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Robust inattentive discrete choice.

Lars Peter Hansen1,2, Jianjun Miao3,4,5, Hao Xing6

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Summary
This summary is machine-generated.

This study introduces a robust decision-making model for data-rich environments, accounting for ambiguity aversion in attention allocation. It offers a more cautious approach than traditional rational inattention models.

Keywords:
information acquisitionrational inattentionrobustness

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Area of Science:

  • Decision Theory
  • Behavioral Economics
  • Information Economics

Background:

  • Rational inattention models assume full commitment to subjective priors in data-rich environments.
  • Decision-makers face costs when processing information, necessitating selective attention.
  • Optimal decision-making involves scrutinizing salient information.

Purpose of the Study:

  • To develop a robustly optimal solution for the inattention problem by incorporating ambiguity aversion.
  • To relax the assumption of full commitment to a subjective prior distribution.
  • To provide a more realistic model for decision-making under uncertainty.

Main Methods:

  • Utilized a simplified setup with discrete choices.
  • Employed Shannon's mutual information to quantify attention costs.
  • Imposed relative entropy to measure prior distribution divergence.
  • Developed necessary and sufficient conditions and numerical methods for the robust solution.

Main Results:

  • The robust decision-maker slants priors towards cautious or pessimistic directions.
  • Attention allocation is optimized under ambiguity aversion.
  • The model demonstrates robustness to prior misspecification.

Conclusions:

  • The proposed model offers a robust alternative to traditional rational inattention.
  • Ambiguity aversion leads to different prior slanting and attention allocation strategies.
  • This framework enhances understanding of decision-making under uncertainty and information costs.