This is the final exam for Deciding Under Deep Uncertainty. It pulls the whole course together: Knight’s split between risk you can compute and uncertainty you can’t; the three ways expected value betrays you under deep uncertainty (the point estimate hides fat tails, the odds were invented, and the ergodicity trap turns a positive average into near-certain ruin for the one person living the sequence); the switch from optimality to robustness (satisficing, minimax-regret, and the ruin-avoidance gate); the buy-back toolkit (reversibility and option value, margin of safety, small experiments, redundancy, scenarios, the pre-mortem, the antifragile barbell); and the four traps — false precision, treating uncertainty as risk, over-hedging into paralysis, and mistaking robustness for pessimism. Several questions look easy until you spot a hidden trap: an invented probability dressed as a fact, a high average that hides a path through zero, or a “robust” plan that’s quietly just pessimism. Diagnose the situation before you answer each one.
How this exam works
Read carefully — this exam is final. Each question appears one at a time. Once you submit an answer it is locked for good: there’s no going back, no retry, and no restart. Your score is hidden until the end, where you’ll see a pass/fail verdict. The pass mark is 70%. A few questions ask you to select all correct answers.
In Frank Knight's distinction, what separates 'risk' from 'uncertainty'?
Select an answer to continue.
Course Recap
Big picture
Deciding under deep uncertainty, in one picture
- Deciding Under Deep Uncertainty
- Risk vs. uncertainty (diagnose)
- Knight's split: risk = knowable odds (a die, a mortality table), optimise; deep uncertainty = unknown/unknowable odds (which tech wins the decade), switch modes. Optimising invented odds is false precision.
- When expected value lies
- The point estimate hides fat tails; the odds were invented; and the ergodicity trap turns a positive ensemble-average into near-certain ruin for the one person living the sequence (the 50/−40 coin: +5% mean, 1.5 × 0.6 = 0.9 downward compounding).
- Robustness over optimality
- Acceptable across many worlds beats best in one guess. Satisfice, use minimax-regret, and gate out ruin first — survival is the precondition, not a factor, because there is no compounding after zero.
- Buy back recoverability
- Reversibility and option value, margin of safety, small reversible experiments, redundancy and slack, scenario thinking, the pre-mortem, and the antifragile barbell so a wild world pays you.
- The four traps + humility
- False precision; treating deep uncertainty as risk; over-hedging into paralysis; mistaking robustness for pessimism. Underneath: calibration humility — you know less than your confidence suggests.
- Risk vs. uncertainty (diagnose)
You've finished Deciding Under Deep Uncertainty
The expert model for choosing in the fog, in one line: diagnose whether you face risk or deep uncertainty; gate out ruin before you optimise anything; seek robustness — a choice that does acceptably across many worlds — over the fragile optimum for your single guess; buy back recoverability with reversibility, margin, experiments, slack, scenarios and the pre-mortem; and dodge the four traps while holding calibration humility. Expected value assumed you knew the odds. Now you know what to do when you don’t — stop optimising, and start surviving.