Collaborative Research: Causal Structures: Experiments and Machine Learning

协作研究：因果结构：实验和机器学习

• 批准号: 2315664
• 负责人: Sevgi Yuksel
• 金额: $ 12.64万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2315664&HistoricalAwards=false
• 关键词: Collaborative; Research; Causal; Structures; Experiments

To make decisions, people must rely on their understanding of the relevant environment: what are the causes and outcomes of the various forces at play. In other words, in many settings, including economic ones, people rely on subjective causal models (or narratives) to understand the world. Such models help agents organize and interpret information, allowing them to make forecasts about the future, and providing them with a way to evaluate counterfactuals. The main goal of this research is to take a first step towards understanding how economic agents come to adopt (possibly incorrect) models and how this depends on the information available to them. The researchers will approach this topic from two different perspectives. The first involves a series of experiments that aim to understand how people’s subjective models arise from patterns they identify in data. Some experiments will be conducted in an abstract setting, while others involve natural context. Natural context can trigger preconceptions about how different variables are associated with each other that may help or hinder people from correctly identifying actual patterns in a set of observations. The second approach aims to better understand whether news media plays a role in heterogeneous subjective models. The goal is to study whether different news outlets organize and explain the same outcomes using different causal models.A growing literature in economic theory studies ramifications of adopting possibly incorrect subjective models, referring to economic agents relying on such models as ‘misspecified.’ But, for the most part, the literature is silent on how a person comes to adopt a subjective model to begin with, how such a subjective model may depend on the setting, and how it may be shaped by the person’s experiences. In addition, it is an open question under what conditions people adopt subjective models that are consistent with the true data generating process. The goal of this research is to take a first step towards understanding how such misspecifications may arise and how they depend on features of the data-generating process. The researchers will approach the topic from two different perspectives. A first approach involves a series of laboratory experiments to understand how people extract patterns from their observations. The novel experimental design asks subjects to organize different sets of observations (data) with the goal of making predictions in similar situations. The experimental data will let the researchers understand whether the predictions subjects make in each environment are consistent with them using some model that posits specific statistical relationships between different variables. Complemented with ancillary non-choice data that emerges as a by-product of the experimental design, the results will provide insights into how people form models of the world by studying data and how they use these models to make predictions. Experiments will be conducted both with an abstract setting and with context. Understanding how people come to adopt (possibly incorrect) models and how this is impacted by the information available to them is important to determine in what situations they are more vulnerable to being manipulated. Furthermore, it can help us design policies that are effective in correcting beliefs and inducing optimal behavior. The second approach aims to better understand whether news media plays a role in shaping heterogeneous subjective models. The goal is to study whether different news outlets organize and explain the same outcomes using different causal models. To do so, the researchers will use an end-to-end trained Machine Learning pipeline that will take text (news articles) as input and identify the main causal statements advanced in this text as output. Documenting the heterogeneous causal models propagated by news outlets is important for understanding why voters with different political affiliation disagree on the optimal response to problems that are accepted by both sides.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.

为了做出决定，人们必须依靠对相关环境的理解：各种力量的原因和结果是什么。换句话说，在许多环境中，包括经济方式，人们都依靠主观催化模型（或叙事）来理解世界。这样的模型帮助代理人组织和解释信息，使他们能够使森林人了解未来，并为他们提供一种评估反事实的方法。这项研究的主要目标是迈出第一步，朝着了解经济代理如何采用（可能是错误的）模型以及这如何取决于他们可用的信息。研究人员将从两个不同的角度处理这个主题。第一个涉及一系列实验，旨在了解人们的主题模型如何来自他们在数据中识别的模式。一些实验将在抽象的环境中进行，而另一些实验涉及自然环境。自然环境可以触发关于不同变量如何相互关联的先入为主，这可能会帮助或阻碍人们正确识别一组观测值中的实际模式。第二种方法旨在更好地了解新闻媒体是否在异质主观模型中发挥作用。目的是研究是否使用不同的因果模型组织不同的新闻媒体并解释相同的结果。经济理论研究中的越来越多的文献的影响是采用可能的不正确的主观模型，指的是依靠“误解”这样的模型的经济特工。但是，在大多数情况下，最大程度地依赖于某人对某人采用的模型的依赖，从而依赖于某人的依赖，而这些模型是如何依赖于某人的模型，而这些模型是如何依赖于该模型的依赖，并且是如何依赖于该模型的方式。经验。此外，在什么条件下，人们采用与真实数据生成过程一致的主题模型。这项研究的目的是迈出第一步，朝着了解这些错误的出现以及它们如何依赖于数据生成过程的特征。研究人员将从两个不同的角度处理该主题。第一种方法涉及一系列实验室实验，以了解人们如何从观察结果中提取模式。新型的实验设计要求受试者组织不同的观测值（数据），以便在类似情况下进行预测。实验数据将使研究人员使用某些模型在不同变量之间提出特定的统计关系的模型来了解每个环境中的预测与它们之间的预测是否一致。结果以实验设计的副产品出现，以辅助非选择数据的形式完成，结果将提供有关人们如何通过研究数据以及如何使用这些模型做出预测的世界模型的见解。实验将以抽象的环境和上下文进行。了解人们如何采用（可能是错误的）模型以及如何受到他们可用信息的影响，这对于确定在哪种情况下更容易被操纵的情况很重要。此外，它可以帮助我们设计有效纠正信念并引起最佳行为的政策。第二种方法旨在更好地了解新闻媒体是否在塑造异质主观模型中发挥作用。目的是研究不同的新闻媒体是否组织并使用不同的因果模型来解释相同的结果。为此，研究人员将使用端到端训练的机器学习管道，将文本（新闻文章）作为输入，并确定本文中先进的主要因果陈述作为输出。记录新闻媒体传播的异质因果模型对于理解为什么不同政治会员的选民对双方接受的问题的最佳反应不同意。该奖项反映了NSF的法定任务，并被认为是通过使用基金会的知识分子和更广泛影响的评估来审查Criteria来通过评估来获得支持的珍贵的。