Modeling & Simulation
After some consideration regarding a meaningful way of putting
System, Model, and Simulation in an appropriate perspective I
arrived at the following distinction.
Modeling and Simulation is a discipline for developing a level
of understanding of the interaction of the parts of a system,
and of the system as a whole. The level of understanding which
may be developed via this discipline is seldom achievable via
any other discipline.
A system is understood to be an entity which maintains its existence
through the interaction of its parts. A model is a simplified
representation of the actual system intended to promote understanding.
Whether a model is a good model or not depends on the extent to
which it promotes understanding. Since all models are simplifications
of reality there is always a trade-off as to what level of detail
is included in the model. If too little detail is included in
the model one runs the risk of missing relevant interactions and
the resultant model does not promote understanding. If too much
detail is included in the model the model may become overly complicated
and actually preclude the development of understanding. One simply
cannot develop all models in the context of the entire universe,
of course unless you name is Carl Sagan.
A simulation generally refers to a computerized version of the
model which is run over time to study the implications of the
defined interactions. Simulations are generally iterative in there
development. One develops a model, simulates it, learns from the
simulation, revises the model, and continues the iterations until
an adequate level of understanding is developed.
Modeling and Simulation is a discipline, it is also very much
an art form. One can learn about riding a bicycle from reading
a book. To really learn to ride a bicycle one must become actively
engaged with a bicycle. Modeling and Simulation follows much the
same reality. You can learn much about modeling and simulation
from reading books and talking with other people. Skill and talent
in developing models and performing simulations is only developed
through the building of models and simulating them. It's very
much a learn as you go process. From the interaction of the developer
and the models emerges an understanding of what makes sense and
I am repeatedly amazed at the ability of my models to point out
my own ignorance. Through the activity of developing the model
and then simulating it, the simulation says, "Based on your
model and your set of assumptions, reality is absurd!" Often
times the model is grossly incorrect, and other times the model
produces great leaps in my understanding of how things actually
work, and, quite often contrary to common sense. It is an amazingly
wonderful journey my models and I are undertaking.
Peter Senge, in "The Fifth Discipline: The Art & Practice
of the Learning Organization" talks about two types of complexity,
detail and dynamic. Detail complexity is associated with systems
which have many component parts. Dynamic complexity is associated
with systems which have cause and effect separated by time and
or space. The understanding is that it is dynamic complexity that
we have great difficulty dealing with because we are unable to
readily see the connections between the parts of the system and
their interactions. One of the great values of simulation is its
ability to effect a time and space compression on the system,
essentially allowing one to perceive, in a matter of minutes,
interactions that would normally unfold over very lengthy time
periods. This is probably best demonstrated by an example. The
following example is an elaboration of one of the introductory
models in the ithink documentation from isee Systems.
Consider a consulting company which has 120 employees. These 120
employees are composed of 60 rookies and 60 professionals. The
company wishes to maintain the total number of employees at 120
so it hires a new rookie for each professional who quits. Rookies
don't quit! Professionals quit at a rate of 10 per month and it
takes 6 months to develop a professional from a rookie. Additionally,
the company bills out rookies at $10k/month and professionals
at $15k/month. All 120 employees are fully applied (I know it's
a pipe dream).
An ithink model for this system might look like the following:
If you run this model you find it exists in essentially a steady
state, and is about as exciting as watching paint dry!
Now, in the 10th month the company notices its revenue has
dropped from $1.5m/month to $1.35m/month and it wonders what has
happened. And where do you think it looks for the problem? All
around the 10th month of course. And what does it find? The company
finds that it still has 120 employees, yet there are now 30 professionals
and 90 rookies. A most puzzling situation!
As it turns out, there was an organizational policy change made
in month 3 which seemed to annoy professionals more than in the
past, and the quit rate jumped from 10 to 15 professionals a month.
The system, with it's built in hiring rule, essentially an auto
pilot no thought action, hired one rookie for each professional
that quit. What this one time transition in quit rate actually
did was set off a 6 month transition within the organization leading
to a new equilibrium state with 30 professionals and 90 rookies.
The following graph represents this transition.
Thus, one of the real benefits of modeling and simulation is
its ability to accomplish a time and space compression between
the interrelationships within a system. This brings into view
the results of interactions that would normally escape us because
they are not closely related in time and space. Modeling and simulation
can provide a way of understanding dynamic complexity!
The model used for this example was done in ithink. [modsimrp.zip,
not the Answer
theWay of Systems
Copyright © 2004 Gene Bellinger