I mostly agree I think—but, how do you teach/train to get good at finding the right ontology? Bayesian reasoning is at least something that can be written down and taught, there’s rules for it.
Recognizing the importance of choosing and comparing models / concepts might be a prerequisite concept. People learn this in various ways … When it comes to choosing what parameters to include in a model, statisticians compare models in various ways. They care a lot about predictive power for prediction, but also pay attention to multicollinearity for statistical inference. I see connections between a model’s parameters and an argument’s concepts. First, both have costs and benefits. Second, any particular combination has interactive effects that matter. Third, as a matter of epistemic discipline, it is important to recognize the importance of trying and comparing frames of reference: different models for the statistician and different concepts for an argument.
I mostly agree I think—but, how do you teach/train to get good at finding the right ontology? Bayesian reasoning is at least something that can be written down and taught, there’s rules for it.
Recognizing the importance of choosing and comparing models / concepts might be a prerequisite concept. People learn this in various ways … When it comes to choosing what parameters to include in a model, statisticians compare models in various ways. They care a lot about predictive power for prediction, but also pay attention to multicollinearity for statistical inference. I see connections between a model’s parameters and an argument’s concepts. First, both have costs and benefits. Second, any particular combination has interactive effects that matter. Third, as a matter of epistemic discipline, it is important to recognize the importance of trying and comparing frames of reference: different models for the statistician and different concepts for an argument.