Decision-Making in a Complex Social Environment: Developing Methodological Approaches Using Innovative Technologies

在这个项目中，我将使用自动跟踪技术和机器学习来测试个人投资和决策如何受到社会环境的影响。为了实现这一目标，我将开发一种方法，用于自动检测野生松鼠种群的决策，并将其与允许我构建社交网络（量化社会关系）和空间网络（记录家庭范围）的技术方法相结合。学习系统我将在埃克塞特大学校园的东部灰松鼠的长期实地考察中进行我的项目（由我提议的主要主管丽莎·利弗博士于2005年建立）。东方灰松鼠是散藏者，一年中相当大的一部分时间依赖于储藏的食物[2]。因此，缓存决策对个体生存具有重要意义。松鼠通常被描述为不合群的，但两性都是共同筑巢的，雌性表现出纳塔尔时的爱，并且有重叠的家域[12]。校园人群中的个体习惯于人类，并经常被捕获并标记PIT标签，以便多年来对个体进行监控。这个陷阱协议将允许我安装GPS项圈和加速度计相对容易。

In this project, I will use automated tracking technology and machine learning to test how individual investments and decision-making are influenced by the social environment. To achieve this, I will develop a method for automated detection of decision-making in a wild population of squirrels, and combine this with technological methods that allow me to construct social networks (quantifying social relationships) and spatial networks (documenting home ranges). Study system I will conduct my project on the long-term field site of the eastern grey squirrels at the University of Exeter campus (established in 2005 by my proposed primary supervisor, Dr Lisa Leaver). Eastern grey squirrels are scatter-cachers relying on cached food for considerable portions of the year[2]. Thus, caching decisions are of significant importance for individual survival. Squirrels are often described as asocial but both sexes nest communally and females show natal philopatry and have overlapping home ranges[12]. Individuals in the campus population are habituated to humans and routinely trapped and marked with PIT tags, such that individuals are monitored across multiple years. This trapping protocol will allow me to fit GPS collars and accelerometers with relative ease.