Antifragility

• Interesting Concept

  • It is common to consider the binary opposition of “fragile vs. robust.”
    • However, there is an argument that it is actually a triad of “fragile vs. robust vs. antifragile.”
      • When fragility and robustness are defined as they are in books, it becomes amusingly understandable that antifragility exists as well.
      • It feels like “If There is a Framework, You Can Recognize the Blank Space Enclosed by the Framework.”
      • It’s interesting to see such misconceptions of binary opposition.
        • Misunderstandings like upper/lower limits.
    • Fragility is often thought of as reducing problems caused by uncertainty.
      • However, there is also an approach where antifragility increases joy from uncertainty.

• Barbell Strategy

• Putting One’s Own Money

• Nonlinearity

  • People tend to approximate things with linear models.
  • This can lead to overlooking fragility.
  • It’s about convexity, essentially whether d^2/dx^2 is positive or negative.
    • If it’s positive, then the upside from variation is greater than the downside, making it antifragile.
    • If it’s negative, then it’s fragile.
    • Recognizing fragility helps avoid dependency on it, which is beneficial.

• It’s already sufficient if you can recognize, “I am currently perceiving things with a linear model.”

  • It can be dangerous to have a vague image of “things are generally on the rise” when considering trade-offs and gains and losses.
  • Even if it’s on the rise, claiming that whether it’s concave or convex makes a significant difference.

• Network effects and Moore’s Law are clear examples of concave nonlinearity.

• In economics, the law of diminishing returns demonstrates convex nonlinearity.