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
- After any significant decision or event, write a brief case note: "This was a case of [relational structure], it produced [outcome]."
- When facing a new problem, ask: "What cases in my experience have this same relational structure?"
- Weight cases by similarity of relational structure, not by surface familiarity or recency.
- 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 About this work
Common mistake
Only encoding successful cases and ignoring failures, which produces a biased library that systematically overestimates how well your typical approaches work.
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More practices for Analogical Reasoning
- Map the relational structure, not the surface features
Ask what relationships are shared between domains, not just what objects look similar.
- Use multiple analogies for the same problem
Draw on at least two different analogies for a new problem and see where they agree and diverge.
- Use progressive alignment: start with similar cases and move to more distant ones
Learn relational structure by comparing easy cases before applying the same structure to harder ones.
- Check for false analogies: does the structural mapping actually hold?
Test whether a compelling analogy survives scrutiny of its relational structure.
- Transfer solutions across domains by mapping the structure of the problem
When stuck, search for problems in other domains with the same relational structure.
Related concepts
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