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<p>This research examines how third parties recognize bias blind spots in business decision-making by using matrix-based graph models. We use the mathematical, theoretical, and philosophical foundations of the Graph Model for Conflict Resolution to emphasize the significance of recognition of bias blind spots as a means to enhance informed decision-making. We introduce the Bias Blind Spots Graph Model and provide a mathematical framework for modeling bias blind spots. Additionally, we provide evidence of the effectiveness of accounting for the presence of bias blind spots that influence decision-making within the context of Japan’s <i>Skymark Airlines</i>. In showcasing the theoretical underpinnings and practical applicability of this model, we contribute to identifying bias blind spots in reasoning and judgment. Further, we extend the model by providing mathematical foundations for considering decision-maker preference and outcome state stability/equilibrium. Ultimately, our findings offer valuable insights for both researchers and third parties to model and evaluate the influences of cognitive bias blind spots in decision-making processes in organizational settings.</p>

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Modeling and Evaluating the Impact of Bias Blind Spots on Business Decisions Through Matrix-Based Graph Models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月23日

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  • Kazuhiko Konno,
  • Andrew A. Hanna

摘要

This research examines how third parties recognize bias blind spots in business decision-making by using matrix-based graph models. We use the mathematical, theoretical, and philosophical foundations of the Graph Model for Conflict Resolution to emphasize the significance of recognition of bias blind spots as a means to enhance informed decision-making. We introduce the Bias Blind Spots Graph Model and provide a mathematical framework for modeling bias blind spots. Additionally, we provide evidence of the effectiveness of accounting for the presence of bias blind spots that influence decision-making within the context of Japan’s Skymark Airlines. In showcasing the theoretical underpinnings and practical applicability of this model, we contribute to identifying bias blind spots in reasoning and judgment. Further, we extend the model by providing mathematical foundations for considering decision-maker preference and outcome state stability/equilibrium. Ultimately, our findings offer valuable insights for both researchers and third parties to model and evaluate the influences of cognitive bias blind spots in decision-making processes in organizational settings.