Bayes’ Theorem & The Implications Of Compound Probability, Understanding + Design

Sources / Additional Reading Wikipedia - Bayes Theorem Veritasium - The Bayesian Trap Paul Rulkens - Why The Majority Is Always Wrong Overcoming Blank Page Syndrome

Key Takeaways

  • ASK QUESTIONS: Get as much information as possible before starting.
  • ROLL WITH THE PUNCHES: Try not to think of revision as being ‘wrong’, but rather as helping clarify future ‘right’
  • VALUE THE PAST: All prior interactions influence current ones.
  • THE DONUT HOLE: When you define something, you inherently define its opposite. BOTH are important.

Lecture Outline:

  • Bayes Theorem:
  • What is the actual probability that you have a disease when you test positive with a 99% accurate test?
  • Not 99%, it’s 9%
  • Your mind generally doesn’t take into account the PRIOR probability you were right
  • This is a flaw in human thinking
  • This is very illustrative of the revisionist design process.
  • Constant refinement to finality
  • Assumptions that the longer an idea stays around, the more correct it is (or closer to approval)
  • People often need to see a lot of wrong options to visualize the right ones
  • You need to know what you’re trying to find for any of the information to be valuable
  • It also spells out “common sense” in math form
  • Thinking “Inside The Box” to help define prior constraints
  • What always is
  • What’s legal to do
  • What’s technologically possible to do
  • What’s moral or socially acceptable to do
  • What am I capable of doing
  • What’s been done before