III-CXT: Collaborative Research: Computational Methods for Understanding Social Interactions in Animal Populations

III-CXT：合作研究：理解动物群体社会互动的计算方法

• 批准号: 0705822
• 负责人: Tanya Berger-Wolf
• 金额: --
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705822&HistoricalAwards=false
• 关键词: III; CXT; Collaborative; Research; Computational

The goal of the proposed research is to create analytical and computational tools that explicitly address the time and order of social interactions between individuals. The proposed approach combines ideas from social network analysis, Internet computing, distributed computing, and machine learning to solve problems in population biology. The diverse computational tasks of this project include design of algorithmic techniques to identify social entities such as a communities, leaders, and followers, and to use these structures to predict social response patterns to danger or disturbances. Nowhere is the impact of social structure likely to be greater than when species come in contact with predators. Thus, the accuracy and predictive power of the proposed computational tools will be tested by characterizing the social structure of horses and zebras (equids) both before and after human- or predator-induced perturbations to the social network. The proposed interdisciplinary research will have broader impacts on a wide range of research communities. New methods for analysis of social interactions in animal populations will be useful for behavioral biologists in such diverse fields as behavioral ecology, animal husbandry, conservation biology, and disease ecology. The machine learning algorithms that will be develop are relevant to many studies in which researchers need to classify temporal interaction data. The proposed network methods have broader relevance to human societies: disease transmission, dissemination of ideas, and social response to crises are all dynamic processes occurring via social networks. Further, through teaching and participation in outreach, students and school teachers will gain access to opportunities for hands-on, interdisciplinary experiences in a new area of computational biology. The research and software resulting from the proposed project will be disseminated both in computational and biological communities and enhanced by cross-disciplinary training activities and will serve to train a new generation of interdisciplinary scientists.

拟议研究的目标是创建分析和计算工具，明确解决个人之间的社会互动的时间和顺序。所提出的方法结合了社会网络分析，互联网计算，分布式计算和机器学习的思想，以解决人口生物学问题。该项目的各种计算任务包括设计算法技术来识别社区，领导者和追随者等社会实体，并使用这些结构来预测对危险或干扰的社会反应模式。当物种与捕食者接触时，社会结构的影响可能比任何地方都大。因此，所提出的计算工具的准确性和预测能力将通过表征人类或捕食者引起的社会网络扰动之前和之后的马和斑马（Equids）的社会结构来测试。拟议的跨学科研究将对广泛的研究界产生更广泛的影响。分析动物群体中社会相互作用的新方法将对行为生态学、畜牧业、保护生物学和疾病生态学等不同领域的行为生物学家有用。将要开发的机器学习算法与研究人员需要对时间交互数据进行分类的许多研究相关。所提出的网络方法对人类社会具有更广泛的相关性：疾病传播、思想传播和社会对危机的反应都是通过社交网络发生的动态过程。此外，通过教学和参与外展，学生和学校教师将获得机会，在计算生物学的一个新领域的动手，跨学科的经验。拟议项目产生的研究成果和软件将在计算界和生物界传播，并通过跨学科培训活动得到加强，将有助于培训新一代跨学科科学家。