Multi-strategy evolutionary games: A Markov chain approach

Mahdi Hajihashemi and Keivan Aghababaei Samani

PLOS ONE, 2022, vol. 17, issue 2, 1-17

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0263979

DOI: 10.1371/journal.pone.0263979

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