Predict processing, network and storage costs in a decentralized, multi-agent decentralized identity system (DID).
- algebraic closed form solution (analytical model)
- Monte Carlo simulation
- Load testing
Monte Carlo simulation provides a powerful tool for evaluating the performance, scalability, and resilience of blockchain systems. By simulating diverse scenarios and analyzing statistical outcomes, researchers can gain valuable insights into the behavior of blockchain networks and inform decision-making processes. As blockchain technology continues to evolve, Monte Carlo simulation will remain a valuable tool for assessing and optimizing blockchain systems for real-world applications.
https://nextrope.com/monte-carlo-simulations-in-tokenomics/
By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks. This is perhaps the most important function of Monte Carlo Process, since these risks can’t be assessed any other way.
Several tools and software packages can facilitate the implementation of Monte Carlo simulations in tokenomics. One of the most notable is cadCAD, a Python library that provides a flexible and powerful environment for simulating complex systems.
https://github.com/cadCAD-org/cadCAD/blob/master/documentation/README.md
Given a Simulation Configuration, cadCAD produces datasets that represent the evolution of the state of a system over discrete time. The state of the system is described by a set of State Variables. The dynamic of the system is described by Policy Functions and State Update Functions, which are evaluated by cadCAD according to the definitions set by the user in Partial State Update Blocks.
https://www.sciencedirect.com/science/article/pii/S2214212621001824 https://doi.org/10.1016/j.jisa.2021.102971
Our implementation has been compared with the current state of the art implementation of p-ABC systems (namely Idemix [17]), outperforming it during both credential issuance and ZK proof generation and verification (taking between 2 and 4 times less to execute equivalent methods depending on the parameters).