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

下次你需要天气预报的时候，停下来问问自己能不能等一下。很有可能，尤其是在我们生活的这个加速发展的时代，你现在就想要这样的预测。等不了几分钟。一小时内不会。当然不是在预报员收集到更多信息之后。你现在就想要。你希望根据他们当时掌握的信息做出最好的估计。这种需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（SPs），它们如何随时间变化，在准确的预测和及时的预测之间有多少权衡？这些问题很难回答，因为现有的sp理论都集中在准确性上。他们中的大多数人对sp是如何随着预测者信息的构建而演变的保持沉默。这个项目试图用计算模型和行为实验来回答这些问题，以绘制SPs的时间过程。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”等问题时产生的sp，并预测人们产生判断所需的时间。其次，一系列实证研究有助于理解人们如何产生判断以及时间压力如何影响他们。本项目开发的计算框架根据问题数量、时间压力和激励结构通知预测民意调查的设计。该项目还有助于培养学生的计算建模和推进STEM教育，因为PI将数据科学培训整合到文科和理科课程中。开发模拟响应时间和判断的方法为扩展算法的预测能力创造了机会，并为社会和行为科学家在发展数据科学领域发挥积极作用提供了渠道。该项目旨在提供人们如何产生主观概率（SP）预测的动态描述。这项提议有三个目的。第一个目标是开发一个信念形成的计算模型和这个过程中产生的SPs动力学（SPs aim的动力学建模）。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SPs的动态建模需要对信念如何演变有一个机械的理解。为此，第二个目的是对当代SPs理论的一套一致性原则进行实证检验（一致性原则目的检验）。这些原则表明，人们对一个假设的证据或支持是独立于其他假设的。在偏好领域也有类似的假设，并且通过所谓的情境效应很好地建立了违反假设的情况。这些情境效应在识别偏好构建背后的认知结构方面具有诊断作用。数据表明，信念的构建可能也是如此。这个项目严格地测试了这些影响。使用sp的计算模型，该项目还检查了在预测者报告一系列待预测事件的sp时权衡速度和准确性的最佳策略。总之，该项目经验验证了sp的计算框架，该框架有助于隔离可能抑制人们给出准确和及时预测的机制；并制定干预措施，以提高获得SPs的效率。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.