EGTtools: Evolutionary game dynamics in Python

Evolutionary Game Theory (EGT) provides an important framework to study collective behavior. It combines ideas from evolutionary biology and population dynamics with the game theoretical modeling of strategic interactions. Its importance is highlighted by the numerous high level publications that have enriched different fields, ranging from biology to social sciences, in many decades. Nevertheless, there has been no open source library that provided easy, and efficient, access to these methods and models. Here, we introduce EGTtools, an efficient hybrid C++/Python library which provides fast implementations of both analyt-ical and numerical EGT methods. EGTtools is able to analytically evaluate a system based on the replicator dynamics. It is also able to evaluate any EGT problem resorting to finite populations and large-scale Markov processes. Finally, it resorts to C++ and MonteCarlo simulations to estimate many important indicators, such as stationary or strategy distributions. We illustrate all these methodologies with concrete examples and analysis.

Automated summary

Evolutionary Game Theory (EGT) is an important framework for studying collective behavior, combining ideas from evolutionary biology, population dynamics, and game theoretical modeling. EGTtools is an efficient hybrid C++/Python library that provides fast implementations of both analytical and numerical EGT methods. It is capable of analytically evaluating systems based on replicator dynamics, as well as evaluating any EGT problem using finite populations and large-scale Markov processes. It utilizes C++ and MonteCarlo simulations to estimate important indicators such as stationary or strategy distributions.