject-object duality
• Newtonian science is founded on the possibility of
objective knowledge acquired through independent
observation
• this belief is based on the assumption of subject-object
dualism
• which says that the observer (subject) can be separated
from from the observed (object); ie. if the observer
makes observations independently of the observed, then
observation will be objective
• positivism is strongly rooted in this assumption; it 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, positivism and the Newtonian
paradigm came under criticism
• it was observed, for example, that:
– in quantum mechanics subatomic particles do not
exist as independent things; they come into being
and are observed only in relationship to something
else; so observation influences the observed and
subject-object duality fails
– 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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• this new awareness led a movement toward holism,
toward understanding phenomena as a system and
emphasising the relationships that exist among
seemingly discrete parts
• when we view the world 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 means that in systems, the observer inevitably
influences the observed since he must be part of the
system; absolute objectivity is therefore impossible,
• furthermore most systems that are important for
understanding the world are complex
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increased complexity
• despite these developments, the positivist – objectivist
- disciplinary approach persists in science
• by contrast, OR has had an interdisciplinary character
from the start and became aware early on 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
causes unexpected and undesirable consequences that
outweigh expected benefits
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1. the Aswan High Dam in Egypt
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loss of fertile silt and a new need for fertilisers
salinisation
loss of land and decline of sardine fisheries
increased incidence of schistosomiasis
2. deterioration of urban transport
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suburbanisation and increased car ownership
extension of the road network
reduced demand for public transport
increased fares, declining service quality
further shift toward private transportation
traffic congestion
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3. assessment of unit production cost
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assessing the performance of each machine
centre on the basis of average unit costs:
the lower the unit cost, the more efficient the
machine centre
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this assessment works fine in simple plants
with one machine centre
in a complex set up with several centres
working together it can lead to
accumulation of work-in-process inventory
and profit loss
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a counterintutive production example
produce more of the highmargin item:
3 units of A, 2 units of B;
total profit = 3(90) + 2(60) = 390
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produce more of the low-margin
item:
2 units of A, 4 units of B
total profit = 2(90) + 4(60) = 420
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a counterintutive experiment: the Hawthorne
effect
– does better lighting improve productivity at the
Hawthorne plant?
– a experiment designed with an experimental and a
control group of workers
• productivity of the experimental group improved with
better lighting
• but the productivity of the control group also improved
• productivity increased even further under poor lighting
– the Hawthorne effect is an example of what we call
reactive effects
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efficacy–effectiveness–efficiency
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efficacy : does the means work? it is the capacity
to produce an effect; e.g. aspirin is efficacious
against head-ache but not against a broken bone
effectiveness : will long term goals be attained?
efficiency : is resource use minimised? ie.
maintaining effectiveness with fewer resources
used
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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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, or human activity systems: e.g. a production system,
• the epistemological (inside us) view is often more useful than the
ontological (out there) view ; e.g.
– an electric power supply system may first appear to be out-there,it
includes 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 as it is when he is
vacationing
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Dällenbach defines a system as follows:
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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.) 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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• so for each system we can identify a relevant
environment that
– provides inputs to a transformation process
– and receives outputs from the transformation
process
• inputs can be uncontrollable or control inputs
(these are represented as decision variables in
mathematical models)
• outputs include measures of performance
• identification of the relevant environment
requires setting boundaries
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some examples
• a traffic system
• a motorcar
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all parts fit together and interact
the motorcar provides transportation, something that
none of its parts can
is this a system out-there?
