A Unique and Stable $$\hbox {Se}{\mathcal {C}}\hbox {ure}$$ Se C ure Reversion Protocol Improving Efficiency: A Computational Bayesian Approach for Empirical Analysis

Cédric Wanko ()
Additional contact information
Cédric Wanko: Université Montpellier 1

Computational Economics, 2018, vol. 52, issue 1, No 1, 23 pages

Abstract: Abstract In Bayesian mechanism we demonstrate the unicity and the stability of secure reversion protocols in which risk-averse players have no incentive to cheat or to deviate from the meditator’s recommendation and that can greatly improve their equilibrium expected payoffs as compared to those generated through correlation device. The main idea of this work is to show the ease with which we can compute the equilibrium expected payoffs for players. Furthermore, we emphasize those results through a numerical simulation for which the method is able to be used for empirical analysis.

Keywords: Revelation principle; Correlated equilibrium distribution; Collective decision process; Secure reversion protocol (search for similar items in EconPapers)
JEL-codes: C70 C72 D78 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9646-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:52:y:2018:i:1:d:10.1007_s10614-017-9646-z

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-017-9646-z

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().