Tag: Nassim Nicholas Taleb

Living an antifragile life

Nassim Nicholas Taleb’s new book is out, which made me think about his previous work, Antifragile (which I enjoyed greatly).

As Shane Parrish quotes in a 2014 article on the subjet, Taleb defines antifragility in the following way:

Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder, and stressors and love adventure, risk, and uncertainty. Yet, in spite of the ubiquity of the phenomenon, there is no word for the exact opposite of fragile. Let us call it antifragile. Antifragility is beyond resilience or robustness. The resilient resists shocks and stays the same; the antifragile gets better. This property is behind everything that has changed with time: evolution, culture, ideas, revolutions, political systems, technological innovation, cultural and economic success, corporate survival, good recipes (say, chicken soup or steak tartare with a drop of cognac), the rise of cities, cultures, legal systems, equatorial forests, bacterial resistance … even our own existence as a species on this planet.

This definition, and the examples Taleb pointed to in his book helped me understand the world a bit better. It’s easy to point to entitled people and see how they manage to get richer no matter what happens. But I think we all know people (and in fact companies, organisations, and communities) that are just set up for success. The notion of them being ‘antifragile’ helps describe that.

Parrish quotes Buster Benson who boils Taleb’s book down to one general, underlying principle:

Play the long game, keep your options open and avoid total failure while trying lots of different things and maintaining an open mind.

More specifically, Benson notes Taleb’s 10 principles of antifragility:

  1. Stick to simple rules
  2. Build in redundancy and layers (no single point of failure)
  3. Resist the urge to suppress randomness
  4. Make sure that you have your soul in the game
  5. Experiment and tinker — take lots of small risks
  6. Avoid risks that, if lost, would wipe you out completely
  7. Don’t get consumed by data
  8. Keep your options open
  9. Focus more on avoiding things that don’t work than trying to find out what does work
  10. Respect the old — look for habits and rules that have been around for a long time

Some great suggestions here, and I’m very much looking forward to reading Taleb’s new book. As a bonus, in putting together this post I discovered that, after jobs at Twitter, Slack, and Amazon, Buster Benson is writing a book. He’s looking for 100 supporters at $1 a month so I didn’t even think twice and pledged!

Source: Farnam Street

The Project Design Tetrahedron

I had reason this week to revisit Dorian Taylor’s interview on Uses This. I fell into a rabbithole of his work, and came across a lengthy post he wrote back in 2014.

I’ve given considerable thought throughout my career to the problem of resource management as it pertains to the development of software, and I believe my conclusions are generalizable to all forms of work which is dominated by the gathering, concentration, and representation of information, rather than the transportation and arrangement of physical stuff. This includes creative work like writing a novel, painting a picture, or crafting a brand or marketing message. Work like this is heavy on design or problem-solving, with negligible physical implementation overhead. Stuff-based work, by contrast, has copious examples in mature industries like construction, manufacturing, resource extraction and logistics.

As you can see in the image above, he argues that the traditional engineering approach of having things either:

  • Fast and Good
  • Cheap and Fast
  • Good and Cheap

…is wrong, given a lean and iterative design process. You can actually make things that are immediately useful (i.e. ‘Good‘), relatively Cheap, and do so Fast. The thing you sacrifice in those situations, and hence the ‘tetrahedron’ is Predictable Results.

If you can reduce a process to an algorithm, then you can make extremely accurate predictions about the performance of that algorithm. Considerably more difficult, however, is defining an algorithm for defining algorithms. Sure, every real-world process has well-defined parts, and those can indeed be subjected to this kind of treatment. There is still, however, that unknown factor that makes problem-solving processes unpredictable.

In other words, we live in an unpredictable world, but we can still do awesome stuff. Nassim Nicholas Taleb would be proud.

Source: Dorian Taylor