Bayesian Models of Social Behavior Using Online Resources

这是对在CI TraCS博士后奖学金下开始的继续研究的“启动者奖”。这段友谊的摘要转载在这里：人们一生中花了大量的时间进行社交，或与其他人互动。在一个典型的日子里，一个人可能会与他们的同事在一个项目上合作，与他们的队友打垒球，并与他们的家人交谈。社会互动本质上是我们大多数活动的一部分，因此，理解社会互动是理解人类行为的基本部分。随着人们在线时间的快速增长，曾经局限于面对面会议、信件和电话的社交互动正越来越多地通过使用电子邮件、Facebook和在线聊天等在线资源来进行。致力于理解人类行为的社会和认知科学家可以分析在线互动，以阐明这种新环境下的社会行为，并从它提供的丰富数据中受益。然而，社交互动是极其复杂的，因此在任何环境下都不容易对其进行分析和建模。幸运的是，贝叶斯概率方法为数据提供了丰富、灵活、可生成的模型，可用于对复杂的、高度结构化的社会交互进行建模。一般而言，贝叶斯方法为推理不确定的世界提供了一个原则性的框架。贝叶斯潜变量模型允许我们推理或发现潜在的相当复杂的、未被观察到的结构，这些结构是我们所观察到的东西的基础。这项研究开发的方法发现了对在线发生的复杂社交互动进行建模所必需的未观察到的结构，探索了群体互动，评估了环境如何影响社交互动，并探索了社交影响。这项工作有可能改善科学(例如，通过改善远程合作)、商业(例如，通过确定哪些企业应该向谁通报其产品)，以及整个社会(例如，通过改善社交网络)。

This is a "Starter Award" for continuation of research begun under a CI TraCS Postdoctoral Fellowship. The Abstract from that fellowship is reproduced here:People spend a great deal of their lives socializing, or interacting with other people. On a typical day a person might collaborate on a project with their work colleagues, play softball with their teammates, and converse with their family. Social interactions are inherently part of most of our activities, therefore, understanding social interactions is a fundamental part of understanding human behavior. As the amount of time people spend online rapidly grows, social interactions which were once limited to in-person meetings, letters, and telephone calls, are increasingly occurring through the use of online resources such as email, Facebook, and online chats. Social and cognitive scientists who strive to understand human behavior can analyze online interactions to illuminate social behavior in this new setting, and benefit from the wealth of data that it provides. However, social interactions are extremely complex, so analyzing and modeling them is not easy in any setting. Fortunately Bayesian probabilistic methods offer rich, flexible, generative models for data, which can be used to model complex, highly structured, social interactions. In general, Bayesian methods provide a principled framework for reasoning about an uncertain world. Bayesian latent variable models allow us to reason about, or discover, the potentially quite complex, unobserved structure that underlies what we do observe. This research develops methods which discover the unobserved structure necessary to model complex social interactions which occur online, explore group interactions, evaluate how context effects social interactions, and explore social influence. This work has the potential to improve science (e.g. by improving long-distance collaborations), commerce (e.g. by identifying whom businesses should inform about their products), and society at large (e.g. by improving social networking).