SBE-UKRI:A Novel Theory of Ordered Judgment Processes

SBE-UKRI:有序判断过程的新颖理论

• 批准号: ES/Z000084/1
• 负责人: Christian Wagner
• 金额: $ 45.53万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FZ000084%2F1
• 关键词: SBE; UKRI; Novel; Theory; Ordered

OverviewThere are three core elements to decision making: judgment, preference, and choice (Fischhoff & Broomell, 2020). Judgments represent how people come to understand the outcomes associated with choices along with their probabilities of occurrence. This proposal focuses on how people form judgments by aggregating multiple pieces of evidence gathered from different information sources. Much of the literature on judgment has relied on linear models as the prominent theoretical basis for understanding such judgments (e.g., Broomell & Budescu, 2009; Dawes, 1971; Karelaia & Hogarth, 2008). We propose a new theory of ordered judgment that fuses psychological theory with operational and theoretical advances from computer science in the areas of data aggregation and artificial intelligence.Intellectual MeritWe propose to advance psychological theory beyond linear models by (1) leveraging computationally simple ordering processes with statistically desirable properties for information aggregation, (2) mimicking the relative nature of perception in judgment, and (3) seamlessly integrating this approach with linear theories previously used. Researchers have implicated ordering as a potential component of cognition but to date, lack the ability to empirically test for its presence. Our theory of ordered judgment will provide the first operational framework for empirical tests of the role of ordering in judgment and beyond. We will develop and test this novel theory through three research objectives. The first objective is to develop a predictive model of judgment based on preliminary work (Broomell & Wagner, 2023). Such a model will facilitate targeted experimentation to detect whether ordered judgment processes can account for human behavior. The second objective is to use lab experiments to understand the degree to which order-based processes naturally fit with judgment processes and can predict human behavior. These studies will reveal systematic and predictable behavior in how judgments react to momentary changes in context. The third objective is to develop methods for estimating the free parameters of the ordered judgment model from observed judgments. The development of such an estimation procedure will allow for a more detailed decomposition of judgments into stable and dynamic priorities that drive judgment. Additionally, such an estimation procedure would have implications for model fitting broadly in psychological and computer science work.Broader ImpactsWe anticipate that this theory integration will have impacts for both psychological and computer science research that go well beyond the intellectual merits. For psychology, our theory of ordered judgment has many implications for how to display data to facilitate accurate processing that will be useful for decision-support and human factors work in contexts ranging from graphical user interfaces to operating machinery. For computer science, we anticipate that this work will contribute to the crucial area of explainable artificial intelligence by articulating direct links between pervasive linear and non-linear aggregation processes and human reasoning. This can afford mechanisms to understand, evaluate and validate machine-learning driven decision-making approaches in critical applications such as security and defense, energy, and healthcare. Further, algorithmic implementations of the proposed theory hold the potential to offer efficient means of aggregating information in machine learning including neural networks, as alluded to in Kreinovich (2022). The work in this proposal will also serve to train doctoral students and postdoctoral reasearchers in interdisciplinary and internationally collaborative research leveraging mathematical, computational, and empirical methods. The results of this work will complement the PI and co-PI's teaching and instrunction at undergraduate and graduate levels in the US and the UK.

概述是决策制定的三个核心要素：判断，偏好和选择（Fischhoff＆Broomell，2020年）。判断代表人们如何理解与选择相关的结果及其发生概率。该提案的重点是人们如何通过从不同信息来源收集的多种证据来形成判断。关于判断的许多文献都依靠线性模型作为理解这种判断的重要理论基础（例如Broomell＆Budescu，2009; Dawes，1971; Karelaia＆Hogarth，2008）。我们提出了一种新的有序判断理论，该理论将心理理论与计算机科学的运作和理论进步融合在一起，在数据聚集和人工智能领域的领域中，智能元素梅里特我们建议通过（1）利用计算上简单的订购过程，并通过统计上的属性来将心理理论超越线性模型，以实现信息的质量，并在（2）相对（2）（2）（2）（2）（2）（2）（3）以前使用的线性理论的方法。研究人员将秩序牵连为认知的潜在组成部分，但迄今为止，缺乏经验测试其存在的能力。我们的有序判断理论将为有序在判断及其他方面的作用提供经验测试的第一个操作框架。我们将通过三个研究目标开发和测试这一新颖理论。第一个目标是基于初步工作开发判断的预测模型（Broomell＆Wagner，2023）。这样的模型将促进有针对性的实验，以检测有序的判断过程是否可以解释人类行为。第二个目标是使用实验室实验来了解基于顺序的过程自然符合判断过程的程度，并可以预测人类的行为。这些研究将揭示判断如何对上下文中的短暂变化做出反应的系统和可预测的行为。第三个目标是开发从观察到的判断中估算有序判断模型的自由参数的方法。这样的估计程序的发展将使判断更详细地分解为稳定而动态的优先事项，以推动判断力。此外，这种估计程序将对在心理和计算机科学工作中广泛拟合的模型具有影响。BroaderImpactswe预计，这种理论整合将对心理和计算机科学研究产生影响，这远远超出了知识上的优点。对于心理学，我们的有序判断理论对如何显示数据以促进准确的处理具有许多影响，这将对决策支持有用，而人为因素在从图形用户界面到操作机械的上下文中起作用。对于计算机科学，我们预计这项工作将通过阐明普遍线性和非线性聚合过程与人类推理之间的直接联系，从而有助于解释人工智能的关键领域。这可以提供理解，评估和验证机器学习驱动决策方法（例如安全和防御，能源和医疗保健）中的机制。此外，所提出的理论的算法实施具有有效的手段来汇总机器学习中的信息，包括神经网络，如克雷诺维奇（Kreinovich）（2022）所述。该提案中的工作还将用于培训跨学科和国际协作研究的博士生和博士后研究人员利用数学，计算和经验方法。这项工作的结果将补充PI和Co-Pi在美国和英国的本科和研究生水平的教学和仪器。