# Minimax Regret **Entity class:** Decision criterion **Domain:** Decision theory / governance **Maturity:** Developed ## Definition Minimax regret selects an action by considering the worst avoidable loss it could produce across uncertain future states and choosing the option whose maximum regret is smallest. ## Mechanism and significance The criterion is useful when probabilities are disputed or poorly known. It can complement expected utility by testing whether a policy exposes society to a loss that would remain intolerable even if the optimistic forecast proves correct. ## Relationships - [[wiki/Expected Utility|Expected Utility]] - [[wiki/AI Governance|AI Governance]] - [[wiki/AI Safety|AI Safety]] - [[wiki/Moonshots - The Coming Manhattan Project|Moonshots — The Coming Manhattan Project]] ## Sources and provenance - [[research/All Things Superintelligence Research - The Coming Manhattan Project - Moonshots|All Things Superintelligence Research — The Coming Manhattan Project — Moonshots]] — episode transcript, reconciled against the preserved ElevenLabs timing layer. - [[research/All Things Superintelligence Research - The Coming Manhattan Project - Moonshots - Reminder and Wiki Preparation|Reminder and Wiki Preparation]] — coherent-thought ledger and evidence boundaries. ## Evidence boundary Minimax regret depends on which scenarios and losses enter the comparison. It can become excessively conservative when catastrophic stories are admitted without evidence or when benefits of action are omitted. ## Simple Reminders, Quotations, and Thoughts > AI governance needs a map of what could go right and what could go wrong, stated by the people making each case. Expected utility helps weigh probable outcomes; minimax regret identifies losses society cannot accept. Policy should respond to the mapped gaps instead of governing from undifferentiated fear. > **— Adapted from Emad Mostaque**, *Moonshots, October 7, 2026* [[reminders/Risk Debate/AI Governance Should Map Expected Benefit and Catastrophic Regret by Emad Mostaque|AI Governance Should Map Expected Benefit and Catastrophic Regret by Emad Mostaque]]