摘要翻译：
使用判决电路框架对影响图进行灵敏度分析是特别方便的，因为关于每个参数的偏导数是容易获得的[Bhattacharjya和Shachter，2007；2008]。本文提出了三种非线性灵敏度分析方法，它们利用了这种偏导数信息，因此不需要多次重新评估决策情况。具体地说，我们展示了如何在决策情况下有效地比较策略，执行对风险厌恶的敏感性和计算完美套期保值的价值[Seyller，2008]。
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英文标题：
《Three new sensitivity analysis methods for influence diagrams》
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作者：
Debarun Bhattacharjya, Ross D. Shachter
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Performing sensitivity analysis for influence diagrams using the decision circuit framework is particularly convenient, since the partial derivatives with respect to every parameter are readily available [Bhattacharjya and Shachter, 2007; 2008]. In this paper we present three non-linear sensitivity analysis methods that utilize this partial derivative information and therefore do not require re-evaluating the decision situation multiple times. Specifically, we show how to efficiently compare strategies in decision situations, perform sensitivity to risk aversion and compute the value of perfect hedging [Seyller, 2008].
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PDF链接：
https://arxiv.org/pdf/1203.3467