Lecture Notes on Model Thinking III

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
4. Retrodict
5. Predict other
6. Inform data collection
7. Estimate hidden parameters
8. Calibrate

|=: 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.

Have fun

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”)?

About modelpractice

Modeling Theory and Abstraction Awareness in strive for scientific rigour and relevance to information systems engineering.
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2 Responses to Lecture Notes on Model Thinking III

  1. Anonymous says:

    just tried it. the presenter had no idea what it was about 😀

  2. If just the world were so easy that it has the structural complexity of just a list …
    … oh my, wouldn’t this be boring?


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