regret-minimization
Regret Minimization
Overview
Most frameworks optimize expected value — pick the highest probability-weighted payoff. That math breaks down for large, asymmetric, life-defining choices where the true unit is not money: career pivots, founding decisions, relocations, relationship exits. There, the decisive question is not "what's the expected payoff?" but "which regret will I be unable to live with at 80?"
Associated with Jeff Bezos (D.E. Shaw → Amazon, 1994); rooted in Stoic Premeditatio Malorum (Seneca, Epistulae Morales 91) and formalized in Regret Theory (Loomes & Sugden, The Economic Journal, 1982).
Compose with neighbors: use first-principles to clarify what is at stake; inversion to surface failure modes; second-order-thinking to verify downstream consequences; then use regret minimization to choose when EV analyses come out close and the true cost is psychological.
When to Use
Apply when: decision is major, hard-to-reverse, asymmetric (career pivot, founding, relocation, relationship, children); EV math feels insufficient because units are joy/meaning/identity; hesitation is emotional, not analytical; user says "what would my 80-year-old self think?", "if I never try this will I regret it?", "I keep hesitating but the math is clear," "should I quit big tech to go all-in on AI / join the AI wave / start a company now (bubble or export-control fears notwithstanding)?"
When NOT to use: routine reversible decisions; EV is genuinely the right unit (portfolio allocation, pricing); framework being re-run weekly (procrastination); both regrets are unlivable (redesign the choice instead).