IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2403.17515.html
   My bibliography  Save this paper

Prediction-sharing During Training and Inference

Author

Listed:
  • Yotam Gafni
  • Ronen Gradwohl
  • Moshe Tennenholtz

Abstract

Two firms are engaged in a competitive prediction task. Each firm has two sources of data -- labeled historical data and unlabeled inference-time data -- and uses the former to derive a prediction model, and the latter to make predictions on new instances. We study data-sharing contracts between the firms. The novelty of our study is to introduce and highlight the differences between contracts that share prediction models only, contracts to share inference-time predictions only, and contracts to share both. Our analysis proceeds on three levels. First, we develop a general Bayesian framework that facilitates our study. Second, we narrow our focus to two natural settings within this framework: (i) a setting in which the accuracy of each firm's prediction model is common knowledge, but the correlation between the respective models is unknown; and (ii) a setting in which two hypotheses exist regarding the optimal predictor, and one of the firms has a structural advantage in deducing it. Within these two settings we study optimal contract choice. More specifically, we find the individually rational and Pareto-optimal contracts for some notable cases, and describe specific settings where each of the different sharing contracts emerge as optimal. Finally, in the third level of our analysis we demonstrate the applicability of our concepts in a synthetic simulation using real loan data.

Suggested Citation

  • Yotam Gafni & Ronen Gradwohl & Moshe Tennenholtz, 2024. "Prediction-sharing During Training and Inference," Papers 2403.17515, arXiv.org.
  • Handle: RePEc:arx:papers:2403.17515
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2403.17515
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gentzkow, Matthew & Kamenica, Emir, 2017. "Bayesian persuasion with multiple senders and rich signal spaces," Games and Economic Behavior, Elsevier, vol. 104(C), pages 411-429.
    2. Dirk Bergemann & Alessandro Bonatti, 2019. "Markets for Information: An Introduction," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 85-107, August.
    3. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    4. Ronen Gradwohl & Moshe Tennenholtz, 2022. "Pareto-Improving Data-Sharing," Papers 2205.11295, arXiv.org.
    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. Áron Tóbiás, 2023. "Cognitive limits and preferences for information," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 221-253, June.
    2. Mingfeng Tang & Mei Mei & Cuiwen Li & Xingyang Lv & Xushuang Li & Lihao Wang, 2020. "How does an individual’s default behavior on an online peer-to-peer lending platform influence an observer’s default intention?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-20, December.
    3. Dirk Bergemann & Alessandro Bonatti, 2024. "Data, Competition, and Digital Platforms," American Economic Review, American Economic Association, vol. 114(8), pages 2553-2595, August.
    4. Wolfgang Pointner & Burkhard Raunig, 2018. "A primer on peer-to-peer lending: immediate financial intermediation in practice," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q3/18, pages 36-51.
    5. Zhou Rongxi & Xiong Yahui & Wang Ning & Wang Xizu, 2019. "Coupling Degree Evaluation of China’s Internet Financial Ecosystem Based on Entropy Method and Principal Component Analysis," Journal of Systems Science and Information, De Gruyter, vol. 7(5), pages 399-421, October.
    6. Pak Hung Au & Mark Whitmeyer, 2018. "Attraction versus Persuasion: Information Provision in Search Markets," Papers 1802.09396, arXiv.org, revised May 2022.
    7. 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.
    8. de Pedraza, Pablo & Vollbracht, Ian, 2020. "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series 515, Global Labor Organization (GLO).
    9. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    10. Christa N. Gibbs & Benedict Guttman-Kenney & Donghoon Lee & Scott Nelson & Wilbert Van der Klaauw & Jialan Wang, 2024. "Consumer Credit Reporting Data," Staff Reports 1114, Federal Reserve Bank of New York.
    11. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    12. Kräussl, Roman & Kräussl, Zsofia & Pollet, Joshua & Rinne, Kalle, 2024. "The performance of marketplace lenders," Journal of Banking & Finance, Elsevier, vol. 162(C).
    13. Yeh, Jen-Yin & Chiu, Hsin-Yu & Huang, Jhih-Huei, 2024. "Predicting failure of P2P lending platforms through machine learning: The case in China," Finance Research Letters, Elsevier, vol. 59(C).
    14. Claude Fluet & Thomas Lanzi, 2021. "Cross-Examination," Working Papers of BETA 2021-40, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    15. Mark Whitmeyer & Cole Williams, 2024. "Dynamic Signals," Papers 2407.16648, arXiv.org.
    16. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    17. Tianle Song, 2022. "Quality Disclosure and Product Selection," Journal of Industrial Economics, Wiley Blackwell, vol. 70(2), pages 323-346, June.
    18. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asu Ozdaglar, 2022. "Too Much Data: Prices and Inefficiencies in Data Markets," American Economic Journal: Microeconomics, American Economic Association, vol. 14(4), pages 218-256, November.
    19. Au, Pak Hung & Kawai, Keiichi, 2020. "Competitive information disclosure by multiple senders," Games and Economic Behavior, Elsevier, vol. 119(C), pages 56-78.
    20. Aneta Dzik-Walczak & Mateusz Heba, 2019. "A comparison of credit scoring techniques in Peer-to-Peer lending," Working Papers 2019-16, Faculty of Economic Sciences, University of Warsaw.

    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:2403.17515. 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.