Apply the 37% rule to search decisions — explore…
When hiring or making other search-based decisions from a known pool size, spend the first 37% of candidates or options in pure exploration (reject all, calibrate threshold), then commit to the next candidate that exceeds the best seen in the exploration phase.
When This Fires
- When making sequential-evaluation decisions from a known or estimable pool size
- When hiring from a candidate pipeline with a known number of applicants
- When apartment hunting with a finite number of viewings scheduled
- When the decision structure is: evaluate options one at a time, accept or reject, can't return to rejected options
Common Failure Mode
Committing during the exploration phase because an early candidate seems excellent: "This person is amazing — what if I don't find anyone better?" The 37% rule says: reject them anyway. You're in the calibration phase. The "amazing" assessment lacks the baseline that only the full exploration phase provides. What feels like the best candidate at 15% of the pool may be merely the first competent one you've seen.
The Protocol
(1) Estimate the pool size: how many candidates/options will you evaluate? (2) Calculate 37% of that number (round to nearest integer). This is your exploration phase. (3) During exploration: evaluate each option carefully, note quality, but reject all of them. No exceptions. (4) After exploration: commit to the first option that exceeds the best you saw during exploration. (5) If no option exceeds the exploration-phase best → you're forced to take the last option or restart. This happens ~37% of the time and is the known cost of the strategy. (6) Adjust for real-world constraints: if options can be recalled, if pool size is uncertain, or if evaluation is imperfect, the optimal fraction shifts lower (20-30%). The 37% assumes the pure mathematical case.