# Dynamic Data-Driven Digital Twins for Blockchain Systems

arXiv: https://arxiv.org/abs/2312.04226 · HTML: https://mlsanity.com/paper/2312.04226/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [An Exploration of Blockchain Enabled Decentralized Capability based Access Control Strategy for Space Situation Awareness](https://mlsanity.com/paper/1810.01291/) (2018)
- [Hybrid Blockchain-Enabled Secure Microservices Fabric for Decentralized Multi-Domain Avionics Systems](https://mlsanity.com/paper/2004.10674/) (2020)
- [Blockchain-based Digital Twins: Research Trends, Issues, and Future Challenges](https://mlsanity.com/paper/2103.11585/) (2021)
- [Digital Twins for Dynamic Management of Blockchain Systems](https://mlsanity.com/paper/2204.12477/) (2022)

## Built on by (most cited first, 2 of 2)

- [TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.06610/) (2026)
- [Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction](https://mlsanity.com/paper/2608.30050/) (2026)

## Abstract (arXiv)

In recent years, we have seen an increase in the adoption of blockchain-based systems in non-financial applications, looking to benefit from what the technology has to offer. Although many fields have managed to include blockchain in their core functionalities, the adoption of blockchain, in general, is constrained by the so-called trilemma trade-off between decentralization, scalability, and security. In our previous work, we have shown that using a digital twin for dynamically managing blockchain systems during runtime can be effective in managing the trilemma trade-off. Our Digital Twin leverages DDDAS feedback loop, which is responsible for getting the data from the system to the digital twin, conducting optimisation, and updating the physical system. This paper examines how leveraging DDDAS feedback loop can support the optimisation component of the trilemma benefiting from Reinforcement Learning agents and a simulation component to augment the quality of the learned model while reducing the computational overhead required for decision-making.