Robust Interactive Decision Analysis: Extensions

决策分析提供了一种理论上有吸引力的方法， 在面对不确定性和多重冲突时做出决策 目标. 不幸的是，大多数决策者（DM） 不愿意对重要决定采取这种理性的态度 问题，部分是由于它对信息的严格要求， 在应用其概念和方法时。 本研究项目 包括开发一种新颖的互动方法和原型， 一种用于执行决策分析的决策支持系统（DSS）， 放宽了严格的测量要求， 对于DM来说，直接、精确地评估他/她的信仰和品味， 和/或完全。 该方法的哲学基础是基于 鲁棒性，旨在使决策分析成为一个潜在的更多 在实践中为决策提供了一个可行的工具。 的 调查人员最初的努力集中在根据 基于不精确、不完整的信念或品味的不确定性。 他们现在 扩展我们的方法（1），以包括在不确定性下做出决策 当信仰和品味都不精确和/或不完全时， （2）包括多准则决策问题， 权衡和各个属性值函数中的一个或两个， 不精确和/或不完整的规定，（3）纳入知识 和基于经验的情报融入我们的RID方法和DSS， 预测和学习，以减少对DM的认知需求， 加快解决过程，（4）开发系统 原则和原型设计的集成，智能决策支持系统， 可以同时支持不确定性下的决策， 多准则决策 研究的贡献将是理论和 务实 例如，在理论层面上， 不精确概率和效用条件下的分析 功能应该揭示关于动态的新见解， 不精确信念、品味和选择偏好之间的关系 在不确定的情况下做决定。 此外，治疗 属性权重和条件值/效用函数为随机 多属性效用分析中的变量应该允许我们指定 不精确诱导聚集的概率函数 与备选方案相关的价值/效用区间。 此类信息 允许使用随机优势概念来过滤劣 替代品. 从务实的角度来看，这一结果 研究应导致制定一种方法， 决策支持系统，应允许更普遍和成功的应用， 实践中的决策分析

Decision analysis provides a theoretically appealing approach to making decisions in the face of uncertainty and multiple conflicting objectives. Unfortunately, most decision makers (DMs) have been unwilling to adopt this rationale approach to important decision problems, in part, due to the stringent information demands it places on them in applying its concepts and methods. This research project involves the development of a novel interactive approach and prototype decision support system (DSS) for performing decision analysis which relaxes its stringent measurement requirements by precluding the need for the DM to assess his/her beliefs and tastes directly, precisely, and/or completely. The philosophical basis of the approach is based on robutness and is aimed at making decision analysis a potentially more accessible and viable tool for decision making in practice. The investigators' initial effort focused on making decisions under uncertainty based on imprecise, incomplete beliefs or tastes. They now extend our approach (1) to include making decisions under uncertainty when both beliefs and tastes are imprecisely and/or incompletely specified, (2) to include multiple criteria decision problems when either or both tradeoffs and individual attribute value functions are imprecisely and/or incompletely specified, (3) to incorporate knowledge and experience-based intelligence into our RID methodology and DSS for prediction and learning to reduce the cognitive demands on the DM and accelerate the solution process, and (4) to develop the system principles and prototype design for an integrated, intelligent DSS that can simultaneously support decision making under uncertainty and multiple criteria decision making. The contribution of the research will be both theoretical and pragmatic. For example, on a theoretical level, research in decision analysis under conditions of both imprecise probability and utility functions should reveal new insights regarding the dynamics of the relationship between imprecise beliefs, tastes, and choice preferences when making decisions under uncertainty. Further, the treatment of attribute weights and conditional value/utility functions as random variables in multiattribute utility analysis should allow us to specify the probability function of the imprecisely induced aggregated value/utility intervals associated with alternatives. Such information permits the use of stochastic dominance concepts to filter inferior alternatives. From a pragmatic perspective, the results of this research should lead to the development of a methodology and integrated DSS that should allow more pervasive and successful applications of decision analysis in practice.