(43-46)
Now, suppose you want to predict the weather. Then, you
need two basic types of information: (1) the current weather
and (2) how weather changes from one moment to the next.
You could attempt to predict the weather by creating a “model
world.” For example, you could overlay a globe of the Earth
with graph paper and then specify the current temperature,
pressure, cloud cover, and wind within each square. These are
your starting points, or initial conditions. Next, you could
input all the initial conditions into a computer, along with a
set of equations (physical laws) that describe the processes
that can change weather from one moment to the next.
Suppose the initial conditions represent the weather
around the Earth at this very moment and you run your
computer model to predict the weather for the next month in
New York City. The model might tell you that tomorrow will
be warm and sunny, with cooling during the next week and a
major storm passing through a month from now. But suppose
you run the model again, making one minor change in the
initial conditions, say, a small change in the wind speed
somewhere over Brazil. This slightly different initial
condition will not change the weather prediction for tomorrow
in New York City. But for next month’s weather, the two
predictions may not agree at all!
The disagreement between the two predictions arises
because the laws governing weather can cause very tiny
changes in initial conditions to be greatly magnified over time.
This extreme sensitivity to initial conditions is sometimes
called the butterfly effect: If initial conditions change by as
much as the flap of a butterfly’s wings, the resulting
prediction may be very different.
The butterfly effect is a hallmark of chaotic systems.
Simple systems are described by linear equations in which,
for example, increasing a cause produces a proportional
increase in an effect. In contrast, chaotic systems are
described by nonlinear equations, which allow for subtler and
more intricate interactions. For example, the economy is
nonlinear because a rise in interest rates does not
automatically produce a corresponding change in consumer
spending. Weather is nonlinear because a change in the wind
speed in one location does not automatically produce a
corresponding change in another location.
Despite the name, chaotic systems are not necessarily
random. In fact, many chaotic systems have a kind of
underlying order that explains the general features of their
behavior even while details at any particular moment remain
unpredictable. In a sense, many chaotic systems— like the
weather— are “predictably unpredictable.” Our understanding
of chaotic systems is increasing at a tremendous rate, but
much remains to be learned about them.
【題組】46. The author suggests that our knowledge of chaotic
systems
_____________ (A) will never allow us to make accurate predictions.
(B) requires more research by the scientific community.
(C) reveals detail that can be predicted quite accurately.
(D) has not improved very much over the years.
(43-46)
Now, suppose you want to predict the weather. Then, you
need two basic types of information: (1) the current weather
and (2) how weather changes from one moment to the next.
You could attempt to predict the weather by creating a “model
world.” For example, you could overlay a globe of the Earth
with graph paper and then specify the current temperature,
pressure, cloud cover, and wind within each square. These are
your starting points, or initial conditions. Next, you could
input all the initial conditions into a computer, along with a
set of equations (physical...
(43-46)
Now, suppose you want to predict the weather. Then, you
need two basic types of information: (1) the current weather
and (2) how weather changes from one moment to the next.
You could attempt to predict the weather by creating a “model
world.” For example, you could overlay a globe of the Earth
with graph paper and then specify the current temperature,
pressure, cloud cover, and wind within each square. These are
your starting points, or initial conditions. Next, you could
input all the initial conditions into a computer, along with a
set of equations (physical laws) that describe the processes
that can change weather from one moment to the next.
Suppose the initial conditions represent the weather
around the Earth at this very moment and you run your
computer model to predict the weather for the next month in
New York City. The model might tell you that tomorrow will
be warm and sunny, with cooling during the next week and a
major storm passing through a month from now. But suppose
you run the model again, making one minor change in the
initial conditions, say, a small change in the wind speed
somewhere over Brazil. This slightly different initial
condition will not change the weather prediction for tomorrow
in New York City. But for next month’s weather, the two
predictions may not agree at all!
The disagreement between the two predictions arises
because the laws governing weather can cause very tiny
changes in initial conditions to be greatly magnified over time.
This extreme sensitivity to initial conditions is sometimes
called the butterfly effect: If initial conditions change by as
much as the flap of a butterfly’s wings, the resulting
prediction may be very different.
The butterfly effect is a hallmark of chaotic systems.
Simple systems are described by linear equations in which,
for example, increasing a cause produces a proportional
increase in an effect. In contrast, chaotic systems are
described by nonlinear equations, which allow for subtler and
more intricate interactions. For example, the economy is
nonlinear because a rise in interest rates does not
automatically produce a corresponding change in consumer
spending. Weather is nonlinear because a change in the wind
speed in one location does not automatically produce a
corresponding change in another location.
Despite the name, chaotic systems are not necessarily
random. In fact, many chaotic systems have a kind of
underlying order that explains the general features of their
behavior even while details at any particular moment remain
unpredictable. In a sense, many chaotic systems— like the
weather— are “predictably unpredictable.” Our understanding
of chaotic systems is increasing at a tremendous rate, but
much remains to be learned about them.
【題組】46. The author suggests that our knowledge of chaotic
systems
(A) will never allow us to make accurate predictions.
(B) requires more research by the scientific community.
(C) reveals detail that can be predicted quite accurately.
(D) has not improved very much over the years.
修改成為
46. The author suggests that our knowledge of chaotic
systems
_____________(A) will never allow us to make accurate predictions.
(B) requires more research by the scientific community.
(C) reveals detail that can be predicted quite accurately.
(D) has not improved very much over the years.