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

信息获取是嵌入在社会环境中的。这扭曲了--或者至少改变了--当个人不确定真相并与他人交流时所面临的激励。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，它正式地研究分散的和自私的代理人何时以及如何收集信息。社会学习文学的一个潜在但尚未实现的目标是，能够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领域越来越多的文献深感关切的是，目前，激励措施与寻求真相/讲真话并不一致，而且讨论正变得越来越两极分化。这导致了一种尖锐的公开言论，充斥着相互矛盾的信息和理论，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，并使用这些技术，增加了对社会如何学习的基本理解。在给定参数的情况下，社会学习系统本身可以被视为一个计算过程。这个项目考虑了这类问题中涉及计算复杂性和算法设计的两个有趣的角度：1)代理最好地响应或确定不同系统的属性所需的计算复杂性；2)将社会学习视为一个复杂系统，其中社会相互作用的模型、输入信号和自我调节/进化性质可以被视为约束，并且设计参数可以被优化以鼓励社会学习走向真理发现。这项工作包括分析具有相关一阶特征的模型，以了解在连续的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，该项目还包括根据平台设计选择优化某些参数的算法和洞察力设计，以在这些环境中促进快速和强大的社交学习。一个关键特征是扩充了社会学习文献，明确考虑了代理人的社会嵌入，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。

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.