Use the 1/N rule for diversification under deep uncertainty
When you cannot estimate the value of each option reliably, spread resources equally.
Key takeaways
- What it is: When you cannot estimate the value of each option reliably, spread resources equally.
- Why it works: Under deep uncertainty — where the probabilities and payoffs of outcomes cannot be estimated with confidence — optimizing an allocation requires assumptions you don’t have data to support. The 1/N heuristic (split equally among N options) outperforms optimized portfolio allocations in out-of-sample real-world tests because it avoids the overfitting that makes optimization strategies fragile when conditions change.
- Evidence: Backed by observational / correlational evidence.
- Avoid: Applying 1/N even when you have reliable data showing that options differ substantially — the heuristic is for deep uncertainty, not a substitute for evidence when evidence exists.
Why it works
Under deep uncertainty — where the probabilities and payoffs of outcomes cannot be estimated with confidence — optimizing an allocation requires assumptions you don’t have data to support. The 1/N heuristic (split equally among N options) outperforms optimized portfolio allocations in out-of-sample real-world tests because it avoids the overfitting that makes optimization strategies fragile when conditions change.
How to do it
- 1When you face a resource allocation decision with genuine deep uncertainty (time between projects, budget across experiments), default to equal allocation.
- 2Only deviate from equal allocation when you have reliable data showing differential returns.
- 3Revisit allocation after you have real outcomes — shift toward what’s working with evidence, not optimism.
What the evidence says
ObservationalDeMiguel, Garlappi & Uppal (2009) compared 1/N portfolio allocation to 14 optimized strategies across multiple stock market datasets and found 1/N competitive with or better than the optimized approaches out-of-sample.
Honest caveat: This result is specific to financial portfolio contexts with limited data and uncertain means. For decisions where returns are meaningfully different and estimable, optimized allocation can outperform 1/N.
- — DeMiguel, Garlappi & Uppal (2009), Optimal versus naive diversification, Review of Financial Studies
Common mistake
Applying 1/N even when you have reliable data showing that options differ substantially — the heuristic is for deep uncertainty, not a substitute for evidence when evidence exists.
IX Coach surfaces whether your energy and time allocation across competing priorities is calibrated to actual returns or just optimistic planning — and helps you adjust based on evidence.
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