Robust Interactive Decision Analysis: Extensions

决策分析在面对不确定性和多个相互冲突的目标时提供了一种理论上有吸引力的决策方法。不幸的是，大多数决策者（DMs）都不愿意采用这种基本原理方法来解决重要的决策问题，部分原因是在应用其概念和方法时对他们提出了严格的信息要求。该研究项目涉及开发一种新的交互式方法和原型决策支持系统（DSS），用于执行决策分析，通过排除DM直接，准确和/或完全评估他/她的信仰和品味的需要，从而放宽其严格的测量要求。该方法的哲学基础是基于健壮性，旨在使决策分析成为实践中决策制定的潜在更容易获得和可行的工具。调查人员最初的努力集中在基于不精确、不完整的信念或品味的不确定性下做出决定。他们现在扩展了我们的方法(1)，包括在信仰和品味都不精确和/或不完全指定的不确定情况下做出决策；(2)当权衡和单个属性值函数都不精确和/或不完全指定时，包括多标准决策问题。(3)将基于知识和经验的智能整合到我们的RID方法和决策支持系统中进行预测和学习，以减少对决策支持系统的认知需求，加快解决方案的过程；能够同时支持不确定性和多准则决策的智能决策支持系统。该研究的贡献将是理论和实践。例如，在理论层面上，在不精确概率和效用函数条件下的决策分析研究应该揭示关于在不确定条件下做出决策时不精确信念、品味和选择偏好之间关系动态的新见解。此外，在多属性效用分析中，将属性权重和条件值/效用函数作为随机变量处理，应该允许我们指定与备选方案相关的不精确诱导聚合值/效用区间的概率函数。这些信息允许使用随机优势概念来过滤较差的替代方案。从实用主义的角度来看，这项研究的结果应该导致一种方法和综合决策支持的发展，这应该允许决策分析在实践中更普遍和成功的应用。

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.