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Computational implementation

Author

Listed:
  • Mehmet Barlo

    (Sabancı University)

  • Nuh Aygün Dalkıran

    (Bilkent University)

Abstract

Following a theoretical analysis of the scope of Nash implementation for a given mechanism, we study the formal framework for computational identification of Nash implementability. We provide computational tools for Nash implementation in finite environments. In particular, we supply Python codes that identify (i) the domain of preferences that allows Nash implementation by a given mechanism, (ii) the maximal domain of preferences that a given mechanism Nash implements Pareto efficiency, (iii) all consistent collections of sets of a given social choice correspondence (SCC), the existence of which is a necessary condition for Nash implementation of this SCC, and (iv) check whether some of the well-known sufficient conditions for Nash implementation hold for a given SCC. Our results exhibit that the computational identification of all collections consistent with an SCC enables the planner to design appealing mechanisms.

Suggested Citation

  • Mehmet Barlo & Nuh Aygün Dalkıran, 2022. "Computational implementation," Review of Economic Design, Springer;Society for Economic Design, vol. 26(4), pages 605-633, December.
    • Handle: RePEc:spr:reecde:v:26:y:2022:i:4:d:10.1007_s10058-021-00282-3
    DOI: 10.1007/s10058-021-00282-3
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    References listed on IDEAS

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    Cited by:

    1. T. Hayashi & R. Jain & V. Korpela & M. Lombardi, 2023. "Behavioral strong implementation," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 76(4), pages 1257-1287, November.
    2. Barlo, Mehmet & Dalkıran, Nuh Aygün, 2023. "Behavioral implementation under incomplete information," Journal of Economic Theory, Elsevier, vol. 213(C).

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    More about this item

    Keywords

    Nash implementation; Computation; Maskin monotonicity; Consistent collections; Maximal domain; Behavioral implementation;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations
    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General

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