- Statistically, more models tends to be better (in the same sense that more information is better) under the assumption that people can tell better from worse.
- I like how you talk about _applying_ models instead of _using_ models. It is an easy one word change that (to me at least) emphasizes the choice points of: (a) selecting the model; (b) selecting parameters; (c) interpretation.
- All models (by definition) are unrealistic in some way. If a model operates at a useful level of abstraction, inaccuracies at lower levels may be acceptable. In fact, such inaccuracies may be the key to making the model tractable and efficient.
- Statistically, more models tends to be better (in the same sense that more information is better) under the assumption that people can tell better from worse.
- I like how you talk about _applying_ models instead of _using_ models. It is an easy one word change that (to me at least) emphasizes the choice points of: (a) selecting the model; (b) selecting parameters; (c) interpretation.
- All models (by definition) are unrealistic in some way. If a model operates at a useful level of abstraction, inaccuracies at lower levels may be acceptable. In fact, such inaccuracies may be the key to making the model tractable and efficient.