Attention Networks and Cognitive Challenges: Positional Advantages in Complex and Distant Search

注意网络和认知挑战：复杂和远程搜索中的位置优势

• 批准号: 1236931
• 负责人: David Stark
• 金额: $ 42.04万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1236931&HistoricalAwards=false
• 关键词: Attention; Networks; Cognitive; Challenges; Positional

Network theory has provided novel concepts and analytical tools for understanding how actors can leverage privileged access to others' expertise to make sound decisions. But to date it has focused on the social ties that comprise social communities. By identifying communities through network patterns of attention, instead of through patterns of direct soical network linkages, this project will identify cognitive communities, thereby reorienting network analysis from its traditional focus on social ties. To do so, a dataset will be constructed on the stock market coverage of more than 19,000 analysts for more than 16,000 companies listed on the major US stock exchange markets, during the period 1983-2011.Intial hypotheses will be examined on how members of a relatively closed cognitive community performs in solving complex but local problems, how actors spanning structural holes perform solving less complex but more distant problems, and how actors occupying a structural fold of overlapping cohesive communities peform in solveing the most complex distant problems. This work will shed light on how attention allocation in markets affects cognition and how the social distribution of attention gives an edge to some actors. Because the reports of analysts are a key ingredient in the trading decisions of market actors, this analysis will provide new lenses to understand how abnormal fluctuations in market prices originate, and ultimately lead to financial bubbles.

网络理论为理解行为者如何利用获得他人专业知识的特权来做出明智的决策提供了新的概念和分析工具。但到目前为止，它一直专注于组成社会社区的社会联系。通过通过关注的网络模式来确定社区，而不是通过直接的社会网络联系的模式，该项目将确定认知社区，从而使网络分析从其传统的对社会关系的关注转向。为此，我们将构建一个数据集，其中包括1983-2011年期间在美国主要证券交易所上市的1.6万多家公司的1.9万多名分析师的股市覆盖率。初步假设将检验以下方面：相对封闭的认知社区的成员在解决复杂但局部问题方面的表现；跨越结构洞的参与者如何解决不那么复杂但距离更远的问题；以及占据重叠的有凝聚力的社区的结构折叠的参与者如何在解决最复杂的遥远问题方面表现。这项工作将阐明市场中的注意力分配如何影响认知，以及注意力的社会分配如何给一些参与者带来优势。由于分析师的报告是市场参与者交易决策的关键因素，这一分析将提供新的视角，以理解市场价格的异常波动是如何产生的，并最终导致金融泡沫。