Symmetry Breaking and Regulation in Algorithmic Decision Systems: A Metaheuristic-Based Bias Intervention Module for Business Development Processes

Cognitive bias introduces structural imbalance in exploration and exploitation within adaptive decision systems, yet existing approaches emphasize outcome accuracy or bias reduction while offering limited explanation of how internal decision structures regulate distortion during iterative search. This study develops a metaheuristic-based bias intervention module as a computational artifact for examining symmetry regulation at the process level of biased decision-making. Using controlled computational experiments, the study compares baseline, conventional metaheuristic, and intervention configurations through structural indicators that characterize decision accuracy, convergence stability, symmetry regulation, and bias reduction. The results show that adaptive decision coherence emerges through regulated structural adjustment rather than symmetry maximization. Across evaluated configurations, systems that maintain intermediate symmetry exhibit stable convergence and effective bias regulation, whereas configurations that preserve higher symmetry display structural rigidity and weaker regulation despite high outcome accuracy. These findings reposition cognitive bias as a structural force shaping adaptive rationality in algorithmic decision systems and advance design science research by expressing cognitive balance as measurable computational indicators for process-level analysis of regulated decision dynamics.