Forecasting in Complex Adaptive Systems: A Systematic Literature Review and Integrative Framework Bridging Geopolitical Conflict and Competitive Markets

Abstract

Forecasting in geopolitical and competitive environments remains highly unreliable despite advances in analytics, machine learning, and predictive modeling. This study argues that many forecasting failures emerge not only from methodological limitations, but from fragmented representations of complex adaptive systems. Using a systematic literature review approach, the paper synthesizes forecasting research across geopolitical conflict, strategic management, signaling theory, and complex systems literature. The findings reveal that existing forecasting models frequently prioritize measurable variables while underrepresenting signaling dynamics, institutional resilience, perception-based interpretation, nonlinear interaction, and recursive feedback processes. In response, the study develops the Adaptive Forecasting Systems Framework (AFSF), which conceptualizes forecasting as a dynamic system-level process shaped by structural capacity, strategic signaling, adaptive interaction, and evolving system dynamics. The study contributes to forecasting and strategic management literature by reframing prediction as an adaptive interpretive process rather than a static estimation exercise. It further demonstrates that geopolitical conflicts and competitive markets share important structural characteristics associated with uncertainty, signaling asymmetry, and feedback-driven system evolution. The paper concludes that forecasting robustness increasingly depends on the ability to interpret evolving interaction structures rather than relying solely on predictive precision.