As a project that started as a mere summer curiosity and grew into a class-project-gone-wild, I built a Python codebase for running game theoretic discrete-time replicator dynamics simulations for non-technical users inspired by libraries such as NashPy. It allows users to see probabilities of outcomes, examine strategy distributions, and apply custom parameters such as correlation, mutation, and multiple populations to existing games like the Prisoner’s Dilemma or Hawk/Dove as well as custom games input via matrix.
I conducted this endeavor in concert with Rory Smead, a professor with whom I’ve taken four classes, been a teaching assistant, and participated in a multidisciplinary reading group with for more than a year - his knowledge of game theory and his guidance towards interesting topics and useful functionalities was instrumental for the project.
The codebase and a guide for how to use it can be found here! If you have questions/comments, I’d love to hear them - feel free to reach out via any of the links below.