Paper published in a book (Scientific congresses, symposiums and conference proceedings)
Blockchain Governance: An Overview and Prediction of Optimal Strategies Using Nash Equilibrium
Khan, Nida; Ahmad, Tabrez; Patel, Anass et al.
In pressIn 3rd AUE International Research Conference
Peer reviewed
 

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Keywords :
Blockchain governance; IT governance; Nash Equilibrium; Game Theory; Mathematical optimization
Abstract :
[en] Blockchain governance is a subject of ongoing research and an interdisciplinary view of blockchain governance is vital to aid in further research for establishing a formal governance framework for this nascent technology. In this paper, the position of blockchain governance within the hierarchy of Institutional governance is discussed. Blockchain governance is analyzed from the perspective of IT governance using Nash equilibrium to predict the outcome of different governance decisions. A payoff matrix for blockchain governance is created and simulation of different strategy profiles is accomplished for computation of all Nash equilibria. We also create payoff matrices for different kinds of blockchain governance, which were used to propose novel mathematical formulae usable to predict the best governance strategy that minimizes the occurrence of a hard fork as well as predicts the behavior of the majority during protocol updates.
Disciplines :
Computer science
Author, co-author :
Khan, Nida ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Ahmad, Tabrez;  ArcelorMittal, Europe
Patel, Anass;  570easi, France
State, Radu  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
yes
Language :
English
Title :
Blockchain Governance: An Overview and Prediction of Optimal Strategies Using Nash Equilibrium
Publication date :
In press
Event name :
AUEIRC 2020
Event date :
25-03-2020 to 26-03-2020
Audience :
International
Main work title :
3rd AUE International Research Conference
Publisher :
Springer
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Finance
FnR Project :
FNR11617092 - Data Analytics And Smart Contracts For Traceability In Finance, 2017 (01/03/2017-31/01/2021) - Nida Khan
Available on ORBilu :
since 10 February 2020

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