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，帮助我们理解行为者如何利用特权获取他人的专业知识来做出合理的决策。但迄今为止，它主要关注构成社会社区的社会关系。通过通过关注网络模式来识别社区，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关注社会关系的方向重新定位。为此，将构建一个数据集，该数据集涵盖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19,000多名分析师的股票市场覆盖范围。初步的假设将检验相对封闭的认知社区的成员如何解决复杂但局部的问题，跨越结构洞的参与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的结构褶皱的参与者如何解决最复杂的远程问题。这项工作将阐明市场上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某些行为者带来优势。由于分析师的报告是市场参与者交易决策的关键因素，这种分析将提供新的视角来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。

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.