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Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification


  • Kunal Pattanayak
  • Vikram Krishnamurthy


Are deep convolutional neural networks (CNNs) for image classification explainable by utility maximization with information acquisition costs? We demonstrate that deep CNNs behave equivalently (in terms of necessary and sufficient conditions) to rationally inattentive utility maximizers, a generative model used extensively in economics for human decision making. Our claim is based by extensive experiments on 200 deep CNNs from 5 popular architectures. The parameters of our interpretable model are computed efficiently via convex feasibility algorithms. As an application, we show that our economics-based interpretable model can predict the classification performance of deep CNNs trained with arbitrary parameters with accuracy exceeding 94% . This eliminates the need to re-train the deep CNNs for image classification. The theoretical foundation of our approach lies in Bayesian revealed preference studied in micro-economics. All our results are on GitHub and completely reproducible.

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  • Kunal Pattanayak & Vikram Krishnamurthy, 2021. "Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification," Papers 2102.04594,, revised Jul 2021.
  • Handle: RePEc:arx:papers:2102.04594

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    References listed on IDEAS

    1. Andrew Caplin & Daniel Martin, 2015. "A Testable Theory of Imperfect Perception," Economic Journal, Royal Economic Society, vol. 125(582), pages 184-202, February.
    2. Andrew Caplin & Mark Dean, 2015. "Revealed Preference, Rational Inattention, and Costly Information Acquisition," American Economic Review, American Economic Association, vol. 105(7), pages 2183-2203, July.
    3. Varian, H.R., 1991. "Goodness of Fit for Revealed Preference Tests," Papers 13, Michigan - Center for Research on Economic & Social Theory.
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    5. Sebastian Bach & Alexander Binder & Grégoire Montavon & Frederick Klauschen & Klaus-Robert Müller & Wojciech Samek, 2015. "On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-46, July.
    6. Paul R. Milgrom, 1981. "Good News and Bad News: Representation Theorems and Applications," Bell Journal of Economics, The RAND Corporation, vol. 12(2), pages 380-391, Autumn.
    7. W. E. Diewert, 1973. "Afriat and Revealed Preference Theory," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 40(3), pages 419-425.
    8. repec:hal:pseose:halshs-01155313 is not listed on IDEAS
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    Cited by:

    1. Kunal Pattanayak & Vikram Krishnamurthy, 2021. "Unifying Revealed Preference and Revealed Rational Inattention," Papers 2106.14486,, revised Jun 2023.
    2. Naudé, Wim, 2023. "Artificial Intelligence and the Economics of Decision-Making," IZA Discussion Papers 16000, Institute of Labor Economics (IZA).

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