IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v204y2021ics0165176521001713.html
   My bibliography  Save this article

High dimensional decision making, upper and lower bounds

Author

Listed:
  • Pourbabaee, Farzad

Abstract

A decision maker’s utility depends on her action a∈A⊂Rd and the payoff relevant state of the world θ∈Θ. One can define the value of acquiring new information as the difference between the maximum expected utility pre- and post information acquisition. In this paper, I find asymptotic results on the expected value of information as d→∞, by using tools from the theory of (sub)-Gaussian processes and generic chaining.

Suggested Citation

  • Pourbabaee, Farzad, 2021. "High dimensional decision making, upper and lower bounds," Economics Letters, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001713
    DOI: 10.1016/j.econlet.2021.109894
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176521001713
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2021.109894?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Al-Najjar, Nabil I. & Pai, Mallesh M., 2014. "Coarse decision making and overfitting," Journal of Economic Theory, Elsevier, vol. 150(C), pages 467-486.
    2. Luciano Pomatto & Philipp Strack & Omer Tamuz, 2018. "The Cost of Information: The Case of Constant Marginal Costs," Papers 1812.04211, arXiv.org, revised Feb 2023.
    3. Nabil I. Al-Najjar & Luca Anderlini & Leonardo Felli, 2006. "Undescribable Events," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 849-868.
    4. Hamsa Bastani & Mohsen Bayati, 2020. "Online Decision Making with High-Dimensional Covariates," Operations Research, INFORMS, vol. 68(1), pages 276-294, January.
    5. Li, Jian, 2019. "The K-armed bandit problem with multiple priors," Journal of Mathematical Economics, Elsevier, vol. 80(C), pages 22-38.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Farzad Pourbabaee, 2021. "High Dimensional Decision Making, Upper and Lower Bounds," Papers 2105.00545, arXiv.org.
    2. Azar, Pablo D. & Micali, Silvio, 2018. "Computational principal agent problems," Theoretical Economics, Econometric Society, vol. 13(2), May.
    3. Larionov, Daniil & Pham, Hien & Yamashita, Takuro & Zhu, Shuguang, 2021. "First Best Implementation with Costly Information Acquisition," TSE Working Papers 21-1261, Toulouse School of Economics (TSE), revised Apr 2022.
    4. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    5. Bartosz Maćkowiak & Filip Matějka & Mirko Wiederholt, 2023. "Rational Inattention: A Review," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 226-273, March.
    6. Rong Jin & David Simchi-Levi & Li Wang & Xinshang Wang & Sen Yang, 2021. "Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses," Management Science, INFORMS, vol. 67(8), pages 4756-4771, August.
    7. Hébert, Benjamin & Woodford, Michael, 2023. "Rational inattention when decisions take time," Journal of Economic Theory, Elsevier, vol. 208(C).
    8. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
    9. Dewan, Ambuj & Neligh, Nathaniel, 2020. "Estimating information cost functions in models of rational inattention," Journal of Economic Theory, Elsevier, vol. 187(C).
    10. David Walker-Jones, 2019. "Rational Inattention and Perceptual Distance," Papers 1909.00888, arXiv.org, revised Dec 2019.
    11. Andrew Postlewaite, 2007. "Courts of Law and Unforeseen Contingencies," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 23(3), pages 662-684, October.
    12. Mark Whitmeyer, 2022. "Making Information More Valuable," Papers 2210.04418, arXiv.org, revised Dec 2023.
    13. Matthias Lang, 2020. "Mechanism Design with Narratives," CESifo Working Paper Series 8502, CESifo.
    14. Xi Chen & Quanquan Liu & Yining Wang, 2023. "Active Learning for Contextual Search with Binary Feedback," Management Science, INFORMS, vol. 69(4), pages 2165-2181, April.
    15. M’hand Fares, 2011. "Can a specific performance contract solve the hold-up problem? [Un contrat à obligation d’exécution peut-il résoudre le problème du hold-up ?]," Post-Print hal-02647357, HAL.
    16. Farzad Pourbabaee, 2022. "Robust experimentation in the continuous time bandit problem," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 73(1), pages 151-181, February.
    17. Jose Luis Montiel Olea & Pietro Ortoleva & Mallesh M Pai & Andrea Prat, 2019. "Competing Models," Papers 1907.03809, arXiv.org, revised Nov 2021.
    18. In-Koo Cho & Jonathan Libgober, 2021. "Machine Learning for Strategic Inference," Papers 2101.09613, arXiv.org.
    19. Claudio Cardoso Flores & Marcelo Cunha Medeiros, 2020. "Online Action Learning in High Dimensions: A Conservative Perspective," Papers 2009.13961, arXiv.org, revised Mar 2024.
    20. Agam Gupta & Biswatosh Saha & Uttam K. Sarkar, 2017. "Emergent Heterogeneity in Keyword Valuation in Sponsored Search Markets: A Closer-to-Practice Perspective," Computational Economics, Springer;Society for Computational Economics, vol. 50(4), pages 687-710, December.

    More about this item

    Keywords

    Decision making; Information valuation; High dimensional vectors;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001713. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.