From data to direction: a cybernetic system of strategic prediction under bounded rationality

Purpose This study adopts a conceptual and design-oriented approach by integrating bounded rationality, sensemaking, strategy-as-practice, and second-order cybernetics. It develops a mechanism-based model of strategic prediction structured as a second-order cybernetic system in which outcome feedback updates the observer's interpretive state across prediction cycles. Design/methodology/approach This study adopts a conceptual and design-oriented approach by integrating bounded rationality, sensemaking, strategy-as-practice, and second-order cybernetics systems. This study proposes that strategic prediction operates through three stages: pattern-based complexity reduction, categorical interpretation, and directional inference. Outcome discrepancies either regulate subsequent actions or update the observer's interpretive state, allowing similar patterns to produce different strategic directions across prediction cycles. The findings demonstrate that prediction does not require analytical optimization but can emerge through simplified and interpretable mechanisms based on second-order cybernetics. Findings The study proposes that strategic prediction operates through a three-stage system: pattern-based complexity reduction, categorical interpretation, and directional inference. These stages form a recursive process in which action outcomes update subsequent interpretations and may alter the directional orientation (maintain, adjust, or shift). The findings demonstrate that prediction does not require analytical optimization but can emerge through simplified, interpretable, and contextually grounded cybernetic processes. Research limitations/implications As a conceptual study, the framework is not empirically tested but offers a foundation for future research on how prediction systems are enacted across organizational contexts. This opens avenues for examining hybrid prediction architectures that combine interpretive and analytical approaches. Practical implications The proposed system enables managers to generate strategic directions using routinely available secondary data without requiring advanced analytics. By emphasizing interpretability and usability, it supports decision-making in resource-constrained settings. The directional outputs – maintain, adjust, and shift – provide actionable guidance that facilitates coordination, communication, and adaptive strategic responses in the face of uncertainty. Social implications This framework enables managers to engage in strategic prediction using routinely available data, supporting anticipatory decision-making without reliance on advanced analytics. It enhances usability, transparency, and adaptability in contexts characterized by resource and capability constraints. Originality/value This study contributes to the literature by conceptualizing strategic prediction as a second-order cybernetic system in which feedback updates the observer's interpretive state, shifting the focus from forecasting accuracy to directional orientation through an explainable and usable mechanism.