IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/90804.html

Generalizing mechanism design theory to a case where agents' types are adjustable

Author

Listed:
  • Wu, Haoyang

Abstract

In mechanism design theory, a designer would like to implement a desired social choice function which specifies her favorite outcome for each possible profile of all agents' types. Since the designer does not know each agent's private type, what she can do is to construct a mechanism and choose an outcome after observing a profile of agents' strategies. There is a dilemma in the sense that even if the designer is not satisfied with some outcome, she has to obey the mechanism designed by herself and announce this outcome. In this paper, we generalize the mechanism design theory to a case where the designer can take some action to actively adjust agents' private types, and yield a more favorite outcome. After defining a series of notions such as adjustable types, optimal adjustment cost and profitably Bayesian implementability, we propose that the traditional notion of Bayesian incentive compatibility does not hold in this generalized case. Finally, we construct a model to illustrate that the auctioneer can obtain an expected profit greater than what she obtains in the traditional optimal auction.

Suggested Citation

  • Wu, Haoyang, 2018. "Generalizing mechanism design theory to a case where agents' types are adjustable," MPRA Paper 90804, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:90804
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/90804/1/MPRA_paper_90804.pdf
    File Function: original version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/90941/1/MPRA_paper_90941.pdf
    File Function: revised version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/90981/1/MPRA_paper_90981.pdf
    File Function: revised version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/91304/1/MPRA_paper_91304.pdf
    File Function: revised version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/91743/1/MPRA_paper_91743.pdf
    File Function: revised version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/94242/1/MPRA_paper_94242.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roger B. Myerson, 1981. "Optimal Auction Design," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 58-73, February.
    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. Arve, Malin & Zwart, Gijsbert, 2023. "Optimal procurement and investment in new technologies under uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    2. Yuya Wakabayashi & Ryosuke Sakai & Shigehiro Serizawa, 2022. "A Characterization of the Minimum Price Walrasian Rule with Reserve Prices for an Arbitrary Number of Agents and Objects," ISER Discussion Paper 1161, Institute of Social and Economic Research, The University of Osaka.
    3. Siddharth Prasad & Maria-Florina Balcan & Tuomas Sandholm, 2025. "Revenue-Optimal Efficient Mechanism Design with General Type Spaces," Papers 2505.13687, arXiv.org.
    4. Nicolas Gruyer, 2009. "Optimal Auctions When A Seller Is Bound To Sell To Collusive Bidders," Journal of Industrial Economics, Wiley Blackwell, vol. 57(4), pages 835-850, December.
    5. Yeon-Koo Che & Ian Gale, 1994. "Auctions with budget-constrained buyers: a nonequivalence result," Working Papers (Old Series) 9402, Federal Reserve Bank of Cleveland.
    6. Scott Fay & Robert Zeithammer, 2017. "Bidding for Bidders? How the Format for Soliciting Supplier Participation in NYOP Auctions Impacts Channel Profit," Management Science, INFORMS, vol. 63(12), pages 4324-4344, December.
    7. Hanming Fang & Peter Norman, 2014. "Toward an efficiency rationale for the public provision of private goods," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 56(2), pages 375-408, June.
    8. Bogetoft, Peter & Nielsen, Kurt, 2003. "Yardstick Based Procurement Design In Natural Resource Management," 2003 Annual Meeting, August 16-22, 2003, Durban, South Africa 25910, International Association of Agricultural Economists.
    9. Shunda, Nicholas, 2009. "Auctions with a buy price: The case of reference-dependent preferences," Games and Economic Behavior, Elsevier, vol. 67(2), pages 645-664, November.
    10. Siao-Leu Phouratsamay & Safia Kedad-Sidhoum & Fanny Pascual, 2021. "Coordination of a two-level supply chain with contracts," 4OR, Springer, vol. 19(2), pages 235-264, June.
    11. Paulo Monteiro, 2009. "Abstract types and distributions in independent private value auctions," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 40(3), pages 497-507, September.
    12. Stefano Galavotti, 2014. "Reducing Inefficiency in Public Good Provision Through Linking," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 16(3), pages 427-466, June.
    13. Yiding Feng & Jason Hartline & Yingkai Li, 2020. "Simple Mechanisms for Agents with Non-linear Utilities," Papers 2003.00545, arXiv.org, revised Oct 2022.
    14. Strausz, Roland, 2006. "Deterministic versus stochastic mechanisms in principal-agent models," Journal of Economic Theory, Elsevier, vol. 128(1), pages 306-314, May.
    15. Stephen Leider & William S. Lovejoy, 2016. "Bargaining in Supply Chains," Management Science, INFORMS, vol. 62(10), pages 3039-3058, October.
    16. Wei He & Jiangtao Li & Weijie Zhong, 2024. "Rank-Guaranteed Auctions," Papers 2408.12001, arXiv.org.
    17. Schmitz, Patrick W., 2003. "On second-price auctions and imperfect competition," Journal of Mathematical Economics, Elsevier, vol. 39(8), pages 901-909, November.
    18. Pablo Guillen & Róbert F. Veszteg, 2021. "Strategy-proofness in experimental matching markets," Experimental Economics, Springer;Economic Science Association, vol. 24(2), pages 650-668, June.
    19. Anil Aswani & Zuo-Jun Max Shen & Auyon Siddiq, 2019. "Data-Driven Incentive Design in the Medicare Shared Savings Program," Operations Research, INFORMS, vol. 67(4), pages 1002-1026, July.
    20. Frank Kelly & Peter Key & Neil Walton, 2016. "Efficient Advert Assignment," Operations Research, INFORMS, vol. 64(4), pages 822-837, August.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations

    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:pra:mprapa:90804. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.