• 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.