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Manfred comments on Separating the roles of theory and direct empirical evidence in belief formation: the examples of minimum wage and anthropogenic global warming - Less Wrong Discussion
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Comments (65)
Show me someone who makes predictions of the future by "just looking at the data," and I'll show you someone who's using a theory but not admitting it.
Yeah, in the AGW case it sounds like the question's more like "to what extent is your belief the result of climate models, and to what extent is it the result of a linear regression model?"
Theory also influences what data you consider in the first place. (Are you looking at your own local weather, global surface temperatures, stratospheric temperatures, ocean temperatures, extreme weather events, Martian climate, polar ice, or the beliefs and behavior of climatologists, and over what time scales and eras?) See also philosophy of science since at least Kuhn on theory-laden observation: http://plato.stanford.edu/entries/science-theory-observation/
The difference should be framed as: are you using a theory developed by fitting known data, or a theory developed from first principles?
I strongly disagree. "Fitting" data is not a theory-neutral process. As khafra points out, if you just have two time series, you can do linear regression to see if they seem correlated, and make predictions based off that. But for this to work requires lots of assumptions - one might even call it a 'theory' - about the world. For how this can go wrong, see the pirate theory of global warming.
Conversely, "first principles" as they exist in reality are usually grounded in experiment. This is most glaring in the case of climate models. What does their code implement? Conservation of mass? Experimental result. Heat transfer? Experimental result. Cloud formation? Experiment. Optical properties of gases, experiment, solar spectrum, experiment, black-body radiation, experiment, Earth's geography, experiment, seasonal cycles, experiment. This is all data! Using this data is just as much "just looking at the data" as linear regression.
Point taken, and I agree. I'll try to better formulate what I meant:
Some theories are developed using data about the system you want to study. E.g., past climate data.
And some theories are developed using data about other systems. Either similar but causally unrelated ones (e.g., greenhouse effect in an actual greenhouse), or models which are so simplified that there's a serious worry they may be too simplified to apply to the original system (e.g., black-body radiation). They also have the advantage that if they work on the system you want to study, then they let you explain it in terms of other things which you already understand.
On an abstract Bayesian level, they're all the same; we don't compartmentalize data about past climate from data about the optical properties of gasses. But for humans who work in different fields the difference matters.