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Skimming the transcript, I can’t help but feel like this field needs to study some signal processing ideas. They use a word like noise, but never use a word li

Skimming the transcript, I can’t help but feel like this field needs to study some signal processing ideas. They use a word like noise, but never use a word like filter, or attentuation. Even their use of the acronym BIN is unfortunate, since binning is a very important concept when dealing with the frequency domain.

wenc 7y ago
Can you say more about what type of attenuation would be possible? The noise models in physical systems are fairly simple/characterizable (y = f(x) + e, where e ~ P and P is some stable distribution, or e = a z-transform model), whereas in social sciences, the "noise" component is actually a catch-all/residual for whatever is unknown (e is unknown or unstable). It seems to me that it would difficult to apply any kind of signal processing techniques but I could be wrong.
- milesvp 7y ago
  And this might be the very problem. Noise is a very well defined term in information theory. If political forecasting is throwing around the term without applying it correctly they’re gonna have a bad time. At the end of the day though, any time you have time series data you can apply filters to smooth and shape your data. I don’t understanding how they’re modeling their data. There’s a good chance they’re doing some kind of frequency modeling where they’re counting correct predictions. It definitely sounds like they’re doing some stochastic modeling when they start talking about percentage predictions. You can definitely shape frequency domain as well with filters, though I havn’t quite thought through how the stochastic aspects might interact. Keep in mind, filters are very basic, and even something as common as averaging data is a low-pass filter. As is fitting to a curve. This all acts to attenuate the signal we care about without also attenuating the noise. Though, again, if someone isn’t being rigorous about what constitutes noise, then no amount of filtering will actually help... I’m also sorry if this thread isn’t very insightful. I’ve been having my nose rubbed in signal processing at work for the last 2 months, and it’s all I can see everywhere I look. I see parrallels everywhere that may not be there. You’re also very correct about the simplicity of physical models versus social sciences. It may just be that trying too hard to apply basic information theory at models that are almost impossible to create in the first place is a fools errand.
  - topologistics 7y ago
    > Noise is a very well defined term in information theory. If political forecasting is throwing around the term without applying it correctly they’re gonna have a bad time. Class is a very well defined concept in software development. Are political science people going to have a bad time because they "throw around" the word class and use it in an economic sense? There are only so many possible finite words that can be created from vocalizable letter combinations, there is bound to be overlap across disciplines.
    - j88439h84 7y ago
      I'm not sure they're using "noise" any differently
  - j88439h84 7y ago
    Wikipedia says > In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion. That sounds like what the interviewee is talking about. What distinction do you have in mind?
pottertheotter 7y ago
I’m about to turn in my PhD dissertation for a PhD in one of the business disciplines. I’ve found this to be very common for academic researchers in finance and accounting. Most have no experience outside academia, and business silos itself. For instance, we have econometrics instead of statistics. Which means we have different terms for just about anything in statistics. And, through discussing my work with my sibling, who is an accomplished statistician, our cutting edge econometrics is usually 20+ years old statistics models that are all but abandoned by the rest of the world.
- bonoboTP 7y ago
  It's definitely an issue across many fields and I don't have any other explanation than pride and "confidence" issues. A similar thing is AI and stats which have only really converged in the 1990s and are still quite separate communities, or reinforcement learning and control theory which investigate overlapping issues from different angles but haven't communicated for a long time. Well then what holds someone back from learning state-of-the-art stats and making a name for themselves in econometrics?

wenc 7y ago

Can you say more about what type of attenuation would be possible? The noise models in physical systems are fairly simple/characterizable (y = f(x) + e, where e ~ P and P is some stable distribution, or e = a z-transform model), whereas in social sciences, the "noise" component is actually a catch-all/residual for whatever is unknown (e is unknown or unstable). It seems to me that it would difficult to apply any kind of signal processing techniques but I could be wrong.

milesvp 7y ago
And this might be the very problem. Noise is a very well defined term in information theory. If political forecasting is throwing around the term without applying it correctly they’re gonna have a bad time. At the end of the day though, any time you have time series data you can apply filters to smooth and shape your data. I don’t understanding how they’re modeling their data. There’s a good chance they’re doing some kind of frequency modeling where they’re counting correct predictions. It definitely sounds like they’re doing some stochastic modeling when they start talking about percentage predictions. You can definitely shape frequency domain as well with filters, though I havn’t quite thought through how the stochastic aspects might interact. Keep in mind, filters are very basic, and even something as common as averaging data is a low-pass filter. As is fitting to a curve. This all acts to attenuate the signal we care about without also attenuating the noise. Though, again, if someone isn’t being rigorous about what constitutes noise, then no amount of filtering will actually help... I’m also sorry if this thread isn’t very insightful. I’ve been having my nose rubbed in signal processing at work for the last 2 months, and it’s all I can see everywhere I look. I see parrallels everywhere that may not be there. You’re also very correct about the simplicity of physical models versus social sciences. It may just be that trying too hard to apply basic information theory at models that are almost impossible to create in the first place is a fools errand.
- topologistics 7y ago
  > Noise is a very well defined term in information theory. If political forecasting is throwing around the term without applying it correctly they’re gonna have a bad time. Class is a very well defined concept in software development. Are political science people going to have a bad time because they "throw around" the word class and use it in an economic sense? There are only so many possible finite words that can be created from vocalizable letter combinations, there is bound to be overlap across disciplines.
  - j88439h84 7y ago
    I'm not sure they're using "noise" any differently
- j88439h84 7y ago
  Wikipedia says > In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion. That sounds like what the interviewee is talking about. What distinction do you have in mind?

pottertheotter 7y ago

I’m about to turn in my PhD dissertation for a PhD in one of the business disciplines. I’ve found this to be very common for academic researchers in finance and accounting. Most have no experience outside academia, and business silos itself. For instance, we have econometrics instead of statistics. Which means we have different terms for just about anything in statistics. And, through discussing my work with my sibling, who is an accomplished statistician, our cutting edge econometrics is usually 20+ years old statistics models that are all but abandoned by the rest of the world.

bonoboTP 7y ago
It's definitely an issue across many fields and I don't have any other explanation than pride and "confidence" issues. A similar thing is AI and stats which have only really converged in the 1990s and are still quite separate communities, or reinforcement learning and control theory which investigate overlapping issues from different angles but haven't communicated for a long time. Well then what holds someone back from learning state-of-the-art stats and making a name for themselves in econometrics?