Tinkering

You know how when you start seeing a theme in one area of life, it starts popping up everywhere? That’s happened this week with success through iteration – the faster the better. Depth-wise, I’m excited because there’s a lot more than a single post’s worth of thoughts. So for today I’m just going to go for breadth and mention all the places that it feels like success comes from iterative tinkering:

Invention – can I introduce you to a certain Edison and his light bulb? The computer has massively accelerated the speed of possible iteration, which leads us to …

Entrepreneurship – few successful companies end up selling what they originally intended, having instead executed the vaunted ‘pivot’ to an alternative.

Markets – zooming out from within one company to groups of many companies, rapid cycles better meet consumer needs.

Biology – from repeated social interactions between individuals, to generational tinkering, there is a lot of success through rapid iteration. (Hi antibiotic resistant bacteria.) And generalizing from biology, we get to…

Science itself – even elegant analytical theories in math and physics are the result of tinkering and failures. While the general idea of the theory may have always been there and the final formula may have you asking ‘how could it be any other way?’ the truth of the matter is that if you look at the notes of the great thinkers, they had to tinker with hundreds of iterations before reaching the solution.

Many of the virtues of tinkering are extolled by Nassim Taleb. I’m pretty sold, but not 100% A big question I’m ruminating on is:

On the one hand, high iterative models lead to long run success. On the other, it is hard to learn from random processes. So, a random genetic algorithm will lead to probably a better answer, but then can we take that answer and build upon it?

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2 thoughts on “Tinkering

  1. I do a lot of tinkering in my work. I’ve found it to be effective for all the reasons you describe, but there’s a huge caveat: without occasional Big Thinking Up Front (which I think of as the converse), an iterative process can sometimes end up at local minima (of the utility space) because it started from the wrong place. Subsequent iteration from that local minimum may be unable to hill-climb out.

    One can combat this by doing big thinking up front, to hopefully start from a better position, but obviously because we’re not always prescient, that doesn’t always work. Another alternative is to iterate in a special way: it is tempting when iterating to explore the surrounding “neighborhood” in utility space, but then you’ll never escape local minima. Sometimes when iterating you need to “jump” to a new starting location and iterate a few stages from there to see if you end up somewhere better.

    • Ooo – I really like the idea of taking an occasional jump out of the neighborhood. I think I do that inadvertently sometimes and try to do it consciously in the future.

      The big thinking up front you describe is what I like to think of as ‘putting a bias’ on the random walk.

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