No. Correct models are good. Or rather, more correct models, applied properly, are better than less correct models, or models applied wrongly.
All those examples, however, are bad:
Calories in / Calories out is a bad model because different sources of calories are metabolized differently and have different effects on the organism. It is bad because it is incomplete and used improperly for things that it is bad at.
It stays true that to get output from a mechanism, you do have to input some fuel into it; CICO is good enough to calculate, for example, how many calories one should eat in, say polar climates where one needs many thousands of them to survive the cold; that is not an argument against using a Correct model, it is an argument for using a model that outputs the correct type of data for the problem domain.
Being unwilling to update is bad, yes, that is a problem with the user of the model, not a problem with the model one is using. Do you mean that knowing and using the best known model makes one unwilling to update on the subsequent better model? Because that is still not a problem with using a Correct model.
That is an entirely different definition of Bad. “Bad for the person holding the model (provided they can’t pretend to hold the more socially-accepted model when that is more relevant to staying alive and non-ostracized)” is absolutely not the same thing as “Bad at actually predicting how reality will react on inputs”.
That is also a problem with the users of the model, both those making the prediction and those accepting the predictions, in domains that the model is not good at predicting.
Still not a problem due to using a Correct model. Also, bad outcomes for who? If immigration is good for the immigrants in some respsect and bad for them in some other respect, that is neither all good or all bad; if it is good for some citizens already there in some ways but bad in others (with some things being a lot of good for some citizens and some other things being a little bad, while also some things being a little good and other things being a lot of bad for other citizens), then the Correct model is the one that takes into account the entirety of all the distributions of the good and the bad things, across all citizens, and predict them Correctly.
( and now please in the name of Truth let this thread not devolve into an object-level discussion of that specific example >_< )
I think the title is a little bit misleading, and perhaps he didn’t put much emphasis on this, but it seems he isn’t claiming correct models are generally bad, just that there are also possible downsides to holding correct models and it’s probably a good idea to be aware to these flaws when applying these models to reality.
Also, it seems to me as he is defining ‘correct model’ as a model in which the reasoning is sound and can be used for some applications, however does not necessarily fully describe every aspect of the problem.
No. Correct models are good. Or rather, more correct models, applied properly, are better than less correct models, or models applied wrongly.
All those examples, however, are bad:
Calories in / Calories out is a bad model because different sources of calories are metabolized differently and have different effects on the organism. It is bad because it is incomplete and used improperly for things that it is bad at. It stays true that to get output from a mechanism, you do have to input some fuel into it; CICO is good enough to calculate, for example, how many calories one should eat in, say polar climates where one needs many thousands of them to survive the cold; that is not an argument against using a Correct model, it is an argument for using a model that outputs the correct type of data for the problem domain.
Being unwilling to update is bad, yes, that is a problem with the user of the model, not a problem with the model one is using. Do you mean that knowing and using the best known model makes one unwilling to update on the subsequent better model? Because that is still not a problem with using a Correct model.
That is an entirely different definition of Bad. “Bad for the person holding the model (provided they can’t pretend to hold the more socially-accepted model when that is more relevant to staying alive and non-ostracized)” is absolutely not the same thing as “Bad at actually predicting how reality will react on inputs”.
That is also a problem with the users of the model, both those making the prediction and those accepting the predictions, in domains that the model is not good at predicting.
Still not a problem due to using a Correct model. Also, bad outcomes for who? If immigration is good for the immigrants in some respsect and bad for them in some other respect, that is neither all good or all bad; if it is good for some citizens already there in some ways but bad in others (with some things being a lot of good for some citizens and some other things being a little bad, while also some things being a little good and other things being a lot of bad for other citizens), then the Correct model is the one that takes into account the entirety of all the distributions of the good and the bad things, across all citizens, and predict them Correctly.
( and now please in the name of Truth let this thread not devolve into an object-level discussion of that specific example >_< )
I think the title is a little bit misleading, and perhaps he didn’t put much emphasis on this, but it seems he isn’t claiming correct models are generally bad, just that there are also possible downsides to holding correct models and it’s probably a good idea to be aware to these flaws when applying these models to reality.
Also, it seems to me as he is defining ‘correct model’ as a model in which the reasoning is sound and can be used for some applications, however does not necessarily fully describe every aspect of the problem.