This book presents the best papers from the 6th International Conference on Mathematical Research for Blockchain Economy (MARBLE) 2025, held in Athens, Greece. 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 revolutionizing 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, and security and showcase solutions and developments to overcome them.

Autorentext

Stefanos Leonardos is a lecturer in Machine Learning at the Department of Informatics, King's College London, UK. His research interests span the areas of game theory and its applications in blockchain-enabled, digital economic systems, artificial intelligence, machine learning, and in strategic interactions that dynamically evolve over time. He has won numerous best paper awards for his research on the design of decentralized economies and on the analysis of exploration-exploitation algorithms in multi-agent systems.

Amir K. Goharshady is an associate professor of Computer Science at the Oxford University, UK and a Fellow of St Catherine's College where he also leads the ALPACAS research group. He is also a visiting professor at the Department of Computer Science and Engineering of the Indian Institute of Technology Bombay. His research interests are in theoretical computer science, specifically formal program verification, parametrised algorithms, compiler optimisation and blockchain. He holds a PhD in Theoretical Computer Science from the Institute of Science and Technology Austria, an MSc in Computer Science from Georgia Tech and undergraduate degrees in Maths and Computing from the Universities of London and Yazd. Prior to joining Oxford, he was an Assistant Professor of Computing and Mathematics at HKUST and used to be actively involved in organising high school Olympiads.

William Knottenbelt is a professor of Applied Quantitative Analysis at the Department of Computing, Imperial College, London, UK. His broad areas of research interest are the application of mathematical modelling techniques to real-life systems. Specific areas of interest include but are not limited to modelling and optimization 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 techniques for SLA specification, compliance prediction, and monitoring.

Panos Pardalos is an emeritus distinguished professor of Industrial and Systems Engineering at the University of Florida, USA. He also holds the Paul and Heidi Brown Preeminent Professor title in the same department. Additionally, he is the director of the Center for Applied Optimization and is an affiliated faculty member of the Computer and Information Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. Professor Pardalos is a world-leading expert in global and combinatorial optimization. He is also a member of several Academies of Sciences and holds honorary Ph.D. degrees and affiliations. He is a 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.