# Expected Utility **Entity class:** Decision-Theory And Collection-Management Layer concept **Expected Utility** is a decision-and-collection concept used to select actions or observations that improve a consequential judgment. The operational value of Expected Utility lies in preserving time, provenance, confidence, and alternative explanations. ## Counterterrorism predictive-graph role The page records capability and analytic function. Any claim of a named operational deployment remains subject to the [[wiki/Counterterrorism Predictive Graph Evidence Ladder|evidence ladder]]. The analytic state should distinguish ground truth, observed evidence, inferred state, and predicted state. ## Relationships - **domain router:** [[wiki/Decision Theory|Decision Theory]]. - **ontology neighbors:** [[wiki/Bayesian Decision Theory|Bayesian Decision Theory]] and [[wiki/Decision Under Uncertainty|Decision Under Uncertainty]]. - **synthesis:** [[wiki/Counterterrorism Predictive Graph|Counterterrorism Predictive Graph]] and [[wiki/Predictive Intelligence Loop|Predictive Intelligence Loop]]. ## Sources - [NIST/SEMATECH — e-Handbook of Statistical Methods (accessed September 23, 2026)](https://www.itl.nist.gov/div898/handbook/) - [Oden Institute — Tan Bui-Thanh research profile (accessed September 23, 2026)](https://oden.utexas.edu/people/directory/Tan-Bui/) **As of:** 2026-09-23 ## 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]]