Bayesian Rapid Optimal Adaptive Design (BROAD) for Estimating

用于估计的贝叶斯快速最优自适应设计 (BROAD)

• 批准号: 1227412
• 负责人: Colin Camerer
• 金额: $ 46.75万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1227412&HistoricalAwards=false
• 关键词: Bayesian; Rapid; Optimal; Adaptive; Design

In this project the Principal Investigators will develop methods to rapidly personalize the series of questions that are asked in an experiment or survey, adjusting new questions based on a respondent?s previous answers and the speed of their responses. The methods guarantee, mathematically, that the most informative question is being asked at each step. The software challenge is being able to recompute rapidly on a variety of computer platforms. We will also make software user-friendly and widely available. In terms of broader impacts, this research will enable scientists to figure out, faster and better, how people are different from each other in what they like and how they behave. Our method will enable creation of short surveys that yield more insight. The method will specifically be used to find out how much risk people accept, how patient they are toward future costs and benefits, and how much they like to cooperate and compete with other people. These methods can help consumers and companies figure out what kind of investments they should make, and help match workers with jobs that are ideal for them.

在这个项目中，主要研究人员将开发方法，以快速个性化的一系列问题，在实验或调查中提出的，调整基于受访者的新问题？的先前的答案和他们的反应速度。从数学上讲，这些方法保证了每一步都提出了最具信息性的问题。软件的挑战是能够在各种计算机平台上快速重新计算。我们还将使软件方便用户使用，并广泛提供。就更广泛的影响而言，这项研究将使科学家们能够更快更好地弄清楚人们在喜欢什么和行为方式方面有何不同。我们的方法将能够创建产生更多洞察力的简短调查。该方法将被专门用于了解人们接受多大的风险，他们对未来成本和收益的耐心程度，以及他们有多喜欢与他人合作和竞争。这些方法可以帮助消费者和公司弄清楚他们应该做什么样的投资，并帮助工人找到适合他们的工作。