Research Topic

Mathematical Tools for Blockchain: from Stochastic to Mean Field Games Approaches

About this Research Topic

During recent years a wide number of different approaches have been developed to take analyse, classify, quantify and forecasting data/values characterizing the rapid growth, both in volume and complexity, of financial markets, related set of rules imposed by governments, on-line transactions activities, and related fraud detection solutions, etc.

Within such a scenario, methods belonging to Machine Learning, Inferential Statistics, Stochastic Analysis and linked algorithms have been developed, as in the case of the celebrated Blockchain Technology (BT).
The latter, seen as a shared data, immutable structure defined via a digital register
based on cryptography systems, represents the most successful example of Distributed Ledgers (DL) solutions.

Since new blocks can be added exclusively via their management by an external, but shared, protocol, implies that the Blockchain nodes may not know each other's identity. In particular, to the addition of a new node corresponds a "private" update performed by each of the previously present nodes.

This feature, typical of the DL-structures, guarantees not only data digitalization, but, more importantly, its decentralization, the disintermediation of operations and the possibility of keeping track of individual transfers.
As a result, we gain: transparency, verifiability, register immutability, and transfers programming.
The main focus of the present article collection is to gather together contributions which aim at overpass the Blockchain-cryptography point of view, as to include more recent Machine Learning technologies, and Stochastic (particularly SPDEs and SMFGs based) tools. The final goal being to provide the status quo of DL/MathFin-inspired solutions as well as to constitute a stimulating arena where both practitioners, data scientists and mathematicians can exchange ideas and presents innovations to push ahead related applications, also from the algorithmic point of view.

Therefore, contributions related to the secure managing of financial transactions, distributed social network dynamics and control, new DL-inspired transaction protocols, digital values managing, DL-based forecasting of material/immaterial goods, insurance/finance peer-to-peer solutions, smart-contracts and their concrete applications, alternative (Altchain) technologies and inspired comparisons, as well as numerical schemes and theoretical stochastic based analysis of DL-related dynamics, will be more than welcome.


Keywords: Deep Learning, Mathematical Finance, Blockchain, cryptography, Altchain, Stochastic Analysis, Inferential Statistics


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

During recent years a wide number of different approaches have been developed to take analyse, classify, quantify and forecasting data/values characterizing the rapid growth, both in volume and complexity, of financial markets, related set of rules imposed by governments, on-line transactions activities, and related fraud detection solutions, etc.

Within such a scenario, methods belonging to Machine Learning, Inferential Statistics, Stochastic Analysis and linked algorithms have been developed, as in the case of the celebrated Blockchain Technology (BT).
The latter, seen as a shared data, immutable structure defined via a digital register
based on cryptography systems, represents the most successful example of Distributed Ledgers (DL) solutions.

Since new blocks can be added exclusively via their management by an external, but shared, protocol, implies that the Blockchain nodes may not know each other's identity. In particular, to the addition of a new node corresponds a "private" update performed by each of the previously present nodes.

This feature, typical of the DL-structures, guarantees not only data digitalization, but, more importantly, its decentralization, the disintermediation of operations and the possibility of keeping track of individual transfers.
As a result, we gain: transparency, verifiability, register immutability, and transfers programming.
The main focus of the present article collection is to gather together contributions which aim at overpass the Blockchain-cryptography point of view, as to include more recent Machine Learning technologies, and Stochastic (particularly SPDEs and SMFGs based) tools. The final goal being to provide the status quo of DL/MathFin-inspired solutions as well as to constitute a stimulating arena where both practitioners, data scientists and mathematicians can exchange ideas and presents innovations to push ahead related applications, also from the algorithmic point of view.

Therefore, contributions related to the secure managing of financial transactions, distributed social network dynamics and control, new DL-inspired transaction protocols, digital values managing, DL-based forecasting of material/immaterial goods, insurance/finance peer-to-peer solutions, smart-contracts and their concrete applications, alternative (Altchain) technologies and inspired comparisons, as well as numerical schemes and theoretical stochastic based analysis of DL-related dynamics, will be more than welcome.


Keywords: Deep Learning, Mathematical Finance, Blockchain, cryptography, Altchain, Stochastic Analysis, Inferential Statistics


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

31 January 2021 Abstract
31 May 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

31 January 2021 Abstract
31 May 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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