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Information Technology in a Global Society
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Computer Models and Simulations
Modeling & Simulation
Simulation in general is to pretend that one deals with a
real thing while really working with an imitation. In
operations research the imitation is a computer model of the
simulated reality. A flight simulator on a PC is also a
computer model of some aspects of the flight: it shows on
the screen the controls and what the "pilot" (the youngster
who operates it) is supposed to see from the "cockpit" (his
armchair).
Why use models?
To fly a simulator is safer and cheaper than the real
airplane. For precisely this reason, models are used in
industry commerce and military: it is very costly, dangerous
and often impossible to make experiments with real systems.
Provided that models are adequate descriptions of reality
(they are valid), experimenting with them can save money,
suffering and even time.
Why use simulations?
Systems that change with time, such as a gas station where
cars come and go (called dynamic systems) and involve
randomness. Nobody can guess at exactly which time the next
car should arrive at the station, are good candidates for
simulation. Modeling complex dynamic systems theoretically
need too many simplifications and the emerging models may
not be therefore valid. Simulation does not require that
many simplifying assumptions, making it the only tool even
in absence of randomness.
Computer Models
Computer modeling is only about three decades old and yet it
has become an important industry, generating hundreds of
millions of dollars of revenues annually.
As
computers have become faster, cheaper, and more widely
available, computer models have become commonplace in
forecasting and public policy analysis, especially in
economics, energy and resources, climate, weather and other
natural systems, demographics, and other crucial areas.
Though
not all of us are going to be model builders, we all are
model users, regardless of whether we know it (or like it).
Because computer models are so poorly understood by most
people, it is easy for them to be misused, accidentally or
intentionally.
Fortunately, everyone is already familiar with models. We
all use models—mental models—every day. We are able to
imagine results of our actions and make predictions based on
our experience. However our decisions and actions are based
not on the real world, but on our mental images of that
world, of the relationships among its parts, and of the
influence our actions have on it.
Advantages and Disadvantages of Computer Models
In
theory, computer models offer improvements over mental
models in several respects:
§
They
are explicit; their assumptions are stated in the written
documentation and open to all for review.
§
They
infallibly compute the logical consequences of the modeler's
assumptions.
§
They
are comprehensive and able to interrelate many factors
simultaneously.
A
computer model that actually has these characteristics has
powerful advantages over a mental model. In practice,
however, computer models are often less than ideal:
§
They
are so poorly documented and complex that no one can examine
their assumptions. They are black boxes.
§
They
are so complicated that the user has no confidence in the
consistency or correctness of the assumptions.
§
They
are unable to deal with relationships and factors that are
difficult to quantify, for which numerical data do not
exist, or that lie outside the expertise of the specialists
who built the model.
Because of these possible flaws, computer models need to be
examined carefully by potential users. But on what basis
should models be judged? How does one know whether a model
is well or badly designed, whether its results will be valid
or not? How can a prospective user decide whether a type of
modeling or a specific model is suitable for the problem at
hand? How can misuses of models be recognized and prevented?
A
model must have a clear purpose, and that purpose should be
to solve a particular problem. A clear purpose is the single
most important ingredient for a successful modeling study.
Of course, a model with a clear purpose can still be
incorrect, overly large, or difficult to understand. But a
clear purpose allows model users to ask questions that
reveal whether a model is useful for solving the problem
under consideration.
The
usefulness of models lies in the fact that they simplify
reality, putting it into a form that we can comprehend. But
a truly comprehensive model of a complete system would be
just as complex as that system and just as inscrutable.
Feedback
Feedback (sometimes called a feedback loop) is the
term we use to describe information (data) that is created
by operating a model or simulation which is then put into
the model that created it. This feedback affects the
conditions on which decisions were made when the model was
created.
Ignoring feedback can result in policies that generate
unanticipated side effects or are diluted, delayed, or
defeated by the system. An example is the construction of
freeways in the 1950s and 1960s to alleviate traffic
congestion in major US cities. In Boston it used to take
half an hour to drive from the city neighborhood of
Dorchester to the downtown area, a journey of only a few
kilometres. Then a highway network was built around the
city, and travel time between Dorchester and downtown
dropped substantially.
But
there's more to the story. Highway construction led to
changes that fed back into the system, causing unexpected
side effects. Due to the reduction in traffic congestion and
commuting time, living in outlying communities became a more
attractive option. Farmland was turned into housing
developments or paved over to provide yet more roads. The
population of the suburbs soared,
as people moved out of the center city. Many city stores
followed their customers or were squeezed out by competition
from the new suburban shopping malls. The inner city began
to decay, but many people still worked in the downtown
area—and they got there via the new highways. The result?
Boston has more congestion and air pollution than before the
highways were constructed, and the rushhour journey from
Dorchester to downtown takes half an hour, again.
Computer models increase the limited ability of the human
mind to determine the best course of action. In order to
function, however, computer models must be based on
simplified versions of the real world so the most we can
hope from them is an approximation of how people ought to
behave. To model how people actually behave requires a very
different set of modeling techniques, these are called
computer simulations.
Computer Simulation
The
Latin verb simulare means to imitate or mimic. The
purpose of a simulation is to mimic the real system so that
its behavior can be studied. The simulation is a laboratory
replica of the real system, a microworld. By creating
a representation of the system in the laboratory, a modeler
can perform experiments that are impossible, unethical, or
prohibitively expensive in the real world. Simulations of
physical systems are commonplace and range from wind tunnel
tests of aircraft design to simulation of weather patterns
and the depletion of oil reserves. Economists and social
scientists also have used simulations to understand how
energy prices affect the economy, how corporations mature,
how cities evolve and respond to urban renewal policies, and
how population growth interacts with food supply, resources,
and the environment.
Computer simulations are "what if' tools, for example an
aircraft flight simulator might allow a pilot to find out
“what if” the plane was hit by lightning, without the
obvious danger of the real event happening.
Limitations of
Simulations
A
computer simulation is only as good as its accuracy to the
reality it represents. For example a flight simulator
which does not mimic the behavior of a real plane accurately
is of little value to a pilot. Adequately representing the
physical system is usually not a problem; the physical
environment can usually be portrayed with whatever detail
and accuracy is needed for the simulation’s purpose.
Simulations do have their limitations, however. Most
problems occur in the behaviors, reactions and
outcomes that are programmed into a simulation by its
human creators. Most of what we know about the world is
descriptive, qualitative, difficult to quantify, and has
never been recorded. Yet such information is crucial for
understanding and modeling complex systems. In setting up
simulations, some modelers limit themselves to only hard
data, stuff that can be measured directly and can be
expressed as numerical data. In doing so they limit the
likelihood of a simulation being close the reality. We
cannot determine scientifically everything that occurs in
life and it is this fact that makes it impossible to mimic
it precisely. We can only strive to create simulations which
come as close to reality as humanly possible, while at the
same time remaining aware that a simulation is not real life
and that there is a danger in placing our total trust
anything we learn from it.
Conclusion
Despite the limitations of modeling, there is no doubt that
computer models and simulations can be and have been
extremely useful foresight tools. Well-built models offer
significant advantages over the often faulty mental models
we currently use.
Computer modeling is an essential part of the process of
gaining information rather than a technology for producing
answers. The success of a computer model depends on our
ability to create and learn from it. Properly used, computer
models can improve the mental models upon which decisions
are actually based and contribute to the solution of the
pressing problems we face. |
