
Multi-strategy evolutionary games: A Markov chain approach
Author(s) -
Mahdi Hajihashemi,
Keivan Aghababaei Samani
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0263979
Subject(s) - markov chain , markov chain mixing time , continuous time markov chain , stationary distribution , markov process , evolutionary dynamics , markov model , computer science , ergodic theory , mathematical optimization , mathematics , variable order markov model , population , statistical physics , physics , machine learning , statistics , mathematical analysis , demography , sociology
Interacting strategies in evolutionary games is studied analytically in a well-mixed population using a Markov chain method. By establishing a correspondence between an evolutionary game and Markov chain dynamics, we show that results obtained from the fundamental matrix method in Markov chain dynamics are equivalent to corresponding ones in the evolutionary game. In the conventional fundamental matrix method, quantities like fixation probability and fixation time are calculable. Using a theorem in the fundamental matrix method, conditional fixation time in the absorbing Markov chain is calculable. Also, in the ergodic Markov chain, the stationary probability distribution that describes the Markov chain’s stationary state is calculable analytically. Finally, the Rock, scissor, paper evolutionary game are evaluated as an example, and the results of the analytical method and simulations are compared. Using this analytical method saves time and computational facility compared to prevalent simulation methods.