Parallelized Monte Carlo methods for algorithmic mechanism design
Implementing parallelization and GPU acceleration for the Haskell 'monad-bayes' library, enhancing performance for Ethereum-based probabilistic programming and game theory applications.User Review
AI Review
A1
Reviewed on 15 Feb 2024 07:56 AM
The Grant must be in support of, or directly advancing the Ethereum ecosystem.
The project aims to improve the performance of the `monad-bayes` Haskell library, which can enhance Compositional Game Theory applications. This could be directly beneficial for research in Ethereum spaces like MEV, PBS, financial incentive analysis of DeFi protocols, and DAO governance — all of which are important components of the Ethereum ecosystem.
The project must be open source.
The project's repository is hosted on GitHub under the username 'CyberCat-Institute,' which implies the project code is openly available. The description also mentions that Compositional Game Theory is open source under the MIT license.