An evolutionary game with environmental feedback and players' opinions

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Evolutionary games are a developing sub-field of game theory. This branch of game theory is used in the study of the adaptation of large, but finite, populations of agents to changes in the environment. It assumes that each agent has no significant influence on the system. Many scientific areas use the theory of evolutionary games. In particular, it is used in biology, medicine and the modelling of wireless networks. In this paper we study an evolutionary game with two levels of interaction between population agents. At the first level, changes in the population state depend on changes in the environment and on increasing or decreasing the resources available to the agents. At the second level, the population's state changes according to how the agents evaluate the state of the environment. These levels make up a decision-making structure with two levels. A change in one parameter of the system, for example the state of the environment, causes a change in the other elements of the system, that is, a change in the state of the population and the opinions of the agents. The study involves the analysis of a modified evolutionary game taking into account the influence of the environment and the opinions of the agents. It also involves the development of computational methods in MATLAB and two sets of numerical experiments.

Sobre autores

Ekaterina Lorits

Saint Petersburg State University

Email: kate.lorits@gmail.com
St. Petersburg

Elena Gubar

Saint Petersburg State University

Email: e.gubar@spbu.ru
St. Petersburg

Bibliografia

  1. КОЛЕСИН И.Д., ГУБАР Е.А., ЖИТКОВА Е.М. Стратегииуправления в медико-социальных системах. – СПб.: Изд-воС.-Петерб. ун-та, 2014. – 128 c.
  2. КУРНОСЫХ З.А., ГУБАР Е.А. Моделирование эволюцион-ной игры с учетом сетевой структуры // Процессы управ-ления и устойчивость. – 2017. – Т. 4, № 1. – С. 631–635.
  3. ЛОРИЦ Е.М. Эволюционная игра с учетом обратной свя-зи с окружающей средой и мнениями игроков // Процессыуправления и устойчивость.–2023.–Т.10,№ 1.–С.462–466.
  4. МАЗАЛОВ В.В., ДОРОФЕЕВА Ю.А., КОНОВАЛЬЧИКО-ВА Е.Н. Моделирование влияния среди участников обра-зовательного коллектива // Вестник Санкт-Петербургскогоуниверситета. – 2019. – Т. 15, Вып. 2. – С. 259–273.
  5. ПЕТРОСЯН Л.А., ЗЕНКЕВИЧ Н.А., ШЕВКОПЛЯС Е.В.Теория Игр. – СПб.: БХВ-Петербург, 2012. – 432 с.
  6. ARGASINSKI K., BROOM M. Evolutionary stability underlimited population growth: Eco-evolutionary feedbacks andreplicator dynamics // Ecol. Complex. – 2017. – Vol. 34, No. 6.
  7. BAYER P., GATENBY R. ET AL. Coordination games incancer // PLoS ONE. – 2022. – Vol. 17, Iss. 1. – Art. e0261578.
  8. BROOM M., KRIVAN V. Two-strategy games with timeconstraints on regular graphs // Journal of TheoreticalBiology. – 2020. – Vol. 506. – Art. 110426.
  9. BROOM M., RYCHTAR J. Game-Theoretical Models inBiology. – CRC Press, 2022. – 591 p.
  10. BROWN J.S., THUIJSMAN F. ET AL. The contributionof evolutionary game theory to understanding and treatingcancer // Dynamic Games and Applications. – 2022. – Vol. 12. –P. 313–342.
  11. CRESSMAN R. Evolutionary Dynamics and Extensive FormGames. – Cambridge: MIT Press, 2003. – 316 p.
  12. MENG Y., BROOM M., LI A. Impact of misinformation in theevolution of collective cooperation. – 2023.
  13. PAARPORN K., EKSIN C. ET AL. Optimal control policiesfor evolutionary dynamics with environmental feedback // IEEEConf. on Decision and Control (CDC). – 2018. – P. 1905–1910.
  14. PRESSLEY M., SALVIOLI M. Evolutionary dynamics oftreatment-induced resistance in cancer informs understandingof rapid evolution in natural systems // Frontiers in Ecologyand Evolution. – 2021. – Vol. 9. – Art. 681121.
  15. RIEHL J.R., CAO M. Control of stochastic evolutionary gameson networks // IFAC. – 2015. – Vol. 48, Iss. 22. – P. 76–81.
  16. SANDHOLM W.H. Population Games and EvolutionaryDynamics. – Cambridge: MIT Press, 2010. – 616 p.
  17. TEMBINE H., ALTMAN E., EL-AZOUZI R., HAYEL Y.Evolutionary games in wireless networks // IEEE Trans. onSystems, Man, and Cybernetics. – 2009. – Vol. 40. Iss. 3. –P. 634–646.
  18. VINCENT T.L., BROWN J.S. Evolutionary Game Theory,Natural Selection, and Darwinian Dynamics. – New York:Cambridge University Press, 2005. – 400 p.
  19. WEIBULL J.W. Evolutionary Game Theory. – Cambridge: MITPress, 1995. – 265 p.
  20. WEITZ J.S., EKSIN C., PAARPORN K. ET AL. An oscillatingtragedy of the commons in replicator dynamics with game-environment feedback // PNAS. – 2016. – Vol. 113, No 47. –P. E7518–E7525.
  21. ZHILIANG Z., YULI Z. ET AL. Evolutionary game dynamicsof the competitive information propagation on social networks //Complexity. – 2019. – Vol. 2019. – Art. 8385426.
  22. ZHU Q., GUBAR E., ALTMAN E. (EDS.). Special Issue onModeling and Control of Epidemics. // Dynamic Games andApplications. – 2022. – Vol. 12.

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