CAREER: Decisions with Multiple and Dependent Objectives

构成任何决策基础的三个要素是：（1）备选方案，或我们能做什么;（2）信息，或我们知道什么;（3）偏好，或我们喜欢什么。概率分布捕获决策的信息元素，多属性效用函数捕获具有多个目标（属性）的决策中的偏好。 因此，多属性效用函数的构造是决策分析的基本步骤。不幸的是，如果我们希望捕获不同属性之间的相互作用（效用依赖关系），在实践中也是一项困难的任务。例如，如果我们身体健康，预期会活很长时间，而如果我们生病，预期会很快死去，那么我们对将自己的一些财富置于风险中的态度可能会有所不同。 因此，重要的是要捕捉这两个属性之间的依赖关系，如果我们希望提供一个准确的表示我们的偏好在这个决策问题。否则，我们最终可能会做出错误的决定。本研究的目的是提供一个通用的框架，用于构建多属性效用函数，捕捉多目标决策中属性之间的效用依赖关系。该研究将通过将其应用于各种实际应用来验证，例如汽车工业，全球变暖决策以及医疗决策。研究成果将通过会议和期刊论文传播。这些算法还将通过基于网络的决策支持系统向公众提供，以帮助决策。此外，该奖项将有助于为伊利诺伊州尚潘县青少年拘留中心的青少年提供决策培训，还将帮助培训一群青少年，即同伴大使小组，他们将学习决策技能，并在青少年中心将其教授给同龄的青少年。

The three elements that form the basis of any decision are (1) The alternatives, or what we can do; (2) The information, or what we know; and (3) The preferences, or what we like. A probability distribution captures the information element of the decision and a multiattribute utility function captures the preferences in decisions with multiple objectives (attributes). The construction of a multiattribute utility function is therefore a fundamental step in decision analysis. Unfortunately, it is also a difficult task to perform in practice if we wish to capture the interactions (utility dependence relations) among the different attributes. We might, for example, have a different attitude toward placing some of our wealth at risk if we were healthy and expected to live a long time than if we were ill and expected to die soon. It is therefore important to capture this dependence relation between the two attributes if we wish to provide an accurate representation of our preferences in this decision problem. Otherwise, we may end up making the wrong decision. The objective of this research project is to provide a general framework for constructing multiattribute utility functions that capture the utility dependence relations among the attributes in decisions with multiple objectives. The research will be validated by applying it to a variety of practical applications such as the automobile industry, global warming decisions, as well as medical decision making. The research results will be disseminated through conferences and journal papers. The algorithms will also be made available to the public through a web-based decision support system to help with decision making. In addition, the award will help provide decision training to teens at the Juvenile Detention Center in Champaign County, IL, and will also help train a group of Teens, the Peer Ambassadors group, who will learn decision skills and teach it to teens of their same age at the Juvenile Center.