Centre for Water Systems, University of Exeter, Exeter, United Kingdom.
Integr Environ Assess Manag. 2012 Jul;8(3):456-61. doi: 10.1002/ieam.192. Epub 2011 Jun 23.
An integrated participatory approach based on Bayesian belief network (BBN) and evolutionary multiobjective optimization is proposed as an efficient decision-making tool in complex management problems. The proposed methodology incorporates all the available evidence and conflicting objectives to evaluate implications of alternative actions in the decision-making process and suggests best decision pathways under uncertainty. A BBN provides a framework within which the contributions of stakeholders can be taken into account. It allows a range of different factors and their probabilistic relationship to be considered simultaneously. It takes into account uncertainty by assigning probability to those variables whose states are not certain. The integration of BBN with evolutionary multiobjective optimization allows the analysis of tradeoff between different objectives and incorporation and acknowledgement of a broader set of decision goals into the search and decision-making process. The proposed methodology can be used as a decision support tool to model decision-making processes for complex problems. It deals with uncertainties in decision making pertaining to human behavior and checks for consistency of the developed BBN structure and the parameters of the probabilistic relationship by uncovering discrepancies in the decision analysis process (e.g., bias in completeness or redundancy of the model based on a utility function). It generates a set of efficient management options (appropriate combinations of interventions) that balances conflicting objectives. The effectiveness of the proposed methodology is discussed through application to a real case study. It is shown that it successfully identifies any inconsistencies in the developed BBN models and generates large numbers of management options that achieve an optimal tradeoff between different objectives.
提出了一种基于贝叶斯信念网络（BBN）和进化多目标优化的综合参与式方法，作为复杂管理问题中的有效决策工具。所提出的方法结合了所有可用的证据和冲突的目标，以评估替代行动在决策过程中的影响，并在不确定条件下建议最佳决策路径。BBN 提供了一个框架，可以考虑利益相关者的贡献。它允许同时考虑一系列不同的因素及其概率关系。它通过对那些状态不确定的变量赋予概率来考虑不确定性。BBN 与进化多目标优化的集成允许分析不同目标之间的权衡，并将更广泛的决策目标纳入搜索和决策过程。所提出的方法可用作决策支持工具，用于对复杂问题的决策过程进行建模。它处理与人类行为相关的决策不确定性，并通过揭示决策分析过程中的差异（例如，基于效用函数的模型的完整性或冗余性的偏差）来检查所开发的 BBN 结构和概率关系的参数的一致性。它生成一组有效的管理选项（干预措施的适当组合），以平衡冲突的目标。通过应用于实际案例研究来讨论所提出的方法的有效性。结果表明，它成功地识别了所开发的 BBN 模型中的任何不一致，并生成了大量的管理选项，实现了不同目标之间的最佳权衡。
