Toolbox for Evolutionary Game Theory.

Simulate evolutionary biology models with Gillespie algorithm.

Digital evolution model dedicated to bacterial in silico experimental evolution.

A two-player dynamic game where deep Q-learning agents collaborate to find optimal solutions to a stochastic version of the graph coloring problem, featuring α-Rank for joint policy ranking and evaluation.

Julia package to easily build and simulate evolutionary models

System to simulate individual interactions and capture ecological and evolutionary dynamics

Evolutionary dynamics in tree networks

Code Availability for "Generalized queueing-based evolutionary dynamics rescues cooperation in social dilemmas."

Additional analysis at SSP at Albion College: Investigating Erythromycin resistance in V. natriegens (2025). Beyond identifying atpD and XRE regulator mutations, we utilized PCA of k-mer frequency vectors and 3D base composition modeling to map fitness landscapes. Findings reveal how environmental stress reshapes adaptive evolutionary trajectories.

Résolution du Paradoxe de Levinthal et de l'Abiogenèse par la Frustration Géométrique. La vie comme état métastable de la matière, pilotée par la minimisation de l'énergie libre sur des paysages rugueux.

This repository contains Python and NetLogo simulations based on the paper "The conditional defector strategies can violate the most crucial supporting mechanisms of cooperation" (Scientific Reports, 2022). The models explore evolution of cooperation, conditional defection, and mechanisms like kin selection, spatial structure, and punishment.

A Python library for simulating two‑player strategic interactions with memory, shocks, and recovery dynamics. It supports large‑scale Monte Carlo experiments to study how behavioral norms form, adapt, and regain stability after temporary disruptions.

4th Year Project - Optimal Control of Directed Evolution

A command line program to generate phylogenies and SNP configurations using simulation of coalescent models with structured populations.

Framework for testing strategic reasoning in LLMs using evolutionary Iterated Prisoner’s Dilemma tournaments. Features 28 agents, variable horizons, memory conditions, high-concurrency execution, and detailed analysis of cooperation rates, strategic fingerprints, and LLM decision rationales across providers and temperatures.

From Nowak's Evolvability Equations to AI Agent Architectures — Living research + design principles

An interactive multi-agent simulation demonstrating why control-based, deceptive, and reward-bypassing AI objectives are structurally self-eliminating — and why long-horizon, system-aware coordination is the attractor. Built to accompany The Alignment of Intelligence, Article 2: Attractor.

The FIT (Force–Information–Time) Framework is a falsifiable, phase-conditional framework for analyzing evolutionary dynamics across systems.