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

合作研究：扩散过程中的学习、行为和设计

• 批准号: 2215256
• 负责人: Krishna Dasaratha
• 金额: $ 15.94万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215256&HistoricalAwards=false
• 关键词: Collaborative; Research; Learning; Behavior; Design; Collaborative; Research; Learning; Behavior; Design

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.

该奖项为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资助。对于许多人来说，社交媒体是越来越重要的新闻来源。用户在此类平台上看到的内容取决于其他用户选择发布和共享的内容。这也取决于平台开发人员用来生成新闻提要的算法。研究人员将研究社交媒体平台的用户和开发人员的决策如何影响人们所学的知识。用户可能会看到哪些类型的内容？人们如何处理此内容以形成对相关问题的信念？这些信念何时可能准确？该研究与最近有关某些用户和开发人员选择是否有助于社交媒体上误导或错误信息的传播有关的辩论涉及。例如，从长远来看，社交媒体新闻提要是否侧重于显示最受欢迎的内容（而不是随机内容）有助于或损害人们信仰的准确性？在项目的第一部分中，研究人员将开发一个理论模型，描述在环境中人们通过发布，共享和重新共享“信号”（例如新闻报道）关于世界状况的社会学习。当人们的行为驱动的信息差异过程也会影响他们的学习时，该模型将重点关注人们的信念和行为如何共同发展。这项研究将产生有关该平台信号采样算法的结果，该算法如何确定向用户显示哪些信号，会影响社会学习的准确性以及人们在人们信念中的一致性程度。该项目的第二部分将在实验室实验中测试这些预测。调查人员将进行社交学习游戏，主题可以看到前任信号，并选择认可其中的一部分信号。在选择过去的信号向未来用户展示时，不同的处理将改变算法对以前用户认可的重量。该项目的第三部分将在复杂的差异模型中处理“临界点”。研究的这一部分将确定有关个体行为的简单条件，这些条件确定是否存在“临界点”，其中不连续的扩散从（a）达到很小的人群到（b）达到了很大一部分人群的情况。作者将调查此类过渡是否总是顺利进行。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响标准来评估诚实地支持了支持。