IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1905.01805.html
   My bibliography  Save this paper

Computing a Data Dividend

Author

Listed:
  • Eric Bax

Abstract

Quality data is a fundamental contributor to success in statistics and machine learning. If a statistical assessment or machine learning leads to decisions that create value, data contributors may want a share of that value. This paper presents methods to assess the value of individual data samples, and of sets of samples, to apportion value among different data contributors. We use Shapley values for individual samples and Owen values for combined samples, and show that these values can be computed in polynomial time in spite of their definitions having numbers of terms that are exponential in the number of samples.

Suggested Citation

  • Eric Bax, 2019. "Computing a Data Dividend," Papers 1905.01805, arXiv.org, revised Jun 2019.
    • Handle: RePEc:arx:papers:1905.01805
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1905.01805
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Milgrom,Paul, 2004. "Putting Auction Theory to Work," Cambridge Books, Cambridge University Press, number 9780521536721.
    2. Nisan,Noam & Roughgarden,Tim & Tardos,Eva & Vazirani,Vijay V. (ed.), 2007. "Algorithmic Game Theory," Cambridge Books, Cambridge University Press, number 9780521872829.
    3. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    4. Benjamin Edelman & Michael Ostrovsky & Michael Schwarz, 2007. "Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords," American Economic Review, American Economic Association, vol. 97(1), pages 242-259, March.
    5. Jonathan D. Levin, 2011. "The Economics of Internet Markets," NBER Working Papers 16852, National Bureau of Economic Research, Inc.
    6. Hal R. Varian, 2009. "Online Ad Auctions," American Economic Review, American Economic Association, vol. 99(2), pages 430-434, May.
    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. Eric Bax, 2020. "Heavy Tails Make Happy Buyers," Papers 2002.09014, arXiv.org.
    2. Yan Chen & Peter Cramton & John A. List & Axel Ockenfels, 2021. "Market Design, Human Behavior, and Management," Management Science, INFORMS, vol. 67(9), pages 5317-5348, September.
    3. Krishnamurthy Iyer & Ramesh Johari & Mukund Sundararajan, 2014. "Mean Field Equilibria of Dynamic Auctions with Learning," Management Science, INFORMS, vol. 60(12), pages 2949-2970, December.
    4. James Li & Eric Bax & Nilanjan Roy & Andrea Leistra, 2015. "VCG Payments for Portfolio Allocations in Online Advertising," Papers 1506.02013, arXiv.org.
    5. Bax, Eric & Kuratti, Anand & Mcafee, Preston & Romero, Julian, 2012. "Comparing predicted prices in auctions for online advertising," International Journal of Industrial Organization, Elsevier, vol. 30(1), pages 80-88.
    6. Scott Duke Kominers & Alexander Teytelboym & Vincent P Crawford, 2017. "An invitation to market design," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 33(4), pages 541-571.
    7. Frank Kelly & Peter Key & Neil Walton, 2016. "Efficient Advert Assignment," Operations Research, INFORMS, vol. 64(4), pages 822-837, August.
    8. Ming Chen & Sareh Nabi & Marciano Siniscalchi, 2023. "Advancing Ad Auction Realism: Practical Insights & Modeling Implications," Papers 2307.11732, arXiv.org, revised Apr 2024.
    9. Philippe Jehiel & Laurent Lamy, 2020. "On the Benefits of Set-Asides," Journal of the European Economic Association, European Economic Association, vol. 18(4), pages 1655-1696.
    10. Jonathan Levin, 2011. "The Economics of Internet Markets," Discussion Papers 10-018, Stanford Institute for Economic Policy Research.
    11. Axel Ockenfels & David Reiley & Abdolkarim Sadrieh, 2006. "Online Auctions," NBER Working Papers 12785, National Bureau of Economic Research, Inc.
    12. Eric Budish & Estelle Cantillon, 2012. "The Multi-unit Assignment Problem: Theory and Evidence from Course Allocation at Harvard," American Economic Review, American Economic Association, vol. 102(5), pages 2237-2271, August.
    13. Satoru Fujishige & Zaifu Yang, 2020. "A Universal Dynamic Auction for Unimodular Demand Types: An Efficient Auction Design for Various Kinds of Indivisible Commodities," Discussion Papers 20/08, Department of Economics, University of York.
    14. Estrella Alonso & Joaquín Sánchez-Soriano & Juan Tejada, 2020. "Mixed Mechanisms for Auctioning Ranked Items," Mathematics, MDPI, vol. 8(12), pages 1-26, December.
    15. Dipankar Das, 2023. "A Model of Competitive Assortment Planning Algorithm," Papers 2307.09479, arXiv.org.
    16. Tayfun Sönmez & Tobias B. Switzer, 2013. "Matching With (Branch‐of‐Choice) Contracts at the United States Military Academy," Econometrica, Econometric Society, vol. 81(2), pages 451-488, March.
    17. Yeon-Koo Che & Erin Cho, 2011. "Brave New World of Market Design," Korean Economic Review, Korean Economic Association, vol. 27, pages 33-56.
    18. Aditya Bhave & Eric Budish, 2017. "Primary-Market Auctions for Event Tickets: Eliminating the Rents of 'Bob the Broker'?," NBER Working Papers 23770, National Bureau of Economic Research, Inc.
    19. Tomoya Kazumura & Debasis Mishra & Shigehiro Serizawa, 2017. "Strategy-proof multi-object auction design: Ex-post revenue maximization with no wastage," ISER Discussion Paper 1001, Institute of Social and Economic Research, Osaka University.
    20. Dütting, Paul & Fischer, Felix & Parkes, David C., 2019. "Expressiveness and robustness of first-price position auctions," LSE Research Online Documents on Economics 85877, London School of Economics and Political Science, LSE Library.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:1905.01805. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.