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.

网络理论提供了新颖的概念和分析工具，用于理解参与者如何利用对他人专业知识的特权访问来做出明智的决策。 但迄今为止，它的重点是构成社会社区的社会联系。 通过通过网络注意力模式而不是通过直接的社会网络联系模式来识别社区，该项目将识别认知社区，从而将网络分析从传统的社会联系重点重新定位。 为此，将根据 19,000 多名分析师对 1983 年至 2011 年期间在美国主要证券交易所上市的 16,000 多家公司的股票市场覆盖情况构建一个数据集。相对封闭的认知社区在解决复杂但局部的问题时表现如何，跨越结构洞的行动者如何解决不太复杂但更遥远的问题，以及占据重叠凝聚力社区结构折叠的行动者如何表现解决最复杂的远程问题。 这项工作将揭示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为某些参与者带来优势。 由于分析师的报告是市场参与者交易决策的关键因素，因此这种分析将为理解市场价格异常波动如何产生并最终导致金融泡沫提供新的视角。