Possibility Intelligence: A General Theory of Intelligent Action as the Preservation and Expansion of Safe Reachable Futures

Artificial intelligence and institutional decision systems are commonly evaluated through accuracy, expected reward, efficiency, robustness, safety, or conformity with specified values. These criteria describe what a system predicts or produces, but not how its actions restructure the future decision space available to affected persons. A decision can increase immediate utility while eliminating alternatives, creating lock-in, transferring irreversible risk, weakening human competence, or removing pathways for exit, correction, appeal, and recovery. This paper introduces Possibility Intelligence, a sequential decision framework in which intelligent action is evaluated partly through the safe, viable, distinct, recoverable, and fairly distributed futures preserved after action. Building on Possibility Ethics, the framework defines the Possibility State, sequential Option Entropy, a canonical structural Possibility Value, Irreversibility Cost, Option Debt, Possibility Dominance, the Possibility Conservation Principle, and a criterion of Possibility Alignment. A constrained Bellman recursion jointly represents task reward, future-option change, expected harm, irreversible opportunity loss, accumulated Option Debt, subgroup floors, and vulnerable-cohort non-regression. Six principal theorems and supporting propositions establish option monotonicity, a reversibility bound, dominance consistency, contraction and fixed-point uniqueness, protected-cohort non-regression, bounded and asymptotically stable Option Debt, duplication invariance, quality monotonicity, and scalar moral incompleteness. Four empirical components complement the formal theory: a reversible grid-world simulation, a synthetic AI-assistant decision experiment, a real observational audit using the Open University Learning Analytics Dataset (OULAD), and a seven-domain synthetic Possibility Intelligence Benchmark (PIB). The framework is not a complete moral theory or an automatic ethical authority. Its purpose is to make future-option expansion, contraction, distribution, and irreversible destruction measurable, contestable, and repairable within sequential human–AI decision systems.