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Encyclopedia of Algorithms pp 1801–1808Cite as

Reducing Bayesian Mechanism Design to Algorithm Design

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  • First Online:

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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 t ij , and this is privately known only to the machine. Each machine reports some processing times \(\hat{t}_{ij}\) to the user, who then runs some algorithm to determine where to process the jobs. Good approximation algorithms are known when machines are honest (i.e., \(\hat{t}_{ij} = t_{ij}\) for all i, j) if the user’s goal is to...

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Recommended Reading

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Author information

Authors and Affiliations

  1. Computer Science, McGill University, Montreal, QC, Canada

    Yang Cai

  2. EECS, Massachusetts Institute of Technology, Cambridge, MA, USA

    Constantinos Daskalakis

  3. Computer Science, Princeton University, Princeton, NJ, USA

    Matthew Weinberg

Authors
  1. Yang Cai
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  2. Constantinos Daskalakis
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  3. Matthew Weinberg
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Corresponding author

Correspondence to Yang Cai .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USA

    Ming-Yang Kao

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© 2016 Springer Science+Business Media New York

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Cai, Y., Daskalakis, C., Weinberg, M. (2016). Reducing Bayesian Mechanism Design to Algorithm Design. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2864-4_787

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