Computational implementation

Barlo, Mehmet and Dalkıran, Nuh Aygün (2021) Computational implementation. (Accepted)

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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.
Item Type: Article
Uncontrolled Keywords: Nash Implementation, Computation, Maskin Monotonicity, Consistent Collections, Maximal Domain, Behavioral Implementation
Subjects: H Social Sciences > HB Economic Theory > HB135-147 Mathematical economics. Quantitative methods
H Social Sciences > HB Economic Theory
Divisions: Faculty of Arts and Social Sciences > Academic programs > Economics
Faculty of Arts and Social Sciences
Depositing User: Mehmet Barlo
Date Deposited: 02 Feb 2022 15:55
Last Modified: 02 Feb 2022 15:55

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