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The value of conceptual knowledge

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  • Benjamin Davies
  • Anirudh Sankar

Abstract

We study the instrumental value of conceptual knowledge when making statistical decisions. Such knowledge tells agents how unknown, payoff-relevant states relate. It is distinct from the statistical knowledge gained from observing signals of those states. We formalize this distinction in a tractable framework used by economists and statisticians. Conceptual knowledge is valuable because it empowers agents to design more informative signals. It is more valuable when states are more "reducible": when they can be explained with fewer common concepts. Its value is non-monotone in the number of signals and vanishes when agents have infinitely many signals. Agents who know more concepts can attain the same payoffs with fewer signals. This is especially true when states are highly reducible.

Suggested Citation

  • Benjamin Davies & Anirudh Sankar, 2025. "The value of conceptual knowledge," Papers 2509.09170, arXiv.org, revised Feb 2026.
  • Handle: RePEc:arx:papers:2509.09170
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    References listed on IDEAS

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    1. Drew Fudenberg & Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2022. "Measuring the Completeness of Economic Models," Journal of Political Economy, University of Chicago Press, vol. 130(4), pages 956-990.
    2. Joshua Schwartzstein, 2014. "Selective Attention And Learning," Journal of the European Economic Association, European Economic Association, vol. 12(6), pages 1423-1452, December.
    3. Ignacio Esponda & Demian Pouzo, 2016. "Berk–Nash Equilibrium: A Framework for Modeling Agents With Misspecified Models," Econometrica, Econometric Society, vol. 84, pages 1093-1130, May.
    4. Joshua S. Gans, 2025. "A Quest for AI Knowledge," NBER Working Papers 33566, National Bureau of Economic Research, Inc.
    5. Benjamin Davies, 2024. "Learning about a changing state," Papers 2401.03607, arXiv.org, revised Jan 2026.
    6. Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2022. "Dynamically Aggregating Diverse Information," Econometrica, Econometric Society, vol. 90(1), pages 47-80, January.
    7. Sendhil Mullainathan & Ziad Obermeyer, 2022. "Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care [“The Determinants of Productivity in Medical Testing: Intensity and Allocation of Care,”]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(2), pages 679-727.
    8. Joshua Schwartzstein & Adi Sunderam, 2021. "Using Models to Persuade," American Economic Review, American Economic Association, vol. 111(1), pages 276-323, January.
    9. Drew Fudenberg & Annie Liang, 2019. "Predicting and Understanding Initial Play," American Economic Review, American Economic Association, vol. 109(12), pages 4112-4141, December.
    10. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    11. Isaiah Andrews & Drew Fudenberg & Lihua Lei & Annie Liang & Chaofeng Wu, 2022. "The Transfer Performance of Economic Models," Papers 2202.04796, arXiv.org, revised Mar 2025.
    12. Christian Gollier & Harris Schlesinger, 1996. "Arrow's theorem on the optimality of deductibles: A stochastic dominance approach (*)," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 7(2), pages 359-363.
    13. George J. Mailath & Larry Samuelson, 2020. "Learning under Diverse World Views: Model-Based Inference," American Economic Review, American Economic Association, vol. 110(5), pages 1464-1501, May.
    14. Teppo Felin & Matthias Holweg, 2024. "Theory Is All You Need: AI, Human Cognition, and Causal Reasoning," Strategy Science, INFORMS, vol. 9(4), pages 346-371, December.
    15. Jeff Dominitz & Charles F. Manski, 2017. "More Data or Better Data? A Statistical Decision Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(4), pages 1583-1605.
    16. Lars Peter Hansen & Thomas J Sargent, 2014. "Robust Control and Model Uncertainty," World Scientific Book Chapters, in: UNCERTAINTY WITHIN ECONOMIC MODELS, chapter 5, pages 145-154, World Scientific Publishing Co. Pte. Ltd..
    17. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
    18. Arjada Bardhi, 2024. "Attributes: Selective Learning and Influence," Econometrica, Econometric Society, vol. 92(2), pages 311-353, March.
    19. Mark Whitmeyer, 2022. "Making Information More Valuable," Papers 2210.04418, arXiv.org, revised Jun 2024.
    20. Kfir Eliaz & Simone Galperti & Ran Spiegler, 2025. "False Narratives and Political Mobilization," Journal of the European Economic Association, European Economic Association, vol. 23(3), pages 983-1027.
    21. Steven Callander, 2011. "Searching and Learning by Trial and Error," American Economic Review, American Economic Association, vol. 101(6), pages 2277-2308, October.
    22. Pirmin Fessler & Maximilian Kasy, 2019. "How to Use Economic Theory to Improve Estimators: Shrinking Toward Theoretical Restrictions," The Review of Economics and Statistics, MIT Press, vol. 101(4), pages 681-698, October.
    23. Peysakhovich, Alexander & Naecker, Jeffrey, 2017. "Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity," Journal of Economic Behavior & Organization, Elsevier, vol. 133(C), pages 373-384.
    24. Peter Klibanoff & Massimo Marinacci & Sujoy Mukerji, 2005. "A Smooth Model of Decision Making under Ambiguity," Econometrica, Econometric Society, vol. 73(6), pages 1849-1892, November.
    25. Rothschild, Michael & Stiglitz, Joseph E., 1970. "Increasing risk: I. A definition," Journal of Economic Theory, Elsevier, vol. 2(3), pages 225-243, September.
    26. Christoph Carnehl & Johannes Schneider, 2025. "A Quest for Knowledge," Econometrica, Econometric Society, vol. 93(2), pages 623-659, March.
    27. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
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