CHS: Small: Collaborative Research: Mobile Language-Based Aids for Intelligent Decisions

CHS：小型：协作研究：基于移动语言的智能决策辅助工具

• 批准号: 1527302
• 负责人: Juan Gilbert
• 金额: $ 20.33万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527302&HistoricalAwards=false
• 关键词: CHS; Small; Collaborative; Research; Mobile

This project will create a solid science base for computer-based conversational decision systems, by integrating psychological decision theories with an experiential understanding of human decision-making, having the potential to impact many domains. People make mundane and critical consumption decisions daily, ranging from what air conditioning filters to buy to which health insurance plan to enroll. Rational choice theory predicts that consumers can make choices to maximize their desired attributes. However, human cognitive limitations coupled with decision complexities often require people to develop preferences quickly and employ multiple decision strategies to arrive at a choice. Sub-optimal consumption decisions hurt the individual, the family, the economy, and the nation. This project will enhance people's abilities to make informed decisions on-the-go, when they lack domain knowledge, time and cognitive resources for optimal decision-making. These benefits are particularly relevant to aging users, those with cognitive, perceptual and motor impairments, and those functioning in multitask environments.Past research on decision aids has focused on single-decision strategies using invariant algorithms, falling short of solving real decision problems, which are inherently contingent on the decision-maker and task. Further, benefits of a mobile, conversational setting have been under-employed in developing decision aids. The goal of this project is to design, develop, and evaluate conversational, mobile decision-aids (MODA) for consumption decisions based on adaptive, intelligent information retrieval and decision strategy use. The three main scientific objectives and methods are: 1. Capture rich spoken dialog between consumers and service associates as they go through different types of decision-making strategies in a physical store, using observational studies. 2. Develop a decision inference and dialog management model that (a) uses inputs from spoken consumer interactions, (b) applies decision inference rules to predict the consumer's decision strategy, (c) tailors MODA's subsequent dialog to their decision strategy, and (d) informs the consumer's decision-making process with data retrieved from product specifications and reviews. 3. Conduct field experiments to assess MODA effectiveness with respect to cognitive, social, and decision outcomes among varied decision-contexts and user characteristics.

该项目将通过将心理决策理论与对人类决策的经验理解相结合，为基于计算机的对话决策系统奠定坚实的科学基础，具有影响许多领域的潜力。人们每天都会做出平凡而关键的消费决定，从购买什么空调过滤器到计划参加什么医疗保险。理性选择理论预测，消费者可以做出选择，以最大化他们想要的属性。然而，人类认知的局限性加上决策的复杂性，往往要求人们迅速形成偏好，并使用多种决策策略来做出选择。次优的消费决策伤害了个人、家庭、经济和国家。这个项目将提高人们在缺乏领域知识、时间和认知资源进行最佳决策的情况下，在旅途中做出明智决策的能力。这些好处与老年用户、有认知、知觉和运动障碍的用户以及那些在多任务环境中发挥作用的用户尤其相关。过去关于决策辅助的研究主要集中在使用不变算法的单一决策策略上，而不是解决真正的决策问题，这些问题本质上取决于决策者和任务。此外，在开发决策辅助工具时，移动对话环境的好处一直没有得到充分利用。该项目的目标是设计、开发和评估基于自适应、智能信息检索和决策策略使用的用于消费决策的对话式移动决策辅助工具(MODA)。三个主要的科学目标和方法是：1.使用观察性研究，捕捉消费者和服务伙伴在实体店中通过不同类型的决策策略进行丰富的口头对话。2.开发决策推理和对话管理模型，该模型(A)使用来自消费者口头交互的输入，(B)应用决策推理规则来预测消费者的决策策略，(C)使Moda的后续对话适应他们的决策策略，以及(D)用从产品规格和评论中检索的数据通知消费者的决策过程。3.进行现场实验，评估MODA在不同决策情境和用户特征中对认知、社交和决策结果的有效性。