Bayesian Models of Social Behavior Using Online Resources

这是一个“启动奖”，用于继续在CI TraCS博士后奖学金下开始的研究。人们一生中的大部分时间都花在社交或与其他人互动上。在一个典型的日子里，人们可能会与他们的同事合作一个项目，与他们的队友打垒球，并与他们的家人交谈。社会互动是我们大多数活动固有的一部分，因此，理解社会互动是理解人类行为的基本部分。随着人们在网上花费的时间迅速增加，曾经仅限于面对面会议、信件和电话的社交互动，越来越多地通过使用电子邮件、Facebook和在线聊天等在线资源进行。努力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，社会互动是极其复杂的，所以在任何情况下分析和建模都不是一件容易的事。幸运的是，贝叶斯概率方法为数据提供了丰富、灵活、生成的模型，可以用来模拟复杂、高度结构化的社会互动。一般来说，贝叶斯方法为不确定世界的推理提供了一个原则性框架。贝叶斯潜变量模型使我们能够推断或发现潜在的相当复杂的、未被观察到的结构，这些结构隐藏在我们所观察到的事物之下。本研究开发了一些方法，这些方法发现了对在线发生的复杂社会互动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会互动，并探索了社会影响。这项工作有潜力改善科学（例如，通过改善远程合作）、商业（例如，通过确定企业应该向谁通报其产品）和整个社会（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).