Strategic Decision-Making in Uncertainty: A Theoretical Model for Financial Analytics

• Emily J. Carter

  Author

This study develops a theoretical framework for strategic decision-making under uncertainty in the context of financial analytics. Drawing on the foundations of Expected Utility Theory, Prospect Theory, and Real Options Theory, the paper integrates rational optimization, behavioral biases, and dynamic flexibility into a unified model. The proposed framework conceptualizes decision-makers as boundedly rational agents who combine data-driven insights with subjective judgment while operating in environments characterized by multidimensional uncertainty. The study further incorporates financial analytics into the decision-making process, highlighting the role of predictive and prescriptive tools in improving the assessment of risks and opportunities. By embedding analytics within a theoretical structure, the model captures the interaction between algorithmic outputs and human cognition, offering a more realistic representation of modern strategic financial behavior. In addition, the framework accounts for institutional and environmental factors, including regulatory conditions and emerging ESG-related uncertainties. The findings suggest that effective strategic decision-making under uncertainty depends on the integration of analytical capabilities, behavioral awareness, and organizational flexibility. The model contributes to the literature by bridging the gap between traditional decision theory and contemporary financial analytics, while also extending existing approaches to account for dynamic learning and multidimensional uncertainty. This study provides important implications for both theory and practice. It offers a foundation for future empirical research and highlights the need for organizations to enhance data infrastructure, improve governance mechanisms, and adopt hybrid decision-making approaches that combine quantitative models with managerial expertise.

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