基于贝叶斯推断的复杂工程项目动态风险评估与决策

Dynamic Risk Assessment and Decision-Making for Complex Engineering Projects Based on Bayesian Inference

摘要: 在当前复杂多变的经济环境下，工程项目投资决策的科学性、风险管理的有效性及其对投资收益的保障成为项目成功的核心议题。传统投资决策过程常面临信息不对称、风险评估方法主观或依赖大量数据、以及决策支持不足等挑战，制约了决策的精准度和风险应对的动态性。为应对这些挑战，本文致力于构建一个集成的工程项目投资决策动态风险评估与智能支持框架。核心创新在于引入贝叶斯推断，特别是利用正态–逆伽马共轭分布模型，对项目投资中的关键风险参数进行估计与预测，该方法在处理小样本数据和不确定性方面具有独特优势。论文详细阐述了风险评估体系的具体构建方法，包括风险识别、基于N-IG模型的风险量化分析、以及风险等级评定步骤。进一步，本文设计了相应的决策支持系统(DSS)的概念架构与实施路径。该DSS旨在整合N-IG模型的分析结果，为项目投资提供动态的风险预警、多策略比选(如风险规避、减轻、转移和接受)以及投资收益预测支持，从而提升决策的科学性和智能化水平。论文不仅探讨了如何通过优化项目管理、强化成本控制及提升项目质量以实现经济、社会与环境等多维度投资收益，更强调了所提出框架与DSS在这些过程中的赋能作用。最后，本文通过一个基于模拟数据的实证研究验证了该集成框架及N-IG算法在提升风险评估准确性和辅助投资决策方面的有效性。研究成果旨在为工程项目投资决策提供一套更为先进和可操作的理论与工具，对未来工程项目在该领域的发展趋势进行了展望，具有显著的理论创新与实践应用价值。

Abstract: In the current complex and volatile economic environment, the scientific nature of investment decision-making, the effectiveness of risk management, and their assurance of investment returns have become core issues for the success of engineering projects. Traditional investment decision-making processes often face challenges such as information asymmetry, subjective risk assessment methods or reliance on large amounts of data, and insufficient decision support. These issues constrain the precision of decisions and the dynamic nature of risk response. To address these challenges, this paper is committed to constructing an integrated framework for dynamic risk assessment and intelligent support in engineering project investment decision-making. The core innovation lies in the introduction of Bayesian inference, particularly using the Normal-Inverse-Gamma (N-IG) conjugate distribution model, to estimate and predict key risk parameters in project investment. This method has unique advantages in handling small sample data and uncertainty. The paper elaborates on the specific construction methods of the risk assessment system, including risk identification, risk quantification analysis based on the N-IG model, and risk level evaluation steps. Furthermore, this paper designs the conceptual architecture and implementation path of a corresponding Decision Support System (DSS). The DSS aims to integrate the analysis results of the N-IG model to provide dynamic risk warnings, multi-strategy comparisons (such as risk avoidance, mitigation, transfer, and acceptance), and investment return prediction support for project investment, thereby enhancing the scientific and intelligent level of decision-making. The paper not only explores how to achieve multi-dimensional investment returns in economic, social, and environmental aspects through optimizing project management, strengthening cost control, and improving project quality but also emphasizes the enabling role of the proposed framework and DSS in these processes. Finally, this paper validates the effectiveness of the integrated framework and the N-IG algorithm in improving the accuracy of risk assessment and assisting investment decision-making through an empirical research based on simulated data. The research results aim to provide a more advanced and operable set of theories and tools for engineering project investment decision-making and look forward to the development trends of future engineering projects in this field, with significant theoretical innovation and practical application value.