TY - JOUR
T1 - EGTtools
T2 - Evolutionary game dynamics in Python
AU - Fernández Domingos, Elias
AU - Santos, Francisco C.
AU - Lenaerts, Tom
N1 - © 2023 The Author(s).
PY - 2023/4/21
Y1 - 2023/4/21
N2 - 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 analytical 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.
AB - 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 analytical 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.
KW - Evolutionary processes
KW - In silico biology
KW - Social sciences
UR - http://www.scopus.com/inward/record.url?scp=85152147748&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2023.106419
DO - 10.1016/j.isci.2023.106419
M3 - Article
C2 - 37102153
AN - SCOPUS:85152147748
VL - 26
JO - iScience
JF - iScience
SN - 2589-0042
IS - 4
M1 - 106419
ER -