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<p>In the face of an increasingly complex decision-making environment, we present the TCC (Trisecting, Clustering, Collaborative decision-making) model, which incorporates the Trisecting-Acting-Outcome (TAO) model as its foundational component. This novel framework for multi-expert systems (MES) enhances decision robustness by dynamically allocating expert weights through clustering techniques and collaborative strategies. We also introduce a method for measuring decision efficiency that combines regret analysis and scenario discovery within the TAO framework. By assessing decision objectives and preferences, we evaluate the need for strategic adjustments to effectively mitigate systemic risk. Online portfolio selection problem (OLPS) is chosen as the decision task, and numerical experiments are conducted. The results indicate that TCC achieves optimal performance in terms of comprehensive return and risk assessment criteria across different datasets, demonstrating the model’s effectiveness and stability. Additionally, the decision efficiency measure <i>k</i> does not exceed 0.16, highlighting the significant uncertainty in the OLPS decision environment and the need for effective integration with stock selection strategies.</p>

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Enhancing Decision-Making in Complex Environments: A Multi-Expert Decision System and Simulation-Driven Efficiency Measurement Approach

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • Hao Chen,
  • Changxin Xu

摘要

In the face of an increasingly complex decision-making environment, we present the TCC (Trisecting, Clustering, Collaborative decision-making) model, which incorporates the Trisecting-Acting-Outcome (TAO) model as its foundational component. This novel framework for multi-expert systems (MES) enhances decision robustness by dynamically allocating expert weights through clustering techniques and collaborative strategies. We also introduce a method for measuring decision efficiency that combines regret analysis and scenario discovery within the TAO framework. By assessing decision objectives and preferences, we evaluate the need for strategic adjustments to effectively mitigate systemic risk. Online portfolio selection problem (OLPS) is chosen as the decision task, and numerical experiments are conducted. The results indicate that TCC achieves optimal performance in terms of comprehensive return and risk assessment criteria across different datasets, demonstrating the model’s effectiveness and stability. Additionally, the decision efficiency measure k does not exceed 0.16, highlighting the significant uncertainty in the OLPS decision environment and the need for effective integration with stock selection strategies.