it may look like that but transportation also requires a
driver, a purpose and a road network
furthermore cars can be different things to different
people: a means of transportation, a prestige symbol or
a collection item
so a car may be better understood as a conceptual
system rather than a system out-there
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Table 3-1 Three different systems views for a sawmill
Systems view
Industrial engineer
Purpose of viewing
entity as a system
study physical layout of equipment,
product handling, &
diff. operating rules
assess financial
return on investment
study effect on costs
of different cutting
patterns to meet
given demand
System components
• buildings, yards,
equipment,
vehicles
• operators
• logs in yards
• intermediate
products
• subsystems. such
as procurement of
logs, production,
warehousing, marketing, finance
• funds invested
• processing
subsystems
• intermediate
product stocks
• cutting operations
• moving of cuts
• drying of cuts
• planing of cuts
• storage
• purchasing of logs
• conversion of logs
• storage of logs
• sales of logs
• control of funds
• subsystem product
conversions
• storage of intermediate products
Activities of system
MS analyst
Relationships
between components
• sequencing of
tasks
• location of fixed
equipment
• feasible comb inations of cutting
patterns
• subsystem outputs
become inputs to
other subsystems
• communications
between
subsystems
• financial aspects
• subsystem outputs
become inputs to
other subsystems
• feasible cutting
combinations
• financial aspects
Inputs from
environment
• types of logs
• supplies (oil, fuel)
• processing rates &
capacities
• operating rules
• funds
• personnel
• product demands
• commercial laws
• pricing policy
• log availabilities
• cost data
• processing rates &
capacities
• operating rules
• product demands
projections for
• net profit
• cash flows
• return on
investment
• market share
projections for total
operating costs to
meet customer
demands
wealth and
production capacity
at time t into wealth
and production
capacity at time t+ I
production capacity,
logs available, and
customer demands
into total operating
cost
Outputs to
environment
Transformati on
process of system
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Owners
• finished products
• by-products (sawdust, off-cuts)
• processing
capacity
• bottlenecks
• equipment cap.
use
logs into finished
products and operating statisti cs
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a sawmill:
different things
to different
people
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• there exists a hierarchy of systems
– the sawmill firm is embedded in a system of regional
sawmills all sharing the same forest resources
– the system of regional sawmills is embedded in the
national wood processing industry etc.
• in general the containing system exercises some
control over the contained system; by setting the
objectives, monitoring performance and having
control over crucial resources
• the controlling system is then referred to as
– the wider system of interest,
while the contained system becomes
– the narrow system ofinterest
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• for example, if the sawmill cost-minimizing system
is the narrow system of interest, then the sawmill
profit-maximizing system and its environment are
its wider system of interest
• the advantage of viewing two is that their
relationships are shown in their correct context
– it may show that improvements in the performance
of the narrow system requires action to be taken in
the wider system
– similarly, the relationships between various inputs
into the narrow system are clarified; for example all
labour costs in the cost-minimisation system of the
sawmill may depend on the union contract signed in
the wider system
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system 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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• although hard systems thinking can sometimes
regard systems as out-there, it is often more
useful to regard systems as inside-us, as
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 which are necessarily
shaped by our world view (ie. weltanschauung)
• this means that subject-object duality cannot
be assumed to hold in social systems
• hence the only valid type of objectivity must be
consensual, ie. socially decided
• this means that unlike the “scientific
objectivity” claim of positivism, systems
thinking accepts that truth and validity can
only be decided dialectically through a process
of negotiation
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critical considerations in systems
thinking
• in order for systems thinking to be effective we
have to be careful when we decide:
1. where to set the boundary
this is called boundary setting, or a boundary
judgement
2. what level of detail, or scale to adopt
this is called scale setting or separation of scales
• these are the only two devices that will help
us deal with complexity
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boundary setting
• supposing we define the environment as that part
of the world that lies beyond our control, it is still
not clear how far our control extends
• if the boundary is set too tight, there is the danger
of leaving out important system parts and
interrelations
• if the boundary is set too wide then there will be
too many components and interrelations that will
be difficult to handle and understand
• the best we can do is to set the boundary and make
sure not to forget that such boundaries must later
be widened or narrowed as necessary
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• failure to do this will result in what Churchman (3)
calls an environmental fallacy
• more specifically, 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
• 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
• an environmental fallacy is often equivalent to
confusing efficiency with effectiveness
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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 and doing the right thing. 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” Why?
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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 , (remember 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)
• 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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large or coarse scale
high level
small or fine scale
low level
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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 normally 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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