Collaborative Research: Learning, Behavior, and Design in Diffusion Processes

该奖项资助了利用经济理论和实验室实验研究社交媒体平台上的信息和学习的研究。对许多人来说，社交媒体是越来越重要的新闻来源。用户在这类平台上看到的内容取决于其他用户选择发布和分享的内容。它还取决于平台开发人员用来生成新闻提要的算法。研究人员将调查社交媒体平台的用户和开发者的决定如何影响人们的学习内容。用户可能会看到哪些类型的内容？人们如何处理这些内容以形成对相关问题的信念？这些信念什么时候可能是准确的？这项研究与最近的争论有关，即某些用户和开发者的选择是否会导致误导性或不正确信息在社交媒体上的传播。例如，从长远来看，专注于展示最受欢迎的内容(而不是随机内容)的社交媒体新闻馈送是帮助还是损害了人们信念的准确性？在该项目的第一部分，研究人员将开发一个理论模型，描述在人们通过发布、分享和再分享关于世界状态的“信号”(例如，新闻故事)副本来学习的环境中的社会学习。该模型将关注当人们的行为驱动的信息传播过程也影响他们的学习时，人们的信念和行为是如何共同演变的。这项研究将得出以下结果：该平台的信号采样算法决定了向用户显示哪些信号，如何影响社会学习的准确性和人们信念的一致性。该项目的第二部分将在实验室实验中验证这些预测。调查人员将进行社交学习游戏，让受试者看到前人的信号，并选择认可这些信号中的一部分。在选择向未来用户显示哪些过去的信号时，不同的处理方法将改变算法对以前用户背书的权重。该项目的第三部分将处理复杂扩散模型中的“临界点”。这部分研究将确定关于个人行为的简单条件，这些条件决定是否存在扩散不连续地从(A)达到非常小一部分人口到(B)达到大部分人口的“临界点”。作者将调查这样的过渡是否总是一帆风顺的。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award funds research that uses economic theory and laboratory experiments to study information and learning on social media platforms. Social media are an increasingly important source of news for many people. The content that users see on such platforms depends on what other users choose to post and share. It also depends on the algorithms that the platform developers use to generate news feeds. The researchers will investigate how the decisions of the users and developers of social media platforms affect what people learn. What types of content are users likely to see? How do people process this content to form beliefs about the relevant issues? When are these beliefs likely to be accurate? The research relates to recent debates about whether certain user and developer choices contribute to the spread of misleading or incorrect information on social media. For example, does a social media news feed that focuses on showing the most popular content (as opposed to random content) help or hurt the accuracy of people's beliefs in the long run? In the first part of the project, the researchers will develop a theoretical model describing social learning in settings where people learn by posting, sharing, and re-sharing copies of “signals” (e.g., news stories) about the state of the world. The model will focus on how people's beliefs and behavior co-evolve when the information diffusion process driven by people's actions also influences their learning. This research will yield results about how the platform's signal-sampling algorithm, which determines which signals get shown to users, affects the accuracy of social learning and the extent of agreement in people's beliefs. The second part of the project will test these predictions in a laboratory experiment. The investigators will conduct social-learning games where subjects see predecessors' signals and choose to endorse a subset of those signals. In choosing which past signals to show to future users, different treatments will vary the weight put by the algorithm on endorsements by previous users. The third part of the project will deal with the “tipping point” in complex diffusion models. This part of the research will identify simple conditions on individual behavior that determine whether there exists a “tipping point” where the diffusion discontinuously switches from (a) reaching a very small fraction of the population to (b) reaching a large fraction of the population. The authors will investigate whether such transitions always happen smoothly.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.