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.
Key takeaways
- What it is: 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.
- Evidence: Backed by observational / correlational evidence.
- Avoid: Only encoding successful cases and ignoring failures, which produces a biased library that systematically overestimates how well your typical approaches work.
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
- 1After any significant decision or event, write a brief case note: "This was a case of [relational structure], it produced [outcome]."
- 2When facing a new problem, ask: "What cases in my experience have this same relational structure?"
- 3Weight cases by similarity of relational structure, not by surface familiarity or recency.
- 4Update cases when outcomes prove them wrong or when new information revises the lesson.
What the evidence says
ObservationalCase-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.
Honest caveat: 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.
- — 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.
IX Coach helps you build and maintain a structured case library across sessions, annotating each decision with its relational structure and outcome so retrieval is accurate when the next structurally similar situation arises.
Practice this with IX Coach →