Collaborative Research: AF: Small: Promoting Social Learning Amid Interference in the Age of Social Media

合作研究：AF：小：在社交媒体时代的干扰下促进社交学习

• 批准号: 2208663
• 负责人: Jie Gao
• 金额: $ 27万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208663&HistoricalAwards=false
• 关键词: Collaborative; Research; AF; Small; Promoting

Information acquisition is embedded in a social setting. This distorts - or at least changes - the incentives individuals face when they are uncertain about the truth and communicate with others. Social learning, an increasingly impactful topic in the computer science/economics literature, formally studies when and how dispersed and self-interested agents aggregate information. A potential, but unrealized, goal of the social-learning literature is to enable the building of socio-computational systems that promote social learning. A growing volume of literature in social media and computational social science is deeply concerned that, at present, incentives are not aligned with truth-seeking/truth-telling and that discussion is becoming increasingly polarized. This leads to an acrimonious public discourse rife with conflicting information and theories, where the truth is hard to locate. Building on and using theoretical computer science techniques, this project adds to the fundamental understanding of how societies learn. The social learning system itself, with given parameters, can be seen as a computational process. This project considers two interesting perspectives in this family of problems that involve computational complexity and algorithm design: 1) the computational complexity required for agents to best respond or to determine the properties of different systems; 2) considering social learning as a complex system where the models of social interactions, input signals, and self-regulating/evolving nature can be viewed as constraints, and the design parameters can be optimized to encourage social learning towards truth discovery. This work includes the analysis of models with relevant first-order features to learn which conditions are sufficient and necessary for crowds to quickly and reliably converge on the truth in both the sequential social learning and social learning with repeated updating settings. In addition, the project includes design of algorithms and insights to optimize certain parameters, corresponding to platform design choices, to promote fast and robust social learning in each of these settings. A key feature is augmenting the social-learning literature to explicitly consider agents' social embeddedness including their mixed incentives and the reality of polarized environments. Additionally, with carefully crafted empirical research, the project develops models for learning more complex truths amid social pressure.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.

信息获取嵌入了社会环境中。 当个人不确定真理并与他人交流时，这会扭曲或至少改变 - 他们面临的激励措施。社会学习是计算机科学/经济学文献中越来越有影响力的话题，正式研究何时以及如何分散和自我利益的代理汇总信息。社会学习文献的潜力但未实现的目标是使建立促进社会学习的社会计算系统。社交媒体和计算社会科学中越来越多的文献深切关注，目前，激励措施与寻求真理/真相的销售不符，并且讨论变得越来越两极化。 这导致了一场激烈的公开话语，充满了矛盾的信息和理论，在这些信息和理论中很难找到。在理论计算机科学技术的基础上，该项目增加了对社会学习方式的基本理解。具有给定参数的社会学习系统本身可以看作是一个计算过程。该项目考虑了涉及计算复杂性和算法设计的这个问题家族中的两个有趣的观点：1）代理最能做出响应或确定不同系统属性所需的计算复杂性； 2）将社会学习视为一个复杂的系统，可以将社会互动，输入信号和自我调节/不断发展的性质的模型视为约束，并且可以优化设计参数以鼓励社会学习对真理发现。 这项工作包括对具有相关一阶功能的模型的分析，以了解哪些条件是足够和必要的，使人群可以通过重复更新设置来快速，可靠地融合真相。此外，该项目还包括算法和见解的设计，以优化与平台设计选择相对应的某些参数，以在每种环境中促进快速而强大的社交学习。 一个关键特征是增强社会学习文献，以明确考虑代理商的社会嵌入，包括它们的混合激励措施和两极分化环境的现实。 此外，通过精心制作的实证研究，该项目在社会压力下开发了学习更复杂的真理的模型。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来进行评估来支持的。