Beyond Optimize and Maximize Why machine thinking fails in complexity

In business writing and on LinkedIn, certain words appear with remarkable confidence: minimize, maximize, optimize. They sound sensible, even responsible. But they rest on a quiet assumption that the world behaves like a machine.

In a complex environment, that assumption does not hold.

We cannot meaningfully optimize a complex system. We cannot know that we have maximized output, minimized risk, or optimized performance. There is no stable baseline, no fixed endpoint, and no reliable way of knowing in advance what “best” even means. We can make things better or worse, but only ever provisionally.

This is why the language of “best decisions” is so misleading. As Margaret Heffernan has pointed out, in complex situations, a decision is better understood as a hypothesis. It is an informed guess about how the world might respond. It can only really be evaluated afterwards, once consequences have unfolded.

I explore this perspective more fully in a recent post in my blook, including why this shift in thinking matters far beyond decision-making alone.