Enhancing Intelligence Analysis with Cognitive Models: Addressing Uncertainty, Biases and Decision-Making Challenges

30.06.2025 | Nedim HAVLE

This research aims to explore the potential and effectiveness of integrating cognitive models into intelligence analysis. The study utilizes a literature review and fictional case analyses to assess the adaptation of models such as Waltz’s Integrated Reasoning Process, the Drift Diffusion Model (DDM), the Time-Based Resource Sharing Model (TBRS), Prospect Theory, Quantum Decision Theory (QDT), and Cultural Consensus Theory (CCT) to the field of intelligence analysis. The findings suggest that these models can enhance the accuracy, speed, and reliability of analytical processes. For example, the DDM is found to optimize decision-making under time constraints, Prospect Theory provides a rational framework for risk management, and CCT incorporates cultural differences into the analytical process. Furthermore, these models address key challenges in intelligence analysis, including uncertainty, cognitive biases, and the management of multiple data sources. The study concludes that cognitive models offer an innovative approach to intelligence analysis, with the potential to develop tools that improve analysts’ decision-making capabilities. However, it underscores the necessity of advancements in analyst training, data quality, and technological infrastructure to ensure the effective implementation of these models. This research bridges a gap in the existing literature and establishes a foundation for future empirical studies.

Havle, N. (2025). Enhancing Intelligence Analysis with Cognitive Models: Addressing Uncertainty, Biases and Decision-Making Challenges. Journal of Intelligence Research and Studies, 4(2), pp. 133-156, DOI: 10.61314/icad.1654549