*What models can and cannot achieve as a mental model.* This reminds me of how [[First Principles Thinking]] Helps us to avoid the complications of believing ourselves. [[reflection]] gives us an opportunity to ask ourselves if we indeed are aware. [[Blind Spot]] is a broken model we have of ourselves, because we are the only ones who cannot see [[your leaky face]]. Similar to: [[What is simple is always wrong, what is not is unusable]] It's kind of like [[don't believe everything you think]] **2.3  Parsimony** Since all models are wrong the scientist cannot obtain a "correct" one by excessive elaboration. On the contrary following [William of Occam](https://en.wikipedia.org/wiki/Occam%27s_Razor "Occam's Razor") he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity. ** This is a way of thinking about models because we can use models poorly or well. Often we try to oversimplify models to get to times and places where we can deal with complexity. However, the oversimplification, although it feels good, doesn't account for the real world. We need to be present and observing in the real world to more deeply understand our relationship with it, and how [[Systems]] work. However, this is like the idea that you cannot know both the location and speed of an electron at the same time. This is important because [[modern ancient mismatch]] means our brain cannot fully account for how we interact with the increasingly abstract and complex world. ### What would the opposite argument be? - We have to live, so do your best to pay attention to the world around you. You have to make models. ## Sources: Box, George E. P. (1976), ["Science and statistics"](http://www-sop.inria.fr/members/Ian.Jermyn/philosophy/writings/Boxonmaths.pdf) (PDF), _[Journal of the American Statistical Association](https://en.wikipedia.org/wiki/Journal_of_the_American_Statistical_Association "Journal of the American Statistical Association")_, **71** (356): 791–799, [doi](https://en.wikipedia.org/wiki/Doi_(identifier) "Doi (identifier)"):[10.1080/01621459.1976.10480949](https://doi.org/10.1080%2F01621459.1976.10480949). [wikipedia link to quote](https://en.wikipedia.org/wiki/All_models_are_wrong#cite_note-2)