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Machine Learning for Optimal Economic Design

  • Paul Dütting
  • Zhe Feng
  • Noah Golowich
  • Harikrishna Narasimhan
  • David C. ParkesEmail author
  • Sai Srivatsa Ravindranath
Chapter
Part of the Studies in Economic Design book series (DESI)

Abstract

This position paper anticipates ways in which the disruptive developments in machine learning over the past few years could be leveraged for a new generation of computational methods that automate the process of designing optimal economic mechanisms.

Notes

Acknowledgements

This research is supported in part by NSF EAGER #124110.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Paul Dütting
    • 1
  • Zhe Feng
    • 2
  • Noah Golowich
    • 2
  • Harikrishna Narasimhan
    • 2
  • David C. Parkes
    • 2
    Email author
  • Sai Srivatsa Ravindranath
    • 2
  1. 1.Department of MathematicsLondon School of EconomicsLondonUK
  2. 2.Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeUSA

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