Making Decisions
Experience doesn’t automatically become learning. You can make thousands of decisions over a career and never get better at making them. The same mistakes recur. The same blindspots persist. Experience accumulates without converting into skill.
The difference is whether you build systems that convert predictions into improving judgement. This requires making beliefs explicit, tracking outcomes, and updating — the learning loop that turns decisions into compounding advantage.
Where to start
How Decisions Compound — The learning loop: predict, act, observe, update. Why some people get better at decisions and others don’t.
Learning Under Uncertainty — Experience doesn’t automatically become learning. How to build systems that actually convert predictions into improving judgement.
The learning loop
How to build judgement that improves over time.
How Decisions Compound — Four questions, asked continuously, that turn decisions into advantage. The meta-skill beneath all other skills.
Learning Under Uncertainty — Most feedback is noisy, delayed, or misleading. How to learn anyway.
Decision Rubrics — Make criteria explicit. Let teams self-score. How to codify decision logic so it can be examined and improved.
Rehearsed Intuition — What looks like instinct is memory under pressure. Pattern recognition is rehearsed intuition, not magic.
Number Sense — Business numeracy: instrumentation, interpretation, influence. The feel for numbers that good operators develop.
Prediction traps
Common ways judgement goes wrong.
Hidden Priors — Every forecast starts with a belief. The question is whether you admit it. Implicit assumptions that shape what you see.
Nothing to Update — Implicit predictions can’t teach you anything. If you never said what you expected, you can’t learn from being wrong.
What Estimates Mean — Estimates are medians, expectations are means. The difference matters more than most people realise.
False Consensus — Universal agreement should make you nervous. When everyone agrees, something important isn’t being said.
Staying Wrong — It’s okay to be wrong. It’s not okay to stay wrong. The difference between errors and persistent blindspots.
When Numbers Twitch — Most decisions are reactions to noise. Knowing when movement is signal and when it’s just variance.
System dynamics
How systems behave in ways that complicate decisions.
Inverse Response — Some systems move the wrong way first. The intervention looks like it’s failing before it works.
Pace Layers — Different parts of a system change at different speeds. Trying to move the slow parts fast breaks things.
Unstable by Design — Some systems amplify errors rather than dampening them. Match response speed to instability.
Suggested reading order
If you’re working through this section systematically:
- How Decisions Compound — the learning loop
- Learning Under Uncertainty — making learning systematic
- Hidden Priors — surfacing assumptions
- Nothing to Update — making predictions explicit
- Staying Wrong — updating when you’re wrong
- Decision Rubrics — codifying decision logic
Then explore system dynamics and prediction traps based on what’s biting you.
Connects to
Reading the Business — Numbers are raw material for judgement. You need data to have something to predict against.
Running the Machine — Decisions are often the hidden bottleneck in execution. These sections are deeply linked.
Leading People — Judgement is individual, but implementation is collective. Getting others to act on decisions requires the people side.