Optimal Decision Making with Rational Inattention Using Noisy Data

Yuan Zhao, A. Abdi, M. Dean, A. Abdi
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Abstract

Rational inattention of decision makers to costly data and information and resources affects their optimal decision making strategies. The theory of rational inattention has found applications in several areas such as economics, finance and psychology. In this paper, we study scenarios where the available data is noisy. The noise may have been generated because of inaccuracies or errors in data collection methods, or the data may have been intentionally distorted to protect private or secret information. Here we introduce a formulation for rationally inattentive decision making when the data is noisy, and derive its optimal decision making strategy. Using a stock trading problem as an example, we demonstrate that as the noise level in the data increases, probability of correct decision decreases. This results in less payoff for the decision maker, when using noisy data. We also show how the noise level and information cost parameters can be estimated using the developed formulation. The results are useful for developing decision making strategies, when using noisy data.
基于噪声数据的理性不注意最优决策
决策者对昂贵的数据、信息和资源的理性忽视影响其最优决策策略。理性注意力不集中理论在经济学、金融学和心理学等多个领域都有应用。在本文中,我们研究了可用数据有噪声的场景。噪音可能是由于数据收集方法的不准确或错误而产生的,也可能是为了保护私人或秘密信息而故意扭曲数据。本文引入了数据有噪声时的理性不注意决策公式,并推导了其最优决策策略。以一个股票交易问题为例,我们证明了随着数据中噪声水平的增加,正确决策的概率降低。当使用噪声数据时,这会导致决策者的收益减少。我们还展示了如何使用所开发的公式来估计噪声水平和信息成本参数。当使用有噪声的数据时,结果对制定决策策略很有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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