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The Cost of Optimally Acquired Information

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  • Alexander W. Bloedel
  • Weijie Zhong

Abstract

This paper introduces a framework for modeling the cost of information acquisition based on the principle of cost-minimization. We study the reduced-form \emph{indirect cost} of information generated by the sequential minimization of a primitive \emph{direct cost} function. Indirect cost functions: (i) are characterized by a novel recursive property, \emph{sequential learning-proofness}; (ii) provide an optimization foundation for the popular class of ``uniformly posterior separable'' costs; and (iii) can often be tractably calculated from their underlying direct costs. We apply the framework by identifying fundamental modeling tradeoffs in the rational inattention literature and two new indirect cost functions that balance these tradeoffs.

Suggested Citation

  • Alexander W. Bloedel & Weijie Zhong, 2025. "The Cost of Optimally Acquired Information," Papers 2511.05466, arXiv.org.
    • Handle: RePEc:arx:papers:2511.05466
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    References listed on IDEAS

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    1. Xiaosheng Mu & Luciano Pomatto & Philipp Strack & Omer Tamuz, 2021. "From Blackwell Dominance in Large Samples to Rényi Divergences and Back Again," Econometrica, Econometric Society, vol. 89(1), pages 475-506, January.
    2. Ramesh Johari & Pete Koomen & Leonid Pekelis & David Walsh, 2022. "Always Valid Inference: Continuous Monitoring of A/B Tests," Operations Research, INFORMS, vol. 70(3), pages 1806-1821, May.
    3. Alex Bloedel & Tommaso Denti & Luciano Pomatto, 2025. "Modeling information acquisition via f-divergence and duality," Papers 2510.03482, arXiv.org.
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