Autonomous Agent Evolutionary Stable Strategy (ESS) & Replicator Dynamics Population ODE Solver
-
Updated
Sep 9, 2026 - Python
Autonomous Agent Evolutionary Stable Strategy (ESS) & Replicator Dynamics Population ODE Solver
Autonomous Agent Evolutionary Stable Strategy (ESS) & Replicator Dynamics Population ODE Solver
CPU-first experiments on predictive correspondences between adaptive dynamical systems
Convert multivariate time series into evolutionary games; fit replicator dynamics and test for ESS (ts2eg).
A lightweight Python library for Game Theory and Multi-Agent Reinforcement Learning (MARL) visualizations.
The forge, distilled: an ontology of three weeks of alignment research — every direction tried, colored verified / falsified / open, each color backed by a named artifact. Products: justitia, proxylimen, fallacy-cutter. Full tree at tag forge-full-tree.
Learn and simulate game theory in the browser: Nash solver, strategy tournaments, spatial grids and evolutionary dynamics.
Seminar materials on replicator dynamics, MWU, and their evolutionary motivation.
Fast solving of Bayesian matrix games with replicator dynamics
Python code for plotting evolutionary game phase portraits and analyzing replicator dynamics
Agent identity as a forgeable greenbeard in an evolutionary AI race: certification buys robustness not safety, and pays with an entry barrier that cannot be removed
Deployment-layer selection in evolutionary AI-race dynamics: closed-form liability thresholds, bistability and hysteresis
Empirical Validation of ROM Consent-Friction Dynamics — code companion to ROM (DAI-2503) and AoC (DAI-2601)
Das wiederholte Gefangenendilemma: zehn Strategien im Rundenturnier, Rauschen, Evolution - und die Einladungsschwelle gemessen gegen ihre Herleitung
Delegation cascades in an evolutionary AI race: specification erosion, per-layer liability attribution, and depth as a liability shelter
From GNN fundamentals to solving an NP-hard problem: message passing, over-smoothing, and Maximum Clique with a GNN + Replicator Dynamics hybrid
Python-based evolutionary market simulation using yfinance data, historical S&P 500 membership, and replicator dynamics to compare algorithmic trading strategies against passive SPY buy-and-hold.
Code, data, and figures for 'A corrective constraint increases multi-target attainment under finite adaptive search' (Brashears, Gladstone, and Ferrer), under review at npj Complexity.
To associate your repository with the replicator-dynamics topic, visit your repo's landing page and select "manage topics."