How Much of the World Is It Possible to Model?

a lot. and not very much. it depends.

my love of physics and quantum mechanics pushes me to generalize, abstract and model. you can get a lot out of a good model if you accept the limitations and constrain the scope of predictions, trust and explainability.

the use of an analogy feels similar to the constraints of how we model parts of our world. it works in some cases, but not at all. they can be abused to misinform in our world of short social media viral rage or they can be used to break down a complex concept and build a bridge for students on something they do understand already.

connecting this to the pitfalls of overconfidence and our use of LLMs was enlightening and opens an opportunity to celebrate our models and analogies but carve a path towards curiosity and humility.

This article explores the power and limitations of mathematical models in various fields, from neurosurgery and climate prediction to election forecasting and the spread of infectious diseases. It delves into the successes and failures of different modeling approaches, highlighting the challenges of balancing simplicity and accuracy, and the need to understand the underlying mechanisms of the phenomena being modeled. The article examines the role of data, technology, and theoretical assumptions in model creation, with a focus on the balance between generalizing into the unknown and remaining faithful to observed reality. It also discusses the increasing complexity and opacity of advanced models, such as those used in artificial intelligence, raising concerns about overconfidence and the risk of conflating correlation with causation.