Alfred Korzybski said that. Reality is not the model. The model however can change. It can be changed. I realized that very recently. It is a powerful idea. It directs responsibility inwards, completely. I am responsible for my emotions, my reactions to the external world.
We collaborate through models. Your model interacting with mine. In relationships, art, cooperative learning etc. We see this in art specially. Eric Kandel talks about the share of the beholder. The beholder’s brain acts as a ‘creativity machine’. Each such interaction is different. This makes sense to me. My neuroscience professor talked about this in visual perception. The data stream fed into the eyes is mostly stripped. Some hints go to the brain. It reconstructs it. It is an amazing idea. We all have a collaborative social agreement on symbols to describe ‘red’. However, do we all perceive red the same way?
Some models are more powerful, I feel. Prose is more effective at bringing magical worlds across time and space. Movies, not so much.
This is true in creating using computers. I spent quite a bit of time to make this site easy on my eyes; I am not satisfied with the end result though. Yet, this is one of the best of the worst ways of doing this. Bret Victor talks about programming being a ‘blindly manipulative task’. I agree. It is apparent when I want to change colors in this blog. Ideally, I could get a crayon, paint maybe. Paint on the mockup. The mockup would be turned into code. I could deploy the code. Instead, I pick a color from a color picker. Change the color in SASS. Regenerate the blog. See how it looks. Redo. Another analogy? Fonts. I can write in my handwriting. Unique to me. Yet online, I pick a font that is closest to what I want. I put the font in my blog. I see how it looks. I repeat. The model is not completely bad. It lets me connect with someone across the world. However, it is not good either. A newsletter using a pen, would be more easy to use.
Bret has another analogy that applies to data science. When you want to visualize data, say in R or Matlab. You put your data in it. The program builds a bar chart or a pie chart or something from a template. Yet, when you write a research paper, you don’t pick a template and fill in the blanks.
This is not to say that only old models are good. Mathematics is a great example. I don’t like symbols. I don’t find numbers that interesting. I hate silly problems about trains moving back and forth. Why is the mechanics of moving locomotives supposed to be relevant to my life? Neither do I particularly enjoy puzzles. Yet, I am deeply in love with math. You think Lolita works on several different levels? Statistics is incredibly rich. A Gaussian probability model underpins so many different narratives: Bayesian, frequentist, even probability theory. These are not immediately apparent, however. The symbols, even some of the drawings are limited. Ceci n’est pas une pipe; indeed.
I was lucky. I found someone who stripped away symbols, shared with me their way of perceiving math. There needs to be a more systematic way of doing this. How? I don’t know yet. I want to find out. I want to build better maps. I want to use old maps more effectively. So I meditate on Math, Art and Prose.