Cognitive Biases in Systems Engineering: Novel Approaches for Enhanced Recognition and Mitigation in Complex Systems

Anand Wanjari
Independent Researcher, USA
DOI – http://doi.org/10.37502/IJSMR.2025.81009

Abstract

Cognitive biases systematically distort human decision-making processes in complex systems engineering, contributing to project failures, cost overruns, and safety incidents. Traditional approaches to bias management rely on static checklists and awareness training, proving insufficient for dynamic engineering environments. This paper presents a novel framework integrating artificial intelligence-augmented detection, multimodal sensing, and dynamic benchmarking for enhanced cognitive bias recognition and mitigation. Through systematic analysis of 240 research papers and empirical validation, we identify five critical biases affecting systems engineers: anchoring, confirmation, optimism, omission, and preferential attachment biases. This proposed framework combines real-time detection algorithms, socio-cognitive design patterns, and iterative debiasing pipelines to achieve measurable improvements in decision quality. Experimental results demonstrate 79% accuracy in bias detection compared to 34% for untrained professionals, with 31% improvement in decision quality when using integrated mitigation protocols. The framework provides practical tools for systems engineers while establishing theoretical foundations for bias-aware engineering practices. This research contributes novel methodologies for real-time bias detection, personalized mitigation strategies, and continuous learning systems that significantly advance the state-of-the-art in human factors engineering for complex systems.

Keywords: Cognitive biases, systems engineering, decision-making, artificial intelligence, human factors, bias mitigation, complex systems, pattern recognition

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