Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)


For citation:

Ivanko E. E., Chervinsky S. M. Survival Rate of Model Populations Depending on the Strategy of Energy Exchange Between the Organisms. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2020, vol. 20, iss. 2, pp. 241-256. DOI: 10.18500/1816-9791-2020-20-2-241-256, EDN: NJNWUB

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
01.06.2020
Full text:
(downloads: 238)
Language: 
Russian
Heading: 
Article type: 
Article
UDC: 
519.688:519.876.5
EDN: 
NJNWUB

Survival Rate of Model Populations Depending on the Strategy of Energy Exchange Between the Organisms

Autors: 
Ivanko Evgeny Evgenievich, Institute of Mathematics and Mechanics, Ural Branch of the Russian Academy of Sciences
Chervinsky Serge Mironovich, Institute of Mathematics and Mechanics, Ural Branch of the Russian Academy of Sciences
Abstract: 

The paper addresses the influence of the energy exchange strategy between the organisms of a population in a gradually changing environment on the survival rate of this population. At the first stage of computational experiments, a “boundary” region is determined in the space of two parameters (mutation rate and energy supply rate), within which the survival of populations with zero energy exchange is ambiguous (lies in the interval from 5 to 95%). At the second stage, on the basis of a random sampling of experimental conditions from the boundary region, the dependence of the survival rate of model populations on the fraction of energy transferred during interaction from an organism with larger energy to an organism with smaller one is constructed. The performed experiments demonstrate: 1) the positive effect of altruistic energy exchange (where the organism with larger energy plays the role of a donor) on the survival rate of the populations and 2) the absence of an observable influence of the amount of energy transferred by the parent to the newborn on the survival rate of the populations. The results obtained may be of interest for the construction of artificial populations, for example, in the design of swarms of medical nanorobots or in the development of evolutionary metaheuristic algorithms for solving various optimization problems.

