Latest / Lead With Clarity: Practical Insight for Senior Leaders / Good Enough, Chosen Deliberately
Transcript
- Blake Holman: Welcome to Lead with Clarity. This is a space for leaders who want to grow without losing themselves in the process. Each episode is a short reflection on leadership, influence, and the inner work that shapes how we show up for others. It's not hype, not noise, just clear thinking for the journey of leading well. Today's episode is Good Enough, Chosen Deliberately. Here we go. I want to introduce you to someone whose name many business leaders have never heard. Even though he may have understood the actual limits of decision making better than almost anyone who ever wrote about it. Here's the sentence that made his career. Herbert Simon wrote, The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problems whose solution is required for objectively rational behavior in the real world. Simon is unusual among the people I talk about on this show because his resume does not fit neatly into one category. He won the Nobel Prize in Economics for his work on decision making inside organizations. He was also a founding figure in artificial intelligence, co-writing The Logic Theorist in 1956, widely considered one of the first true AI programs, designed specifically to prove mathematical theorems the way a person would. He spent his career. Moving back and forth between studying how humans actually think and trying to build machines that could think at all, and that unusual vantage point is exactly what let him see something that many economists of his era largely missed. Classical economics of the mid-20th century assumed something Simon considered obviously false: that people facing a decision could gather all relevant information, calculate every outcome, and select the objectively optimal choice. Simon pointed out that this was not just difficult in practice, it was impossible in principle for a reason rooted in basic cognitive architecture. The human mind, and it turns out any mind, has a small bounded capacity for holding and processing information at once. Real decisions in the real world routinely involve more variables, more uncertainty, and more interacting consequences than that bounded capacity could ever fully process. Full rationality, in the strict sense economists meant it, was never actually available to anyone. His answer to this problem was a concept that he called satisficing, a word he built by combining satisfy and suffice. Instead of searching for the objectively optimal answer, a rational actor operating under real constraints looks for an option that clears a reasonable bar of good enough, then stops searching and takes action. This gets commonly misread as a lesser strategy, a compromise for situations where there simply is not time to do things properly. Simon's actual claim was far more radical. Satisficing is not second best. Given genuine bounds on time, information, and processing capacity, it is the only rational strategy available to any real decision maker, full stop. Here's where this becomes urgently relevant to how leaders should think about the tools now sitting on their desks. It's tempting to assume that a sufficiently powerful model finally escapes Simon's constraint, that enough processing power and enough training data adds up to something closer to the full rationality that classic economics originally imagined. That is a misunderstanding of what Simon actually proved. A model can hold vastly more information than a single human mind, but the real world it is being asked to reason about remains just as tangled and just as resistant as To full optimization as it always was. The model satisfies two. It reaches a good enough answer very quickly and delivers it with total confidence, without ever announcing that a bound was hit or a simplification was made along the way. Recognizing that a fluent answer is a reasonable simplification, not a complete solution to an infinitely complex problem, is still a judgment call that belongs to the person using the tool. So here's what I would suggest. Before your next significant decision, name explicitly what you actually know, how much time you genuinely have, and what a good enough answer would look like given those real constraints. Notice whether you are still searching for a non existent perfect answer, and whether that search has quietly become an excuse to delay a decision that you are actually equipped to make right now. Simon studied minds built to reason and, later in his career, machines built to imitate reasoning would be And arrived at the same honest verdict about both. An answer chosen deliberately as good enough will consistently outperform a perfect answer that never actually arrives.