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

网络理论提供了新的概念和分析工具，帮助人们理解行为者如何利用特权获得他人的专业知识来做出合理的决策。 但迄今为止，它一直侧重于组成社会社区的社会联系。 通过识别社区通过网络模式的注意力，而不是通过模式的直接社会网络联系，这个项目将识别认知社区，从而重新定位网络分析，从传统的社会关系的重点。 为此，将构建一个数据集，该数据集涵盖了1983- 2011年期间在美国主要证券交易所上市的16，000多家公司的19，000多名分析师。初始假设将被检验，即相对封闭的认知社区的成员如何解决复杂但局部的问题，跨越结构空洞的行动者如何解决不太复杂但更遥远的问题，以及占据重叠内聚社区的结构折叠的行动者如何解决最复杂的遥远问题。 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为某些行为者提供优势。 由于分析师的报告是市场参与者交易决策的关键因素，因此这种分析将提供新的视角来了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。

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.