This mathematical concept caught my attention while I binged on DONG videos.
I often struggle with the idea of how to measure data in complicated and ever-changing systems. For example if I’m posting 20 times per week on Steem, and each post is a different topic with different quality, title, thumbnail, etc, how can I measure progress?
It turns out I’m not the only one who has thought about this.
In statistics, we refer to “chaotic systems” as existing in two forms: Level 1 and Level 2.
Level 1 chaotic systems are systems which are chaotic but can be measured without changing the results. DONG uses our society's weather predictions as an example of this.
If we develop a better algorithm for measuring the weather next week, it is not going to change next week’s weather. The more accurate we are, the better our forecast is.
This is different from Level 2 Chaotic Systems which are changed as you measure them. For example, the cryptocurrency markets that we all deal with — if you perfectly predict the next few days of activity, then post those predictions in a post on your steem blog, the act of posting the prediction will change the end result.
Even if you keep your predictions private, the mere act of you buying and selling tokens will change the results. If other people predict the info, the results change more.
The extreme example is when an infamous stock picker starts screaming BUY BUY BUY about some stock, immediately up-ending the entire market context surrounding the stock and erupting the situation into chaos.
As personal bloggers we are all trapped in a level 2 chaotic system.
Our own blogs change every day, every time we do anything to it something is changing. Your post may inspire other bloggers to create differently. The butterfly effect lurks around every corner.
If it ever feels like an overwhelming and confusing quest to gain success on Steem, keep in mind that it’s an intense level 2 system. You aren’t bad at this or dumb for finding it confusing — in fact its the opposite, you are perceptive to notice how hard it is to predict anything here.
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