Models and verifies state-dependent Markov games using Attributed C-sets and GF(3) conservation laws.
Markov Game ACSet is a specialized skill designed to bridge the gap between compositional open games and state-dependent stochastic processes. By leveraging the AlgebraicJulia ACSet framework, it enables developers and researchers to model complex multi-agent interactions where strategy spaces and payoffs depend on the current environment state. The skill introduces rigorous constraints such as derangement-constrained transitions—ensuring information reflow by preventing self-loops—and GF(3) trit conservation across state changes. It is an essential tool for formalizing economic protocols, decentralized systems, and game-theoretic models that require rigorous categorical foundations.
主な機能
01GF(3) trit conservation auditing across state-action-state transitions
02Automated verification of derangement constraints to ensure information reflow
032 GitHub stars
04ACSet schema implementation for Markov game modeling
05State-graph visualization with support for Gay-coded color palettes
06Markov-Nash equilibrium computation for state-dependent strategies
ユースケース
01Formalizing information flow and entropy conservation in complex systems
02Designing and verifying compositional economic protocols in decentralized finance
03Modeling multi-agent reinforcement learning environments using categorical algebra
