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

信息获取植根于社会环境中。这扭曲了——或者至少改变了——个人在不确定真相并与他人交流时所面临的动机。社会学习是计算机科学/经济学文献中一个越来越有影响力的话题，它正式研究分散和自利的主体何时以及如何聚集信息。社会学习文献的一个潜在但尚未实现的目标是建立促进社会学习的社会计算系统。越来越多的社交媒体和计算社会科学方面的文献深感担忧，目前，激励机制与寻求真相/讲述真相不一致，讨论正变得越来越两极化。这导致了激烈的公共讨论充斥着相互矛盾的信息和理论，而真相很难找到。这个项目建立在理论计算机科学技术的基础上，增加了对社会如何学习的基本理解。具有给定参数的社会学习系统本身可以被视为一个计算过程。该项目考虑了涉及计算复杂性和算法设计的这类问题的两个有趣的观点：1)代理最佳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，其中社会互动模型、输入信号和自我调节/进化性质可以被视为约束，并且可以优化设计参数以鼓励社会学习走向真理发现。这项工作包括对具有相关一阶特征的模型进行分析，以了解在顺序社会学习和具有重复更新设置的社会学习中，群体快速可靠地收敛于真理的充分和必要条件。此外，该项目还包括设计算法和见解，以优化与平台设计选择相对应的某些参数，从而在每种设置中促进快速和强大的社会学习。一个关键特征是增加社会学习文献，明确考虑代理人的社会嵌入性，包括他们的混合激励和两极分化环境的现实。此外，通过精心设计的实证研究，该项目开发了在社会压力下学习更复杂真相的模型。该奖项反映了美国国家科学基金会的法定使命，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。

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.