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Current models of the entire planetary ecosystem are admittedly quite primitive, but they often have proven to be predictive What have they predicted?

planetguy 14y ago
I don't know, but I also want to point out that "often proven to be predictive" is pretty weak cheese. I mean, I have a model for predicting the result of coin flips which is often predictive. It's called "heads".
- SwellJoe 14y ago
  Are you suggesting that models are not a useful scientific tool?
  - bgilroy26 14y ago
    Going from "We can be confident in our models" to "We can be confident in our planetary models" is a big step up in scale and complexity.
  - ars 14y ago
    Yes, somewhat. Models are a useful tool to see what happens when the science is known. They work well to check if you understand your theory well: You model it and experiment with it at the same time. If you model matches the real world then you are good to go. But if you try to make a model based only on your observations, without also being able to experiment, then that's not science, that's tautology. Your model will do whatever you want, and you have no way of knowing if it has anything to do with the real world because you can not run multiple experiments. In some cases if you wait long enough you may randomly get enough variation in the real world to be confident in your model (for example stellar evolution). But using a model to predict something you have never seen in the real world? That's not science, that's speculation.
    - anthonyb 14y ago
      I think you're confused about how models work in a scientific sense. They can be used for prediction, and if they couldn't they wouldn't have any value at all. Of course they sometimes don't line up with the real world; why do you think there are a metric shit-ton of scientists in Antarctica? In terms of the article, the theory of climate is pretty well understood at this stage, and the models are sound at the macro scale. The hard parts are now in factoring in relatively small changes to make the numbers line up even better, and working through the implications of their predictions... which is what the article seems to be doing.
      - ars 14y ago
        > They can be used for prediction, and if they couldn't they wouldn't have any value at all. Correct - they can't be used for prediction, and therefor they have no value at all (for prediction - they are useful for validation). > Of course they sometimes don't line up with the real world Then you improve them, but you don't try to predict from them. By improving them you increase your knowledge, but you don't predict from them because the only thing that comes out of a model is what you put into it. You learn nothing about a real world from a model. You learn about your understanding of the real world from the model. (They better you know the real world the better you model - the model teach you about your own knowledge. It has zero value in teaching you about the world since nothing can come out of it that you didn't put into it.) > why do you think there are a metric shit-ton of scientists in Antarctica? To do research about the real world? Is this supposed to be a big revelation? Not sure waht you were trying to imply here. > The hard parts are now in factoring in relatively small changes to make the numbers line up even better Well obviously. The details are always the hard part - the details is also where all the interesting stuff happens. You can make the best model in the world, and then miss the one tiny detail that only happens outside the domain of your model, and all the predictions are worthless.
        anthonyb 14y ago
        If what you are saying is true, then every scientific and mathematical formula would be worthless, since after all, you just get out of it what you put into it. e=mc^2? f=ma? v=ir? Yep, no bearing on the real world or any predictive power whatsoever.
  - a5seo 14y ago
    http://www.amazon.com/Models-Behaving-Badly-Confusing-Illusion-Reality-Disaster/dp/1439164983 http://www.amazon.com/Models-Behaving-Badly-Confusing-Illusi... "A model...[is] really much more of a metaphor, an attempt to find an analogy between something you want to understand and something to really do understand, either heuristically or by a theory." from http://www.econtalk.org/archives/2012/03/derman_on_theor.html http://www.econtalk.org/archives/2012/03/derman_on_theor.htm... - very good interview.
- ktizo 14y ago
  El Niño is the main thing that springs to mind when thinking about predictive powers of (short term) climate modelling. They are getting reasonably good at that. Also, your coin flip is not a predictive model any more than claiming that using the same numbers every week on the lottery is a predictive model. For one thing, it makes no attempt to model anything, and for another, it has no attempt to be predictive, it just attempts to be right nearly half the time (edge), through understanding of the likely odds, which is a different thing altogether.

planetguy 14y ago

I don't know, but I also want to point out that "often proven to be predictive" is pretty weak cheese. I mean, I have a model for predicting the result of coin flips which is often predictive. It's called "heads".

SwellJoe 14y ago
Are you suggesting that models are not a useful scientific tool?
- bgilroy26 14y ago
  Going from "We can be confident in our models" to "We can be confident in our planetary models" is a big step up in scale and complexity.
- ars 14y ago
  Yes, somewhat. Models are a useful tool to see what happens when the science is known. They work well to check if you understand your theory well: You model it and experiment with it at the same time. If you model matches the real world then you are good to go. But if you try to make a model based only on your observations, without also being able to experiment, then that's not science, that's tautology. Your model will do whatever you want, and you have no way of knowing if it has anything to do with the real world because you can not run multiple experiments. In some cases if you wait long enough you may randomly get enough variation in the real world to be confident in your model (for example stellar evolution). But using a model to predict something you have never seen in the real world? That's not science, that's speculation.
  - anthonyb 14y ago
    I think you're confused about how models work in a scientific sense. They can be used for prediction, and if they couldn't they wouldn't have any value at all. Of course they sometimes don't line up with the real world; why do you think there are a metric shit-ton of scientists in Antarctica? In terms of the article, the theory of climate is pretty well understood at this stage, and the models are sound at the macro scale. The hard parts are now in factoring in relatively small changes to make the numbers line up even better, and working through the implications of their predictions... which is what the article seems to be doing.
    - ars 14y ago
      > They can be used for prediction, and if they couldn't they wouldn't have any value at all. Correct - they can't be used for prediction, and therefor they have no value at all (for prediction - they are useful for validation). > Of course they sometimes don't line up with the real world Then you improve them, but you don't try to predict from them. By improving them you increase your knowledge, but you don't predict from them because the only thing that comes out of a model is what you put into it. You learn nothing about a real world from a model. You learn about your understanding of the real world from the model. (They better you know the real world the better you model - the model teach you about your own knowledge. It has zero value in teaching you about the world since nothing can come out of it that you didn't put into it.) > why do you think there are a metric shit-ton of scientists in Antarctica? To do research about the real world? Is this supposed to be a big revelation? Not sure waht you were trying to imply here. > The hard parts are now in factoring in relatively small changes to make the numbers line up even better Well obviously. The details are always the hard part - the details is also where all the interesting stuff happens. You can make the best model in the world, and then miss the one tiny detail that only happens outside the domain of your model, and all the predictions are worthless.
      - anthonyb 14y ago
        If what you are saying is true, then every scientific and mathematical formula would be worthless, since after all, you just get out of it what you put into it. e=mc^2? f=ma? v=ir? Yep, no bearing on the real world or any predictive power whatsoever.
- a5seo 14y ago
  http://www.amazon.com/Models-Behaving-Badly-Confusing-Illusion-Reality-Disaster/dp/1439164983 http://www.amazon.com/Models-Behaving-Badly-Confusing-Illusi... "A model...[is] really much more of a metaphor, an attempt to find an analogy between something you want to understand and something to really do understand, either heuristically or by a theory." from http://www.econtalk.org/archives/2012/03/derman_on_theor.html http://www.econtalk.org/archives/2012/03/derman_on_theor.htm... - very good interview.
ktizo 14y ago
El Niño is the main thing that springs to mind when thinking about predictive powers of (short term) climate modelling. They are getting reasonably good at that. Also, your coin flip is not a predictive model any more than claiming that using the same numbers every week on the lottery is a predictive model. For one thing, it makes no attempt to model anything, and for another, it has no attempt to be predictive, it just attempts to be right nearly half the time (edge), through understanding of the likely odds, which is a different thing altogether.