Most of us live lives constantly on the go, with little-to-no-time for formulating hypotheses to investigate further. Exploration becomes something that occurs by chance. Long on data, short on theory. Curiosity helps, but it needs acting on it to work.
The relentless quest for predictable outcomes blinds us from the reality that business and most specifically commerce is about risk creation, the other side of the opportunity coin. Our conversations often suffer from a lack of appreciation for the micro-trade offs that are constantly necessary to make something work… and keep working.
Translating to layman terms — we idolize the macro-bulshitters and trivialize the people who adapt, iterate, and do what it takes to make things happen. Those who learn the art of making trade-offs achieve better performance, without getting too tricked into taking foolish shortcuts.
Nassim Nicholas Taleb describes a useful concept in Antifragile:
We can simplify the relationships between fragility, errors, and antifragility as follows.
When you are fragile, you depend on things following the exact planned course, with as little deviation as possible — for deviations are more harmful than helpful.
This is why the fragile needs to be very predictive in its approach, and, conversely, predictive systems cause fragility.
When you want deviations, and you don’t care about the possible dispersion of outcomes that the future can bring, since most will be helpful, you are antifragile.
Further, the random element in trial and error is not quite random, if it is carried out rationally, using error as a source of information.
If every trial provides you with information about what does not work, you start zooming in on a solution — so every attempt becomes more valuable, more like an expense than an error.
And of course you make discoveries along the way.
It's not the risk in making trade-offs that gets us in trouble, but the difference between what we expect or forecast and what actually happens. It's the variance we need to learn more about. In stable situations, predictable may work. We want to learn to understand variance by experimenting more, not less in highly changing environments. Differences in results are sources of valuable information.
Here's where the idea of small things being useful comes in. The idea is to have a progress path along which we move in small steps. With each step, we gain access to more knowledge and skills. When we learn what works this way, by doing the right things in the right way, we make the path robust.
We can solve problems by thinking small. A favorite is letting go of a bad situation faster to free resources and energy for a better one. Small things make a big difference when they're the right kind of things, and we're framing them with a theory that tells us something about the mechanisms at work.