success is good. It may be too early
to tell. As a concentrated research
effort, this work has been under-
way only a year.

 But let me explain how we
began.

 Three years ago, we solved a
major problem for a high-rise office
building project with a computer.
We determined how high it
should be built for maximum eco-
nomic return.

 We had clients who wanted to
build on a very choice site in
downtown Houston. It was to con-
tain a home office as well as gen-
eral rentable office space. Our
client's charge was "Tell us the
optimum building size for maxi-
mum economic return."

 The answer was complicated, but
possible. We needed data in three
areas: business economics, con-
struction costs, and the implica-
tions of height on the building's
efficiency. We were able to formu-
late the data and with a com-
puter's help, we rapidly calculated
the return on investment for build-
ings from 15 to 50 stories. Inci-
dentally, in this instance, 32
stories was the answer we found.

 This success encouraged us and
we have pursued many other appli-
cations. At present we are working
with several other approaches
which will affect design. The most
promising appears to be simu-
lation.

Models
 Simulation is the art of model
making and testing. A model (or a
simulator) is a device which, in
some way, can be made to act like
a part of the real world.

 Of course a model can be a dia-
gram, a girl in a new fashion, a
JANUARY, 1967


cardboard physical replica of a
building, or a numerical structure.
But all have one purpose to imi-
tate something. A computer im-
plemented simulator is no differ-
ent.

 Normally we think of models as
a physical tangible entity. It's not
necessarily so. We can use num-
bers as the materials with which
to build the model. In the high
rise project, we built a model of
the economic activity of 35 differ-
ent buildings and predicted which
would be the most profitable.

 Now we are trying to build a
model of a university to test its
growth and functioning over the
next 10 years and to see how it
would respond to varying design
criteria.

 Our approach is this. When we
are asked to develop a master plan
for a college or university, we must
first establish potential growth and
determine how the institution uses
its facilities. Precise answers to


these two issues require processing
enormous quantities of informa-
tion. Then we must find ways to
"grow" the campus. Each new
building causes a department to
move. The vacated space is filled
by another department and even-
tually the effect ricochets through-
out the campus.

 We are now working, assisted
by an EFL grant, with Hewes,
Holz, and Willard of Cambridge,
Massachusetts and Duke Univer-
sity to develop a series of programs
which will simulate this affect.
The programs will show the need
for future facilities, help Duke use
existing space more effectively,
help us determine proper location
of new buildings, simulate pedes-
trian circulation and eventually
simulate the physical evolution of
the institution.

 Of course this is a very ambi-
tious effort but there are other
applications which are very simple
although also very helpful. Perhaps


 ."-- --- z_.

"All I know is that every hour it quits for ten
minutes and the cup of coffee disappears."
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