Use maximin reasoning for high-stakes, irreversible decisions under ambiguity
Choose the option whose worst plausible outcome is most survivable — when you can’t compute expected value, optimize the floor.
Key takeaways
- What it is: Choose the option whose worst plausible outcome is most survivable — when you can’t compute expected value, optimize the floor.
- Why it works: Under genuine Knightian uncertainty, expected-value maximization is incoherent — you can’t compute expected value without a probability distribution. Gilboa and Schmeidler’s maxmin framework recommends choosing the option whose worst plausible outcome is best. This is conservative but appropriate when: (1) stakes are high, (2) outcomes are irreversible, and (3) the probability distribution is genuinely unknown. For low-stakes decisions, small bets are preferable because information value is high relative to cost.
- Evidence: Plausible mechanism, limited direct outcome data.
- Avoid: Applying maximin to low-stakes decisions where a small experiment would produce real data — maximin is a last resort for irreversible high-stakes choices, not an everyday decision rule.
Why it works
Under genuine Knightian uncertainty, expected-value maximization is incoherent — you can’t compute expected value without a probability distribution. Gilboa and Schmeidler’s maxmin framework recommends choosing the option whose worst plausible outcome is best. This is conservative but appropriate when: (1) stakes are high, (2) outcomes are irreversible, and (3) the probability distribution is genuinely unknown. For low-stakes decisions, small bets are preferable because information value is high relative to cost.
How to do it
- 1List all plausible scenarios, including tail cases.
- 2For each option, identify the worst realistic outcome.
- 3Choose the option where the worst outcome is most survivable or reversible.
- 4Reserve this for high-stakes, irreversible decisions; for low-stakes decisions, run small bets instead.
What the evidence says
MechanisticMaximin is a well-formalized decision rule in game theory and decision theory under ambiguity (Gilboa & Schmeidler, 1989). Its real-world effectiveness is untested in RCTs but it is logically defensible under genuine uncertainty and widely used in policy analysis.
Honest caveat: Maximin is extremely conservative and can be worse than expected-value reasoning when probabilities are actually estimable — use only when the probability distribution is genuinely unknown.
- — Gilboa, I., & Schmeidler, D. (1989). Maxmin expected utility with non-unique prior. Journal of Mathematical Economics, 18(2), 141–153.
Common mistake
Applying maximin to low-stakes decisions where a small experiment would produce real data — maximin is a last resort for irreversible high-stakes choices, not an everyday decision rule.
In IX Coach’s decision journal, a maximin template guides you through listing scenarios and worst-case outcomes, making the choice explicit rather than intuitive.
Practice this with IX Coach →