Decision making procedure based on Multiattribute Utility theory Using Bayesian inference
Decision making procedure based on Multiattribute Utility theory Using Bayesian inference
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DOI：
10.1109/scisisis55246.2022.10001910
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Tomohiro Hayashida;I. Nishizaki;Shinya Sekizaki;Masato Ono
中科院分区：
文献类型：
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作者：
Tomohiro Hayashida;I. Nishizaki;Shinya Sekizaki;Masato Ono
在多属性效用分析中，决策者需要对不同的点和主观概率反复回答问题，以评估属性之间的权衡。值得注意的是，决策者很难准确地回答这些问题。为了避免这种困难，通过要求决策者从一对仅在两个属性值上不同的特定结果中选择一个，我们开发了一种识别多属性效用函数的方法。假设标度常数为概率分布，根据贝叶斯推理的思想，更新随机标度常数参数曲线对应的概率。在我们的方法中，我们可以在偏好信息较少的情况下获得决策者的最佳方案，并且通过添加更多的偏好信息，可以增加所提方法建议的方案对决策者来说是真正最佳的可信度。通过一个简单的数值算例说明了根据所提出的方法寻找最佳方案的过程。此外，作为多属性效用分析的应用实例，我们研究了一个农业决策问题。
In multiattribute utility analysis, it is required for the decision maker to repeatedly answer questions for indifferent points and subjective probabilities to evaluate the tradeoff between attributes. It is noted that it is difficult for a decision maker to accurately answer such questions. To avoid such difficulties, by asking the decision maker to choose one from a pair of certain outcomes that differ only in two attribute values, we develop a method for identifying the multiattribute utility function. Assuming that scaling constants are probability distributions, the probability corresponding to a parameter profile of the stochastic scaling constants is updated according to the idea of Bayesian inference. In our method, we can obtain the best alternative for the decision maker with less preference information, and by adding more preference information, it can increase the credibility that the alternative suggested by the proposed method is truly best for the decision maker. We demonstrate the procedure for finding the best alternative according to the proposed method with a simple numerical example. Furthermore, as an application example of multiattribute utility analysis, we examine an agricultural decision problem.