IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v181y2019i1d10.1007_s10957-018-1442-y.html
   My bibliography  Save this article

Power Diagram Detection with Applications to Information Elicitation

Author

Listed:
  • Steffen Borgwardt

    (University of Colorado Denver)

  • Rafael M. Frongillo

    (University of Colorado Boulder)

Abstract

Power diagrams, a type of weighted Voronoi diagram, have many applications throughout operations research. We study the problem of power diagram detection: determining whether a given finite partition of $${\mathbb {R}}^d$$ R d takes the form of a power diagram. This detection problem is particularly prevalent in the field of information elicitation, where one wishes to design contracts to incentivize self-minded agents to provide honest information. We devise a simple linear program to decide whether a polyhedral cell complex can be described as a power diagram. For positive instances, a representation of the cell complex as a power diagram is returned. Further, we discuss applications to property elicitation, peer prediction, and mechanism design, where this question arises. Our model can efficiently decide the question for complexes of $${\mathbb {R}}^d$$ R d or of a convex subset thereof. The approach is based on the use of an alternative representation of power diagrams and invariance of a power diagram under uniform scaling of the parameters in this representation.

Suggested Citation

  • Steffen Borgwardt & Rafael M. Frongillo, 2019. "Power Diagram Detection with Applications to Information Elicitation," Journal of Optimization Theory and Applications, Springer, vol. 181(1), pages 184-196, April.
  • Handle: RePEc:spr:joptap:v:181:y:2019:i:1:d:10.1007_s10957-018-1442-y
    DOI: 10.1007/s10957-018-1442-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-018-1442-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10957-018-1442-y?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. Nolan Miller & Paul Resnick & Richard Zeckhauser, 2005. "Eliciting Informative Feedback: The Peer-Prediction Method," Management Science, INFORMS, vol. 51(9), pages 1359-1373, September.
    2. Bartlett, Peter L. & Jordan, Michael I. & McAuliffe, Jon D., 2006. "Convexity, Classification, and Risk Bounds," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 138-156, March.
    3. Borgwardt, S. & Brieden, A. & Gritzmann, P., 2017. "An LP-based k-means algorithm for balancing weighted point sets," European Journal of Operational Research, Elsevier, vol. 263(2), pages 349-355.
    4. David Hartvigsen, 1992. "Recognizing Voronoi Diagrams with Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 4(4), pages 369-374, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Frongillo, Rafael M. & Kash, Ian A., 2021. "General truthfulness characterizations via convex analysis," Games and Economic Behavior, Elsevier, vol. 130(C), pages 636-662.
    2. Norde, Henk & Voorneveld, Mark, 2019. "Feasible best-response correspondences and quadratic scoring rules," SSE Working Paper Series in Economics 2019:2, Stockholm School of Economics.

    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. Julio César Hernández-Sánchez & José Luis Vicente-Villardón, 2017. "Logistic biplot for nominal data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(2), pages 307-326, June.
    2. Peysakhovich, Alexander & Plagborg-Møller, Mikkel, 2012. "A note on proper scoring rules and risk aversion," Economics Letters, Elsevier, vol. 117(1), pages 357-361.
    3. Xiangyan Kong & Zhen Zhang & Qilong Feng, 2023. "On parameterized approximation algorithms for balanced clustering," Journal of Combinatorial Optimization, Springer, vol. 45(1), pages 1-14, January.
    4. Aperjis, Christina & Zeckhauser, Richard J. & Miao, Yali, 2014. "Variable temptations and black mark reputations," Games and Economic Behavior, Elsevier, vol. 87(C), pages 70-90.
    5. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    6. Charness, Gary & Gneezy, Uri & Rasocha, Vlastimil, 2021. "Experimental methods: Eliciting beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 234-256.
    7. António Osório, 2017. "Judgement and ranking: living with hidden bias," Annals of Operations Research, Springer, vol. 253(1), pages 501-518, June.
    8. Gary Bolton & Ben Greiner & Axel Ockenfels, 2013. "Engineering Trust: Reciprocity in the Production of Reputation Information," Management Science, INFORMS, vol. 59(2), pages 265-285, January.
    9. Ghysels, Eric & Babii, Andrii & Chen, Xi & Kumar, Rohit, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," CEPR Discussion Papers 15418, C.E.P.R. Discussion Papers.
    10. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2006. "Robust Learning from Bites for Data Mining," Technical Reports 2006,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    11. Xiang Zhang & Yichao Wu & Lan Wang & Runze Li, 2016. "Variable selection for support vector machines in moderately high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 53-76, January.
    12. Yaoyao Xu & Menggang Yu & Ying‐Qi Zhao & Quefeng Li & Sijian Wang & Jun Shao, 2015. "Regularized outcome weighted subgroup identification for differential treatment effects," Biometrics, The International Biometric Society, vol. 71(3), pages 645-653, September.
    13. Siddarth Srinivasan & Ezra Karger & Yiling Chen, 2023. "Self-Resolving Prediction Markets for Unverifiable Outcomes," Papers 2306.04305, arXiv.org.
    14. Antonio Merlo & Áureo de Paula, 2017. "Identification and Estimation of Preference Distributions When Voters Are Ideological," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(3), pages 1238-1263.
    15. Ewa Zawojska & Michał Krawczyk, 2022. "Incentivizing stated preference elicitation with choice-matching in the field," Working Papers 2022-04, Faculty of Economic Sciences, University of Warsaw.
    16. Andrew Bennett & Nathan Kallus, 2020. "Efficient Policy Learning from Surrogate-Loss Classification Reductions," Papers 2002.05153, arXiv.org.
    17. Shohei Ohsawa, 2021. "Truthful Self-Play," Papers 2106.03007, arXiv.org, revised Feb 2023.
    18. Azrieli, Yaron, 2022. "Delegated expertise: Implementability with peer-monitoring," Games and Economic Behavior, Elsevier, vol. 132(C), pages 240-254.
    19. Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.
    20. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2007. "Robust learning from bites for data mining," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 347-361, September.

    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:spr:joptap:v:181:y:2019:i:1:d:10.1007_s10957-018-1442-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.