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Power Diagram Detection with Applications to Information Elicitation

Author

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. Frongillo, Rafael M. & Kash, Ian A., 2021. "General truthfulness characterizations via convex analysis," Games and Economic Behavior, Elsevier, vol. 130(C), pages 636-662.

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