Build pattern libraries through deliberate case exposure

Systematically expose yourself to varied cases in your domain to grow the pattern library recognition draws from.

Why it works

RPD depends entirely on having seen the current situation — or a structural analogue — before. Each novel case that is consciously processed and reflected on adds a prototype to the library. Without sufficient case diversity, recognition fires on superficially similar situations that are mechanistically different, producing confident errors. Deliberate case exposure is how the library is grown intentionally rather than by accident.

How to do it

1. Identify the types of situations you make high-stakes decisions in and audit which types you have seen least.
2. Seek out cases in those underrepresented types: read case studies, debrief colleagues, review past failures.
3. After each new case, write a brief note on what made it recognizable and what it rules out.
4. Revisit stored cases periodically — memory consolidation requires spaced reactivation, not just initial exposure.

Evidence

Klein’s fieldwork in naturalistic decision settings found that experienced decision-makers cite the diversity and volume of cases encountered as the primary source of their judgment skill. This is consistent with deliberate practice research (Ericsson) on the role of varied, feedback-rich exposure. (observational)

Naturalistic studies cannot isolate case exposure from other factors (mentorship, feedback quality, domain structure). Deliberate practice research is primarily on physical and cognitive performance skills; extension to managerial/clinical judgment is plausible but less precisely quantified.

Sources

• Klein (1998), Sources of Power: How People Make Decisions
• Ericsson et al. (1993), "The role of deliberate practice in expert performance," Psychological Review

Common mistake

Assuming that time in role automatically builds a useful pattern library — without reflection and feedback, experience can entrench bad patterns as efficiently as good ones.