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They are highly distinct. Compare reporting the temperature tomorrow as a mode of all temperatures in November at your location, with a climate & weather simul
by mjburgess 2y ago
They are highly distinct.
Compare reporting the temperature tomorrow as a mode of all temperatures in November at your location, with a climate & weather simulation involving: cloud layers, the ocean, etc.
The former is likely to be vastly more predictively accurate than the latter, but explains nothing.
Explanatory models are often less predictively accurate than these (weakly inductive) predictive models. Their purpose is to tell us how reality works, and that provides some insight as to when we can adopt merely predictive approaches. This is because merely predictive models capture accidental features of measurement which hold up for awhile in some environments, that we wouldn't wish to explain.
Without explanatory insight we find merely predictive models catastrophically collapse, and are otherwise, highly fragile. Eg., consider the performance of "predict the mode" in a snow storm.
If you want a midly formal analysis of the difference: explanatory models quantify over causal properties of reality, describe their relationship, and provide necessary inferential methods for deducing conclusions from models. They permit arbitrary simulation across all relevant measuring systems.
Merely predictive models quantify over historical measurements, assume similarity conditions across them, and assume future similarity to past cases. They provide only extremely weak inferential grounds for any inference. They can offer only repetition of one kind of measure, and cannot simulate the state of other relevant measuring devices or in different environments of measurement.
Explanatory models describe reality. Predictive models describe the measurement device you happened to use, in the environment you specifically used it in.
- nialse 2y agoI enjoyed your conversation and just want to chip in that there are as many definitions of science and knowledge as there are philosophers. One don’t have to have only one definition, but usually one have to adhere to the ones within the realm of ones scientific paradigm to be accepted and to develop the science. Normal science as Kuhn called it.
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- mjburgess 2y agoIf you look at the history of these accounts of science though, they are "pre modern" in a negative sence. We didnt have formal methods of causal analysis to the 1920s, and it took til the 80s to have a real robust formalism and account of causal analysis. Before, experimenters would "have in their heads" the causal knowledge of how to conduct and interpret these results -- but this was never formalised, or given explicitly. So accounts of science before this "causal revolution" of the late 20th C. are broken, and based on a philosopher's literal reading of scientific experiments (and the like) with little undersatnding of how the experimenters actaully thought about them -- and philosophers often doctrinally opposed to entertaining these thoughts seriously. Today the partition between science/engineering, prediction/explanation, etc. can be given on much more robust grounds, and there's few philosophers of science working that adopt positions wholly at odds with my account. What you find from ML-ists is humean conceptions which ideologically honour their curve-fitting methods with equivalent status to actual experimentation and explanation
- naasking 2y ago> The former is likely to be vastly more predictively accurate than the latter, but explains nothing. It actually explains quite a bit, most importantly that weather is cyclical with only statistically minor variations around the mode. This predictive model is also very specific, rather than general. There are plenty of "explanations" that are also not predictive, like that "Thor creates thunder". Explanations and predictive models that are both general and minimize parameters are better, and they are interchangeable.
- mjburgess 2y agoAs I've said, merely predictive models do not quantify over the features of reality. A weakly inductive model is simply this: the next case will be some average of the prior cases because we assume unknown aspects of the environment will ensure it is so. An explanatory model tells you why. It says: there are planets, gravity, molecules, mountains, germs, atoms, and so on. The semantics of explanations are causal properties of reality, and their relationship. Explanations do not have "parameters" to "minimise". The universal law of gravitation is not a curve fit to data. There was never a dataset of F,M,m,r; netwon did not fit any parameters -- there are no such parameters.
- naasking 2y agoExplanations absolutely do have parameters, called premises, and we absolutely do try to minimize them. There's a whole principle about it called Occam's razor.
- mjburgess 2y agoThose are propositions with existential quantifications, not parameters, and we do not minimise them We use comparisons over the richness of existential commitments in theory comparison, but NOT in the creation of theories nor in their specification
- naasking 2y agoThese are all distinctions without a difference when analyzing theory descriptions under a unified framework, like ordering them by Kolmogorov complexity, eg. a Turing machine description is equivalent to a universal Turing machine+a suitably encoded tape.