Mathematical Research for Blockchain Economy

2nd International Conference MARBLE 2020, Vilamoura, Portugal
1. Auflage 2020
ISBN: 978-3-030-53355-7
Verlag: Springer International Publishing

Buch, Englisch, 177 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 453 g

Reihe: Springer Proceedings in Business and Economics

This book presents the best papers from the 2nd International Conference on Mathematical Research for Blockchain Economy (MARBLE) 2020, held in Vilamoura, Portugal. While most blockchain conferences and forums are dedicated to business applications, product development or Initial Coin Offering (ICO) launches, this conference focused on the mathematics behind blockchain to bridge the gap between practice and theory.

Blockchain Technology has been considered as the most fundamental and revolutionising invention since the Internet. Every year, thousands of blockchain projects are launched and circulated in the market, and there is a tremendous wealth of blockchain applications, from finance to healthcare, education, media, logistics and more. However, due to theoretical and technical barriers, most of these applications are impractical for use in a real-world business context. The papers in this book reveal the challenges and limitations, such as scalability, latency, privacy andsecurity, and showcase solutions and developments to overcome them.

Zielgruppe

Research

Autoren/Hrsg.

Pardalos, Panos

Knottenbelt, William

Kotsireas, Ilias

Guo, Yike

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftstheorie, Wirtschaftsphilosophie
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensfinanzen
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Bankwirtschaft
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker

Inhaltsverzeichnis

Smart Contract Derivatives.- Bitcoin Crypto–Bounties for Quantum Capable Adversaries.- An Econophysical Analysis of the Blockchain Ecosystem.- Stress Testing Diversified Portfolios: The Case of the CoinShares Gold and Cryptoassets Index.- Selfish Mining in Ethereum.- The Speculative (In)Efficiency of the CME Bitcoin Futures Market.- Carbon Trading with Blockchain.- Economic Games as Estimators.- Promise: Leveraging Future Gains for Collateral Reduction.- Step on the Gas? A Better Approach for Recommending the Ethereum Gas Price

Über Autor(innen)

Panos Pardalos is a Distinguished Professor of Industrial and Systems Engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of Industrial and Systems Engineering. He is also an affiliated faculty member of the Computer and Information Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. He is also the Director of the Center for Applied Optimization. Pardalos is a world leading expert in global and combinatorial optimization.  Pardalos is also a member of several  Academies of Sciences, and he holds several honorary PhD degrees and affiliations. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization, and Computational Management Science. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing. Ilias S. Kotsireas serves as a full professor of Computer Science at Wilfrid Laurier University in Waterloo, Ontario, Canada. In 2002 he founded the CARGO Lab, and serves as its director for more than 15 years. He has over 150 refereed journal and conference publications, chapters in books, edited books and special issues of journals, in the research areas of Computational Algebra, Metaheuristics, High-Performance Computing, Dynamical Systems and Combinatorial Design Theory. He serves on the Editorial Board of 7 international journals. He serves as the Managing Editor of a Springer journal and as the Editor-in-Chief of a Springer journal and a Birkhauser book series. He has organized a very large number of international conferences in Europe, North America and Asia, often serving as a Program Committee Chair or General Chair. 

Yike Guo is a Professor of Computing Science in the Department of Computing at Imperial College London. He is the founding Director of the Data Science Institute  at Imperial College, as well as leading the Discovery Science Group in the department. Professor Guo also holds the position of CTO of the tranSMART Foundation, a global open source community using and developing data sharing and analytics technology for translational medicine. Professor Guo received a first-class honours degree in Computing Science from Tsinghua University, China, in 1985 and received his PhD in Computational Logic from Imperial College in 1993 under the supervision of Professor John Darlington. He founded InforSense, a software company for life science and health care data analysis, and served as CEO for several years before the company's merger with IDBS, a global advanced R&D software provider, in 2009. He has been working on technology and platforms for scientific data analysis since the mid-1990s, where his research focuses on knowledge discovery, data mining and large-scale data management. He is a Senior Member of the IEEE and is a Fellow of the British Computer Society.

William Knottenbelt is Professor of Applied Quantitative Analysis in the Department of Computing at Imperial College, London, UK. His broad area of research interest is the application of mathematical modelling techniques to real life systems. Specific ares of interest include, but are not limited to, modelling and optimisation in parallel queuing systems, modelling of storage systems, stochastic modelling of sport, stochastic modelling of healthcare systems, resource allocation and control in cloud-computing environments,numerical solution of Markov-models and specification techniquesfor SLA specification, compliance prediction and monitoring.