Encyclopedia of Algorithms

Living Edition
| Editors: Ming-Yang Kao

Reducing Bayesian Mechanism Design to Algorithm Design

  • Yang Cai
  • Constantinos Daskalakis
  • Matthew Weinberg
Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-27848-8_787-1

Years and Authors of Summarized Original Work

  • STOC2012; Cai, Daskalakis, Weinberg

  • FOCS2012; Cai, Daskalakis, Weinberg

  • SODA2013; Cai, Daskalakis, Weinberg

  • FOCS2013; Cai, Daskalakis, Weinberg

  • SODA2015; Daskalakis, Weinberg

Problem Definition

The goal is to design algorithms that succeed in models where input is reported by strategic agents (henceforth referred to as strategic input), as opposed to standard models where the input is directly given (henceforth referred to as honest input). For example, consider a resource allocation problem where a single user has m jobs to process on n self-interested machines. Each machine i can process job j in time ti j, and this is privately known only to the machine. Each machine reports some processing times \(\hat{t}_{ij}\)


Mechanism design Job scheduling Fair allocation Revenue maximization Equivalence of separation and optimization 
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Recommended Reading

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Yang Cai
    • 1
  • Constantinos Daskalakis
    • 2
  • Matthew Weinberg
    • 3
  1. 1.Computer ScienceMcGill UniversityMontreal, QCCanada
  2. 2.EECSMassachusetts Institute of TechnologyCambridge, MAUSA
  3. 3.Computer SciencePrinceton UniversityPrinceton, NJUSA