Big Data, Scarce Attention and Decision-Making Quality

Abstract

Big data technology enables us to access tremendous amounts of information; however, individuals cannot process all available information due to the bounded attention. The impact of this tension upon information seeking and processing behaviors and the resultant decision-making quality is still unclear. By agent-based simulation, we explicitly model the endogenous information choice in a sequential decision-making process, where individuals choose independently how much information and what type of information (shallow information such as the popularity a product, or deep information such as the expected utility of a product) is to be used. It is found that when the information is costly, only a small part of the individuals use deep information and only limited pieces of it, and other individuals simply follow the majority choice. The decrease in the cost of of information cost due to big data can encourage individuals to make use of more information, resulting in a better overall decision quality. However, if the big data only reduces the cost of shallow information but not that of the deep information, the decision quality is diminished because more individuals are induced to adopt the herding strategy.