Build a library of cases and reason from them

Accumulate a diverse set of cases with known outcomes, and retrieve structurally similar ones when facing a new problem.

Why it works

Case-based reasoning — a formal AI and cognitive science framework — models how experts think: they accumulate a large library of cases with known outcomes and retrieve the most structurally similar case when facing a new problem. The analogy is implicit: the retrieved case is the base domain. Deliberate case library construction — actively noting "this is a case of X type with Y outcome" — creates the raw material for accurate analogical retrieval rather than relying on whatever cases happen to be salient.

How to do it

1. After any significant decision or event, write a brief case note: "This was a case of [relational structure], it produced [outcome]."
2. When facing a new problem, ask: "What cases in my experience have this same relational structure?"
3. Weight cases by similarity of relational structure, not by surface familiarity or recency.
4. Update cases when outcomes prove them wrong or when new information revises the lesson.

Evidence

Case-based reasoning is a formal cognitive model with support from expert-performance research: expert decision-makers in medicine, chess, and firefighting retrieve and adapt known cases rather than reasoning from first principles each time. (observational)

Expert case libraries are built over years; this practice accelerates construction of that library but requires honest case annotation (including failures) to be useful rather than self-serving.

Sources

• Klein (1999), Sources of Power: How People Make Decisions — recognition-primed decision model in expert practitioners

Common mistake

Only encoding successful cases and ignoring failures, which produces a biased library that systematically overestimates how well your typical approaches work.