To understand what I mean this video provides the most basic example
Science is based on predictions and experiments. In order to make a prediction, we require a model. This can be a mental model, mathematical model, etc. Models allow us to make predictions.
As we gain knowledge, our mental models improve. As these models become more accurate we are able to make more accurate predictions. These predictions allow us to make wise decisions.
This videos below provides a good distinction between hypotheses, theories, and models:
The reason wisdom is asymmetric is because we do not all have accurate models. Some work with mental models which are inaccurate. The predictions made from these models would also be inaccurate. Some have models which are very accurate and the predictions made from these models are more often correct.
Not that I mean to imply the social sciences are nearly as analytically robust or lean on scientistic nonsense, but it isn't all rubish. Herbet Blumer's "What's Wrong With Social Theory" still seems pretty good, unfortuantely, seems to be hidden behind paywalls that helps keep these bubbles inflated. But this link may work. JSTOR wanted $14 to download shaking my head. But it seems like much of the social sciences are full of what Blumer would define as "sensitizing concepts." Blumer seemed to emphasize the importance of bringing concepts into a verification process with teh empirical world so that errors inaccruracies would eventually be discovered and corrected--if possible--assuming there is is "unified model" as you seem to discuss in the previous post.
https://www.tu-chemnitz.de/wirtschaft/bwl5/forschung/forschungsseminar/downloads/1415/Blumer%20-%20What%20is%20wrong%20with%20social%20theory.pdf
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Wow...so amazing i love hearing the word of statistics, where we bring "fact from the figures".
Statistical Modeling deal with prediction and experimenting of data, be it secondary or primary data.
Thanks @dana-edwards for this amazing statistical modeling content.
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Pls, define 'wisdom' first.
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Note that all models are physical because all modelers are finite and all modeling is process limited to available resources. I think true/false 0/1 is asymptotic. 0 and 1 is as 'reachable' as the speed of light and the absolute zero or the planck or hubble scales. The relation thing-model is always of 'resemblance'. Euclidean geometry is 'like' the world, and it's 'laws' are neat form of compression, outputting good enough and cheap results to be used in practice. To 'model everything' ofc the modeler must BE at least as big (in Shannon terms) as the modeled. As big in complexity terms indeed, which implies also resources in the light of stronger algo = doing more with less. This gives some hope a part of the universe to grok it, as long as the algo which outputs universe is no longer than its full bitstring.
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