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      Computational implementation

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      Author(s)
      Barlo, M.
      Dalkıran, Nuh Aygün
      Date
      2022-12
      Print ISSN
      1434-4742
      Publisher
      Review of Economic Design
      Volume
      26
      Issue
      4
      Pages
      605 - 633
      Language
      English
      Type
      Article
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      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. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
      Keywords
      Behavioral implementation
      Computation
      Consistent collections
      Maskin monotonicity
      Maximal domain
      Nash implementation
      Permalink
      http://hdl.handle.net/11693/111498
      Published Version (Please cite this version)
      https://doi.org/10.1007/s10058-021-00282-3
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      • Department of Economics 724
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