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

该奖项资助使用经济理论和实验室实验来研究社交媒体平台上的信息和学习的研究。社交媒体对许多人来说是越来越重要的新闻来源。用户在这些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人员的决定如何影响人们的学习。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？这些信念在什么时候可能是准确的？这项研究涉及到最近关于某些用户和开发人员的选择是否有助于社交媒体上误导或不正确信息的传播的争论。例如，从长远来看，专注于显示最受欢迎内容（而不是随机内容）的社交媒体新闻提要是否有助于或损害人们信仰的准确性？在该项目的第一部分，研究人员将开发一个理论模型，描述人们通过发布、分享和重新分享“信号”（例如，新闻故事）关于世界的状态。该模型将关注当人们的行为驱动的信息扩散过程也影响他们的学习时，人们的信念和行为如何共同进化。这项研究将产生关于平台的信号采样算法如何影响社交学习的准确性和人们信念的一致程度的结果。该项目的第二部分将在实验室实验中测试这些预测。研究人员将进行社交学习游戏，让受试者看到前辈的信号，并选择认可这些信号的一个子集。在选择向未来用户显示哪些过去的信号时，不同的处理将改变算法对先前用户认可的权重。该项目的第三部分将处理复杂扩散模型中的“临界点”。这一部分的研究将确定个人行为的简单条件，以确定是否存在一个“临界点”，即扩散从（a）到达人口的一小部分到（B）到达人口的一大部分的不连续切换。该奖项反映了NSF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。

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.