Hidden Attractors in Social Systems: A Phase Transition Framework for Anticipating Emergent Futures

Futures research requires methods for anticipating social configurations that do not yet exist. This paper introduces an integrated framework combining Stuart-Landau phase transition dynamics with systematic anomaly detection to identify hidden attractors in social systems. Drawing on physics methodology—where anomalies like Mercury's perihelion precession led to general relativity—we argue that systematic social outliers reveal attractor basins not captured by current models. The framework provides: (1) mathematical formalization of social phase transitions using order and control parameters, (2) methodology for detecting hidden attractors from clustered anomalies, (3) early warning signals for approaching critical thresholds, and (4) structured scenario generation based on attractor landscape mapping. We demonstrate the framework's utility across multiple domains including organizational transformation, technological adoption cascades, political polarization dynamics, and collective behavior shifts. Unlike predictive models claiming to forecast specific futures, this framework maps possibility spaces and transition conditions, offering researchers and practitioners a rigorous tool for navigating uncertainty. The contribution is methodological: a universal analytical structure for social futures that complements existing approaches including Causal Layered Analysis, backcasting, and transition management.