I am still finding it difficult to understand how the focus on causality of mapmaking is more helpful than examining the intent to summarize (which encompasses which information gets thrown away based on what domain of prediction the map is created for) and the (pretty pure bayesean) accuracy of predictions.
I think “intent to summarize” + “accuracy of predictions” is basically the whole story. What I want is a theory that can talk about both, at the same time.
Causality of mapmaking matters mainly because of the second piece: the map-making process is what determines how accurate the predictions will be. (The Bottom Line is an example which highlights this.)
I am still finding it difficult to understand how the focus on causality of mapmaking is more helpful than examining the intent to summarize (which encompasses which information gets thrown away based on what domain of prediction the map is created for) and the (pretty pure bayesean) accuracy of predictions.
I think “intent to summarize” + “accuracy of predictions” is basically the whole story. What I want is a theory that can talk about both, at the same time.
Causality of mapmaking matters mainly because of the second piece: the map-making process is what determines how accurate the predictions will be. (The Bottom Line is an example which highlights this.)