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Understanding Chaos Theory and Its Relevance to Forecasting Error, Essays (university) of Economics

The concept of chaos theory, focusing on its dynamic and non-linear characteristics, and discusses its relevance to forecasting error. The text highlights the importance of updating forecasting models to keep up with dynamic changes and the significance of calibrating forecasting errors. Additionally, it touches upon the weather channel's bias towards forecasting higher chances of rain to mitigate the 'boy who cried wolf' effect.

What you will learn

  • How does data inaccuracy impact forecasting models based on Chaos Theory?
  • What are the two key features of Chaos Theory?
  • What is the 'boy who cried wolf' effect, and how does it relate to weather forecasting?

Typology: Essays (university)

2019/2020

Uploaded on 03/02/2020

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Christopher McSweeny
Chaos Theory was a fascinating point in this chapter because it stood out in a different light
than what Malcolm had explained form Jurassic Park all those years ago. As silver explains it, it has
two key distinctive features, it is dynamic or ‘ever-changing’, and the systems are non-linear. This
can be seen in numerous systems within the world, but weather forecasting is the best example of
this, because the atmosphere is always changing and moving so a continuous update on a forecasting
model is necessary to keep up with its dynamic change. If a forecaster did not update the model, it
would rapidly change very quickly because one wrong forecast would turn into an even worse
forecast. This also is important to note with data inaccuracy as well. As silver points out with
Loren’s team forecasting program, slight errors in data can create a big problem and this only grows
over time with forecasting if it is wrong in the first place, it grows at a more rapid rate as you
continue to try and model with that data. I was thinking to myself, what we are learning in class that
pertains to this topic and I think its perfectly relevant to forecasting error. The importance of
calibrating your forecasting error is ever more important because if you don’t it is more likely that
your errors may become the same or worse over time.
I also found it interesting that the Weather Channel instinctively held a bias based on
consumer response to their forecasting models. They intentionally forecasted higher chances of rain
when rain had a chance to happen intentionally to ensure that if it did rain, the consumer (the
viewer), was informed and made an informed decision. This makes sense because of the boy who
cried wolf is very relevant to where if the weatherman didn’t forecast rain, and then it rained, people
are more apt to remember it than if they had done the opposite. If the weatherman forecasted rain,
and it hadn’t rained, the consumer was more likely to forget about it. This is definitely an eye opener
to understand perceptual biases moving forward.

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Christopher McSweeny Chaos Theory was a fascinating point in this chapter because it stood out in a different light than what Malcolm had explained form Jurassic Park all those years ago. As silver explains it, it has two key distinctive features, it is dynamic or ‘ever-changing’, and the systems are non-linear. This can be seen in numerous systems within the world, but weather forecasting is the best example of this, because the atmosphere is always changing and moving so a continuous update on a forecasting model is necessary to keep up with its dynamic change. If a forecaster did not update the model, it would rapidly change very quickly because one wrong forecast would turn into an even worse forecast. This also is important to note with data inaccuracy as well. As silver points out with Loren’s team forecasting program, slight errors in data can create a big problem and this only grows over time with forecasting if it is wrong in the first place, it grows at a more rapid rate as you continue to try and model with that data. I was thinking to myself, what we are learning in class that pertains to this topic and I think its perfectly relevant to forecasting error. The importance of calibrating your forecasting error is ever more important because if you don’t it is more likely that your errors may become the same or worse over time. I also found it interesting that the Weather Channel instinctively held a bias based on consumer response to their forecasting models. They intentionally forecasted higher chances of rain when rain had a chance to happen intentionally to ensure that if it did rain, the consumer (the viewer), was informed and made an informed decision. This makes sense because of the boy who cried wolf is very relevant to where if the weatherman didn’t forecast rain, and then it rained, people are more apt to remember it than if they had done the opposite. If the weatherman forecasted rain, and it hadn’t rained, the consumer was more likely to forget about it. This is definitely an eye opener to understand perceptual biases moving forward.