Modeling the dynamics of belief formation: Towards a computational understanding of the timing and accuracy of probability judgments

对信念形成的动态进行建模：对概率判断的时间和准确性进行计算理解

• 批准号: 2350258
• 负责人: Timothy Pleskac
• 金额: $ 69.21万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2350258&HistoricalAwards=false
• 关键词: Modeling; dynamics; belief; formation; Towards

The next time you need a forecast, stop and ask yourself if you could wait for it. Chances are, especially in this age of accelerations we live in, you want that forecast now. Not in a few minutes. Not in an hour. Certainly not after the forecaster can collect more information. You want it now. You want the best estimate based on the information they have right then. This demand implies that accurate and timely forecasts are valued. How does time pressure impact subjective probability judgments (SPs), how do they change over time, and how much of a trade-off is there between accurate forecasts and timely ones? It is hard to answer these questions because extant theories of SPs have focused on accuracy. Most of them are silent about how SPs evolve as they are constructed with the forecaster's information. This project seeks to answer these questions using computational modeling and behavioral experiments to map the time course of SPs. First, the computational model predicts the SPs people generate in response to questions such as "What is the probability that the University of Kansas men's basketball team will win this year's tournament” and predicts the time it takes people to generate the judgment. Second, a set of empirical studies advance understanding of how people generate the judgments and how time pressure impacts them. The computational framework developed in this project informs the design of prediction polls in terms of number of questions, time pressure, and incentive structures. The project also serves to train students in computational modeling and advance STEM education as the PI integrates data science training within the liberal arts and science curriculum. Developing methods to model response times and judgments create opportunities to expand algorithms' predictive power and provide a channel for social and behavioral scientists to play an active role in developing the field of data science.This project seeks to provide a dynamic account of how people generate subjective probability (SP) forecasts. This proposal has three aims. The first aim is to develop a computational model of belief formation and the dynamics of SPs that result from this process (Modeling the Dynamics of SPs Aim). Such a model enables to predict SPs, how they change over the briefest of time intervals as people construct a belief and predict how time pressure impacts SPs. But, modeling the dynamics of SPs requires a mechanistic understanding of how belief evolves. To this end, the second aim empirically tests a set of consistency principles of contemporary theories of SPs (Tests of Consistency Principles Aim). These principles state that the evidence or support people recruit about a hypothesis is independent of the alternative hypotheses. Analogous assumptions have been made in the domain of preference, and violations are well established via so-called context effects. These context effects have been diagnostic in identifying the cognitive architecture underlying the construction of preference. Data suggest this may also be the case for the construction of belief. This project rigorously tests these effects across time. Using the computational model of SPs the project also examines the optimal policy for trading off speed and accuracy as forecasters report their SPs over a series of to-be-predicted events. Together, the project empirically-validates a computational framework of SPs that help isolate mechanisms that may inhibit people from giving accurate and timely forecasts; and develop interventions to improve the efficiency of obtaining SPs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

下一次当你需要一个预测时，停下来问问自己是否可以等待。很有可能，尤其是在我们生活的这个加速时代，你现在就想要那个预测。几分钟后就不是了。一个小时内不会。当然不是在预测者可以收集更多的信息之后。你现在就要。你想根据他们当时掌握的信息做出最佳估计。这一要求意味着准确和及时的预测是有价值的。时间压力如何影响主观概率判断（SP），它们如何随着时间的推移而变化，以及在准确预测和及时预测之间有多大的权衡？这些问题很难回答，因为现存的SP理论都集中在准确性上。他们中的大多数是沉默的SP如何演变，因为他们是用预测者的信息构建的。本项目旨在通过计算建模和行为实验来绘制SP的时间过程来回答这些问题。首先，计算模型预测人们在回答诸如“堪萨斯大学男子篮球队赢得今年锦标赛的概率是多少”之类的问题时产生的SP，并预测人们产生判断所需的时间。其次，一系列实证研究促进了对人们如何产生判断以及时间压力如何影响判断的理解。在这个项目中开发的计算框架通知预测民意调查的问题，时间压力和激励结构的数量方面的设计。该项目还用于培训学生计算建模和推进STEM教育，因为PI将数据科学培训整合到文科和理科课程中。开发响应时间和判断的建模方法，为扩大算法的预测能力创造了机会，并为社会和行为科学家在数据科学领域的发展中发挥积极作用提供了渠道。本项目旨在提供人们如何产生主观概率（SP）预测的动态解释。这项建议有三个目的。第一个目标是开发一个计算模型的信念形成和动态的SP，从这个过程中产生的（建模动态的SP目的）。这样的模型能够预测SP，当人们构建信念时，它们如何在最短的时间间隔内变化，并预测时间压力如何影响SP。但是，对SP的动态建模需要对信念如何演变的机械理解。为此，第二个目标实证测试的一致性原则的当代理论的SP（一致性原则的目标测试）。这些原则指出，人们对一个假设的证据或支持是独立于替代假设的。在偏好领域也有类似的假设，通过所谓的语境效应，可以很好地建立违规行为。这些背景效应在识别偏好构建的认知结构方面具有诊断作用。数据表明，这可能也是信念构建的情况。这个项目严格测试了这些影响。使用SPs的计算模型，该项目还研究了权衡速度和准确性的最佳策略，因为预报员在一系列待预测的事件中报告他们的SPs。该项目共同验证了SP的计算框架，有助于隔离可能阻止人们提供准确和及时预测的机制;并制定干预措施以提高获得SP的效率。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。