IDEAS home Printed from https://ideas.repec.org/a/eee/gamebe/v134y2022icp199-228.html
   My bibliography  Save this article

The secretary recommendation problem

Author

Listed:
  • Hahn, Niklas
  • Hoefer, Martin
  • Smorodinsky, Rann

Abstract

We revisit the basic variant of the classical secretary problem. We propose a new approach in which we separate between an agent (the sender) that evaluates the secretary performance and one (the receiver) that makes the hiring decision. The sender signals the quality of the candidate to the hiring agent. Whenever the two agents' interests are not fully aligned, this induces an information transmission (signaling) challenge for the sender. We study the sender's optimization problem subject to persuasiveness constraints for the receiver in several variants of the problem. Our results quantify the loss in performance for the sender due to online arrival. We provide optimal and near-optimal persuasive mechanisms. In most cases the sender can recover at least a constant fraction of the utility that he would have obtained had he been able to access all information at the outset.

Suggested Citation

  • Hahn, Niklas & Hoefer, Martin & Smorodinsky, Rann, 2022. "The secretary recommendation problem," Games and Economic Behavior, Elsevier, vol. 134(C), pages 199-228.
  • Handle: RePEc:eee:gamebe:v:134:y:2022:i:c:p:199-228
    DOI: 10.1016/j.geb.2022.05.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0899825622000793
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.geb.2022.05.002?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. Emir Kamenica & Matthew Gentzkow, 2011. "Bayesian Persuasion," American Economic Review, American Economic Association, vol. 101(6), pages 2590-2615, October.
    2. Jeffrey Ely & Alexander Frankel & Emir Kamenica, 2015. "Suspense and Surprise," Journal of Political Economy, University of Chicago Press, vol. 123(1), pages 215-260.
    3. Moran Feldman & Ola Svensson & Rico Zenklusen, 2018. "A Simple O (log log(rank))-Competitive Algorithm for the Matroid Secretary Problem," Mathematics of Operations Research, INFORMS, vol. 43(2), pages 638-650, May.
    4. Renault, Jérôme & Solan, Eilon & Vieille, Nicolas, 2017. "Optimal dynamic information provision," Games and Economic Behavior, Elsevier, vol. 104(C), pages 329-349.
    5. Arieli, Itai & Babichenko, Yakov, 2019. "Private Bayesian persuasion," Journal of Economic Theory, Elsevier, vol. 182(C), pages 185-217.
    6. Pak Hung Au, 2015. "Dynamic information disclosure," RAND Journal of Economics, RAND Corporation, vol. 46(4), pages 791-823, October.
    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. Ronen Gradwohl & Niklas Hahn & Martin Hoefer & Rann Smorodinsky, 2020. "Reaping the Informational Surplus in Bayesian Persuasion," Papers 2006.02048, arXiv.org.
    2. Ehud Lehrer & Dimitry Shaiderman, 2022. "Markovian Persuasion with Stochastic Revelations," Papers 2204.08659, arXiv.org, revised May 2022.
    3. Anton Kolotilin & Tymofiy Mylovanov & Andriy Zapechelnyuk & Ming Li, 2017. "Persuasion of a Privately Informed Receiver," Econometrica, Econometric Society, vol. 85(6), pages 1949-1964, November.
    4. Kolotilin, Anton & Li, Hongyi, 2021. "Relational communication," Theoretical Economics, Econometric Society, vol. 16(4), November.
    5. Parakhonyak, Alexei & Vikander, Nick, 2023. "Information design through scarcity and social learning," Journal of Economic Theory, Elsevier, vol. 207(C).
    6. Ehud Lehrer & Dimitry Shaiderman, 2021. "Markovian Persuasion," Papers 2111.14365, arXiv.org.
    7. Koessler, Frederic & Laclau, Marie & Renault, Jérôme & Tomala, Tristan, 2022. "Long information design," Theoretical Economics, Econometric Society, vol. 17(2), May.
    8. Au, Pak Hung & Kawai, Keiichi, 2020. "Competitive information disclosure by multiple senders," Games and Economic Behavior, Elsevier, vol. 119(C), pages 56-78.
    9. Shih-Tang Su & Vijay G. Subramanian & Grant Schoenebeck, 2021. "Bayesian Persuasion in Sequential Trials," Papers 2110.09594, arXiv.org, revised Nov 2021.
    10. Gur, Yonatan & Macnamara, Gregory & Saban, Daniela, 2020. "On the Disclosure of Promotion Value in Platforms with Learning Sellers," Research Papers 3865, Stanford University, Graduate School of Business.
    11. Mensch, Jeffrey, 2021. "Rational inattention and the monotone likelihood ratio property," Journal of Economic Theory, Elsevier, vol. 196(C).
    12. Dirk Bergemann & Stephen Morris, 2019. "Information Design: A Unified Perspective," Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
    13. Caio Lorecchio, 2022. "Persuading crowds," UB School of Economics Working Papers 2022/434, University of Barcelona School of Economics.
    14. Geng, Sen & Guan, Menglong, 2023. "Trustworthy by design," Games and Economic Behavior, Elsevier, vol. 141(C), pages 70-87.
    15. Daehong Min, 2021. "Bayesian persuasion under partial commitment," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 72(3), pages 743-764, October.
    16. Escudé, Matteo & Sinander, Ludvig, 2023. "Slow persuasion," Theoretical Economics, Econometric Society, vol. 18(1), January.
      • Matteo Escud'e & Ludvig Sinander, 2019. "Slow persuasion," Papers 1903.09055, arXiv.org, revised Apr 2022.
    17. Yishay Mansour & Aleksandrs Slivkins & Vasilis Syrgkanis, 2019. "Bayesian Incentive-Compatible Bandit Exploration," Operations Research, INFORMS, vol. 68(4), pages 1132-1161, July.
    18. Pak Hung Au & Keiichi Kawai, 2021. "Competitive disclosure of correlated information," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 72(3), pages 767-799, October.
    19. Miltiadis Makris & Ludovic Renou, 2018. "Information design in multi-stage games," Working Papers 861, Queen Mary University of London, School of Economics and Finance.
    20. Jibang Wu & Zixuan Zhang & Zhe Feng & Zhaoran Wang & Zhuoran Yang & Michael I. Jordan & Haifeng Xu, 2022. "Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning," Papers 2202.10678, arXiv.org.

    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:gamebe:v:134:y:2022:i:c:p:199-228. 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/inca/622836 .

    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.