Reference : Blockchain Governance: An Overview and Prediction of Optimal Strategies Using Nash Eq...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences; Finance
	To cite this reference:	http://hdl.handle.net/10993/42468

Title :	Blockchain Governance: An Overview and Prediction of Optimal Strategies Using Nash Equilibrium
Language :	English
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) > >]
Publication date :	In press
Main document title :	3rd AUE International Research Conference
Publisher :	Springer
Peer reviewed :	Yes
Audience :	International
Event name :	AUEIRC 2020
Event date :	25-03-2020 to 26-03-2020
Keywords :	[en] 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.
Permalink :	http://hdl.handle.net/10993/42468
FnR project :	FnR ; FNR11617092 > Nida Khan > > Data Analytics and Smart Contracts for traceability in finance > 01/03/2017 > 31/01/2021 > 2017

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