Looking at our financial markets in early October 2022 is like riding one of these rollercoasters that take you up a loooooong time, only to become really sloooooow before you reach the apex.
You know markets will come down hard. This wait seems like an eternity.
Can we learn to benefit from the uncertainty ahead ?
Our financial markets show indicators of another hard correction impending. The VIX is as high as it was right before the first COVID crash. Morgan Stanley (under-) estimates that the financial markets will take a 15% plunge from here (after already being down 24% on the year). The Fed indicates continuing interest rate hikes into 2023. Rumors of Credit Suisse going bust.
Nicholas Taleb is a leading economist of our time, who made waves with theories around the 2008 financial crash. He explained black swan events in 2007, and posited that growth in uncertainty flourishes under barbell strategies due to anti-fragility.
For Taleb, anti—fragility is fundamentally about creating optionality and accepting that knowledge is always limited, while exploration under trial & error is unlimited. Taleb says that if trial & error costs less than the upside of being right once – we should adopt an anti-fragile strategy of learning through trial & error to maximize outcomes.
This inspired me to spend to explore anti-fragility further – and look at it in the context of construction.
If we dumb it down, construction has two types of projects: n = 1 projects, where every project is unique in many ways, and n > 1 projects, where we repeat a project serially (or parts of it, for example volumetric modules).
Taleb has identified 7 rules of when anti-fragility applies and an optionality and trial & error maximizes the outcomes – and not seriality.
I’ve analyzed and applied the 7 rules of anti-fragility to both construction project types. You can find the analysis in my slide deck.
To my surprise, it seems to suggest that construction has higher chances of maximal long-term results BECAUSE it widely uses an n = 1 approach. All 7 rules of anti-fragility seem to check out with n = 1 construction projects.
When I interpreted the results, there is a way to make sense of it. Construction is known for relying more on people and their coordination and trouble-shooting than on plans. It’s because construction projects have a lot of interdependencies and externalities. Within the anti-fragility world, top-down derived strategies are inherently less likely to achieve the optimal outcome than a trial & error approach led by the construction project managers and workers on the ground dealing with the interdependencies.
I also find it fascinating in the context of asset-heavy serial modular construction companies, such as Katerra (which went out of business in mid 2021). Anti-fragility suggests that asset-heavy modular contractors will suffer from avoidance of optionality through trial & error which leads to slow knowledge acquisition. Looking from the outside-in, this seems to be consistent with Katerra’s well-documented shortcomings.
It’s fascinating to think that either construction has already found the most efficient learning mode by being n = 1.
Or that projects which are n > 1, asset-light supply chain integrators such as 011h or Juno who facilitate existing competencies in the supply chain and allow for prototyping and testing are more optimal than asset-heavy models.
Definition of anti-fragility | Knowledge is a poor substitute for convexity and anti-fragility | Seven rules of anti-fragility