References: 
  1. Lorenz E. N. Deterministic nonperiodic flow // Journal of the Atmospheric Sciences. 1963. Vol. 20, № 2. P. 130–141.
  2.  Bak P., Tang C., Wiesenfeld K. Self-organized criticality: An explanation of the 1/f noise // Phys. Rev. Lett. 1987. Vol. 59, iss. 4. P. 381–384. DOI: https://doi.org/10.1103/PhysRevLett.59.381
  3. May R. Simple mathematical models with very complicated dynamics // Nature. 1976. Vol. 261, № 5560. P. 459–467. DOI: https://doi.org/10.1038/261459a0
  4. Collier N. RePast: An extensible framework for agent simulation // Natural Resources and Environmental Issues. 2001. Vol. 8. Article 4. URL: https://digitalcommons.usu.edu/nrei/vol8/iss1/4 (дата обращения: 07.03.2019).
  5. Tisue S., Wilensky U. NetLogo: A simple environment for modeling complexity // International Conference on Complex Systems. 2004. Vol. 21. P. 16–21.
  6. Luke S., Cioffi-Revilla C., Panait L., Sullivan K., Balan G. Mason: A multiagent simulation environment // Simulation. 2005. Vol. 81, № 7. P. 517–527. DOI: https://doi.org/10.1177/0037549705058073
  7. Trevorrow A., Rokicki T., Hutton T., Greene D., Summers J., Verver M. Golly – a game of life simulator. URL: http://golly.sourceforge.net/ (дата обращения: 07.03.2019).
  8. Sayama H. PyCX: A Python-based simulation code repository for complex systems education // Complex Adaptive Systems Modeling. 2013. Vol. 1. P. 2. DOI: https://doi.org/10.1186/2194-3206-1-2
  9. Waldrop M. M. Complexity: The Emerging Science at the Edge of Order and Chaos. N. Y. : Simon & Schuster, 1992. 380 p.
  10. Sayama H. Introduction to the Modeling and Analysis of Complex Systems. N. Y. : SUNY Binghamton, 2015. 478 p.
  11. Hamann H. Swarm Robotics: A Formal Approach. N. Y. : Springer International Publishing, 2018. 210 p. DOI: https://doi.org/10.1007/978-3-319-74528-2
  12. Fitzhugh R. Impulses and Physiological States in Theoretical Models of Nerve Membrane // Biophysical Journal. 1961. Vol. 1, № 6. P. 445–466. DOI: https://doi.org/10.1016/S0006-3495(61)86902-6
  13. Drossel B., Schwabl F. Self-organized criticality in a forest-fire model // Physica A : Statistical Mechanics and its Applications. 1992. Vol. 191, № 1. P. 47–50. DOI: https://doi.org/10.1016/0378-4371(92)90504-J
  14. Strogatz S. Sync: The Emerging Science of Spontaneous Order. N. Y. : Penguin, 2004. 339 p.
  15. Wolfram S. A New Kind of Science. N. Y. : Wolfram Media, 2002. 1197 p.
  16. Bjorner A., Lovasz L., Shor P. W. Chip-firing games on graphs // European Journal of Combinatorics. 1991. Vol. 12, № 4. P. 283–291. DOI: https://doi.org/10.1016/S0195-6698(13)80111-4
  17. Clifford P., Sudbury A. A model for spatial conflict // Biometrika. 1973. Vol. 60, № 3. P. 581–588. DOI: https://doi.org/10.1093/biomet/60.3.581
  18. Kagel H. J., Roth E. A. The Handbook of Experimental Economics. N. J. : Princeton Univ. Press, 1997. 744 p.
  19. Levin S. A. Public goods in relation to competition, cooperation, and spite // PNAS. 2014. Vol. 111 (Supplement 3). P. 10838–10845. DOI: https://doi.org/10.1073/pnas.1400830111
  20. Obolski U., Lewin-Epstein O., Even-Tov E., Ram Y., Hadany L. With a little help from my friends: cooperation can accelerate the rate of adaptive valley crossing // BMC Evolutionary Biology. 2017. Vol. 17. Article 143. DOI: https://doi.org/10.1186/s12862-017-0983-2
  21. Pfeiffer T., Bonhoeffer S. An evolutionary scenario for the transition to undifferentiated multicellularity // PNAS. 2003. Vol. 100, № 3. P. 1095–1098. DOI: https://doi.org/10.1073/pnas.0335420100
  22. Kreft J.-U. Biofilms promote altruism // Microbiology. 2004. Vol. 150, iss. 8. P. 2751–2760. DOI: https://doi.org/10.1099/mic.0.26829-0
  23. Cesta A., Miceli M., Rizzo P. Coexisting agents: Experiments on basic interaction attitude // Journal of Intelligent Systems. 2001. Vol. 11, iss. 1. P. 1–42. DOI: https://doi.org/10.1515/JISYS.2001.11.1.1
  24.  Ivanko E. Is evolution always “egolution”: Discussion of evolutionary efficiency of altruistic energy exchange // Ecological Complexity. 2018. Vol. 34. P. 1–8. DOI: https://doi.org/10.1016/j.ecocom.2018.02.001
  25. Hamilton W. D. The genetical evolution of social behaviour // Journal of Theoretical Biology. 1964. Vol. 7, № 1. P. 1–52. DOI: https://doi.org/10.1016/0022-5193(64)90038-4
  26. Trivers R. L. The evolution of reciprocal altruism // The Quarterly Review of Biology. 1971. Vol. 46, № 1. P. 35–57. DOI: https://doi.org/10.1086/406755
  27. Axelrod R., Hamilton W. D. The evolution of cooperation // Science. 1981. Vol. 211, № 4489. P. 1390–1396. DOI: https://doi.org/10.1126/science.7466396
  28. Nowak M. A. Five rules for the evolution of cooperation // Science. 2006. Vol. 314, iss. 5805. P. 1560–1563. DOI: https://doi.org/10.1126/science.1133755
  29. Stuart A., West A., Griffin S., Gardner A. Evolutionary explanations for cooperation // Current Biology. 2007. Vol. 17, iss. 16. P. R661–R672. DOI: https://doi.org/10.1016/j.cub.2007.06.004
  30. Lewin-Epstein O., Aharonov R., Hadany L. Microbes can help explain the evolution of host altruism // Nature Communications. 2017. Vol. 8. Article 14040. DOI: https://doi.org/10.1038/ncomms14040
  31. Esteban-Fernandez de ´ Avila B., Angsantikul P., Ram ´ ´ırez-Herrera D. E., Soto F., Teymourian H., Dehaini D., Chen Y., Zhang L., Wang J. Hybrid biomembrane–functionalized nanorobots for concurrent removal of pathogenic bacteria and toxins // Science Robotics. 2018. Vol. 3, iss. 18, eaat0485. DOI: https://doi.org/10.1126/scirobotics.aat0485
  32. Morice C. P., Kennedy J. J., Rayner N. A., Jones P. D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset // Journal of Geophysical Research : Atmospheres. 2012. Vol. 117. D08101. DOI: https://doi.org/10.1029/2011JD017187
  33. Makeham W. M. On the Law of Mortality and the Construction of Annuity Tables // The Assurance Magazine, and Journal of the Institute of Actuaries. 1860. Vol. 8, № 6. P. 301–310. DOI: https://doi.org/10.1017/S204616580000126X
  34. MacArthur R. H., Wilson E. O. The theory of island biogeography. N. J. : Princeton Univ. Press, 2001. 224 p.
  35. Aurenhammer F., Klein R., Lee D.-T. Voronoi Diagrams and Delaunay Triangulations. N. J. : World Scientific Publishing, 2013. 348 p.
  36. Uran cluster. URL: http://parallel.uran.ru/node/419 (дата обращения: 07.03.2019).
  37. Simon D. Evolutionary Optimization Algorithms. N. Y. : Wiley, 2013. 772 p.
  38. Schapire R. E., Freund Y. Y. Boosting: Foundations and Algorithms. Cambridge : The MIT Press, 2012. 544 p.
Received: 
16.05.2019
Accepted: 
08.12.2019
Published: 
01.06.2020