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You can't do science without speculation. Science often models things it has never seen in the real world. Sometimes, when it becomes possible to make a measur

by ktizo 14y ago

You can't do science without speculation. Science often models things it has never seen in the real world. Sometimes, when it becomes possible to make a measurement, the model is found to be accurate. Like in the classic xkcd "Science. It Works, Bitches." cartoon about the microwave background radiation. - http://xkcd.com/54/ http://xkcd.com/54/ Models do not just do whatever you want. Many unexpected behaviours turn up in models and some of them can be almost impossible to know the future behaviour of in advance of running them, even when you know all the input states. In science, models are often what you use when the science isn't known, as you can use them as a guide to pick up on interesting things to go and look at. Engineering is usually where you use models when the science is known. [edit] And economics is where you use models when the science isn't known, and then you worship them and hope that money falls out.

ars 14y ago
> You can't do science without speculation. That's not true. It's a fallacy that the first step in the scientific method is formulating a hypothesis. It's completely unnecessary. The first step is "let's see what happens". You do not need any speculation or hypothesis first. That comes later - after you have collected your data then you try to understand and predict. > Sometimes, when it becomes possible to make a measurement, the model is found to be accurate. And for more often the model is wrong. But you have no idea if it's wrong or right if you can not test the real world. > microwave background radiation That's not a model, and the fact the you think it is makes we wonder. That's an exact mathematical representation of the phenomena. A model is imprecise, it includes as many parameters as possible, but by necessity can not include everything since the world is too complex. > Models do not just do whatever you want. Many unexpected behaviours turn up in models and some of them can be almost impossible to know the future behaviour of in advance of running them, even when you know all the input states. That's called Chaos. And the interesting thing about Chaos is that tiny changes in the input (for example what decimal precision you use) cause large changes in the output. If your model is chaotic then it's utterly useless for any conclusions whatsoever because it's completely impossible for you to enter the input with the same level of precision as the real world. Chaos is fun to look at, but pretty useless for prediction. There's a second thing possible called emergent behavior. But that too can not be modeled without understanding the real world first. What you do is keep changing the model till it matches the real world, then pull out the seemingly simple rules that cause complex behavior. But the model will fail as soon as you go outside the domain it was built in. Just because something acts the same way every time in the limited circumstances you tried does not mean it will keep doing so forever. That's a common extrapolation fallacy. So again, useless for prediction since prediction by definition puts you in a circumstance you have not yet seen. > In science, models are often what you use when the science isn't known, as you can use them as a guide to pick up on interesting things to go and look at. Operative word: To go and look at. Not to draw conclusions from. Engineering uses models to validate the assumptions, Science uses models to verify understanding. In no field are (should) models be used to draw conclusions.
- ktizo 14y ago
  It is true, you cannot do science without speculation. And it doesn't matter if you speculate before or after gathering a particular set of data, speculation is still required. I didn't say that you shouldn't test your model, was just pointing out that you can model something based on an incomplete understanding and it can still be considered part of the scientific process before you have gone and checked your results. It can even be science even when there is currently no known way of checking the results, as long as you can point to a reasonable path that might lead you there. Chaotic models are used in prediction. Regularly. Computational fluid dynamics wouldn't exist as a discipline without this. And I never claimed that you should draw your final conclusions from a model, just that they can be a fantastically useful tool for exploring the unknown. They can also be a trap if you trust the model more than the data, but that isn't the fault of modelling.

by ktizo 14y ago

ars 14y ago

> You can't do science without speculation. That's not true. It's a fallacy that the first step in the scientific method is formulating a hypothesis. It's completely unnecessary. The first step is "let's see what happens". You do not need any speculation or hypothesis first. That comes later - after you have collected your data then you try to understand and predict. > Sometimes, when it becomes possible to make a measurement, the model is found to be accurate. And for more often the model is wrong. But you have no idea if it's wrong or right if you can not test the real world. > microwave background radiation That's not a model, and the fact the you think it is makes we wonder. That's an exact mathematical representation of the phenomena. A model is imprecise, it includes as many parameters as possible, but by necessity can not include everything since the world is too complex. > Models do not just do whatever you want. Many unexpected behaviours turn up in models and some of them can be almost impossible to know the future behaviour of in advance of running them, even when you know all the input states. That's called Chaos. And the interesting thing about Chaos is that tiny changes in the input (for example what decimal precision you use) cause large changes in the output. If your model is chaotic then it's utterly useless for any conclusions whatsoever because it's completely impossible for you to enter the input with the same level of precision as the real world. Chaos is fun to look at, but pretty useless for prediction. There's a second thing possible called emergent behavior. But that too can not be modeled without understanding the real world first. What you do is keep changing the model till it matches the real world, then pull out the seemingly simple rules that cause complex behavior. But the model will fail as soon as you go outside the domain it was built in. Just because something acts the same way every time in the limited circumstances you tried does not mean it will keep doing so forever. That's a common extrapolation fallacy. So again, useless for prediction since prediction by definition puts you in a circumstance you have not yet seen. > In science, models are often what you use when the science isn't known, as you can use them as a guide to pick up on interesting things to go and look at. Operative word: To go and look at. Not to draw conclusions from. Engineering uses models to validate the assumptions, Science uses models to verify understanding. In no field are (should) models be used to draw conclusions.

ktizo 14y ago
It is true, you cannot do science without speculation. And it doesn't matter if you speculate before or after gathering a particular set of data, speculation is still required. I didn't say that you shouldn't test your model, was just pointing out that you can model something based on an incomplete understanding and it can still be considered part of the scientific process before you have gone and checked your results. It can even be science even when there is currently no known way of checking the results, as long as you can point to a reasonable path that might lead you there. Chaotic models are used in prediction. Regularly. Computational fluid dynamics wouldn't exist as a discipline without this. And I never claimed that you should draw your final conclusions from a model, just that they can be a fantastically useful tool for exploring the unknown. They can also be a trap if you trust the model more than the data, but that isn't the fault of modelling.