This project aims to implement parallelization and GPU acceleration features for the monad-bayes[1][2] Haskell library, a probabilistic programming library.
These extensions will result in faster performance and better optimization for all methods relying on Bayesian reasoning. In the context of the broader Ethereum ecosystem, this in particular means better performance for the growing corpus of protocols and research projects employing Compositional Game Theory, such as core research in MEV and PBS, financial incentive analysis of DeFi protocols and DAO governance.
The last few years have seen a sharp rise in the employment of algorithmic mechanism design methods in the context of DeFi incentive alignment, MEV (Maximal Extractable Value), and PBS (Proposer-Builder Separation) research, with a steady inflow of new players in the crypto mechanism design space.
In particular, in the last years Compositional Game Theory has imposed itself as a reliable choice for fast prototypation and analysis of games and mechanisms.
Compared to other approaches in algorithmic mechanism design, Compositional Game Theory[3] relies on a novel mathematical paradigm called Compositionality, which defines games as processes that can be composed out of the box in ways that preserve their semantics.
What this means in practice is that Compositional Game Theory allows for much faster model prototyping as games an...