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Big Data, Scarce Attention and Decision-Making Quality

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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.

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Notes

  1. While, in principle, agents can observe the choice and the realized utilities of all preceding consumers, their attention scarcity may not allow them to do so exhaustively. See Sect. 2.2 for the details.

  2. The reference point is determined as the expected value of the items so that the probability of the idle item being larger than the active item is less than the probability of the idle item being smaller than the active item. In this paper, the specific reference point which we employ is the average of \(EV_i\), i.e., 0.5.

  3. It is possible that the constant progress in the information and communication technology may narrow the gap, and in the limit \(c_U = c_H\), and even \(c_U = c_H=0\). Our simulation, to be detailed in Sect. 2.4, will explore this rich combination.

  4. The cost function can easily be extended to a nonlinear form which may depict the cost more accurately. However, in our paper, the purpose is to gain insight into how big data impacts the decision through reducing the attention cost but not the exact form of attention cost function. Hence, a simple linear function can serve better here.

  5. For a review of reinforcement learning and its use from psychology to economics, the interested reader is referred to Chen (2016).

  6. We cannot and do not attempt to identify the border between layers. However, some white blanks are faintly visible between them. A simple one-dimensional k-mean clustering algorithm (Hartigan and Wong 1979) provides the same partition.

  7. It may seem that the attention distribution for the herd-based strategy follows an exponential distribution, but we tried both the gamma and exponential distributions and found that the former fits better.

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Acknowledgements

The authors are grateful for the research support in the form of Ministry of Science and Technology (MOST) Grants, MOST 106-2410-H-004-006-MY2 and MOST 105-2811-H-004-034, respectively.

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Correspondence to Shu-Heng Chen.

Appendix: Pseudo-Code of the Simulation

Appendix: Pseudo-Code of the Simulation

See Algorithm 1.

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Yu, T., Chen, SH. Big Data, Scarce Attention and Decision-Making Quality. Comput Econ 57, 827–856 (2021). https://doi.org/10.1007/s10614-018-9798-5

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