Some lecture notes/ scribble on Model Thinking by Scott E. Page. Lecture Intro, Part 4: “Using and Understanding Data”. The lesson today is actually not so much in the course, it’s more about the course:
Models for using and understanding Data, breaks down to:
1. Understand patterns
2. Predict points
3. Produce bounds
5. Predict other
6. Inform data collection
7. Estimate hidden parameters
|=: This is a pretty list of features. However, where do they come from? How are they related? … Perhaps a model would be of help here? I mean sth like a common underlying model, where the above features can be explained with. Ok, it’s just the course intro. So, wait and see.
Got the lesson? => If you see a plain listing of any kind of stuff, always ask for the underlying model!
Try it, for example, at the next bullet point list in a power point presentation you’re confronted with. 🙂
Now, was this, so to say, a “meta modelling-lesson” (not to be confused with “meta-modelling lesson”)?