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Ratcheting based on neighboring niches determines lifestyle

  • Ye Ye
  • Xiao Rong Hang
  • Jin Ming Koh
  • Jarosław Adam  Miszczak
  • Kang Hao CheongEmail author
  • Neng Gang XieEmail author
Original paper
  • 27 Downloads

Abstract

In this paper, a co-evolution method of game dynamics and network structure is adopted to demonstrate that neighboring niches of an individual or population may have great influence in determining lifestyle adoption. The model encompasses network structure evolution, denoted CaseA, and pure games participated in by individuals in the network with two asymmetric branches determining winning and losing states, denoted Case B. The selection between game branches is dependent on the demographic of neighboring niches, and favorable or unfavorable effects from the neighborhood can be made to manifest by setting probabilistic game parameters. Theoretical analysis reveals that losing configurations of Case B, when stochastically mixed with neutral Case A, can result in paradoxical winning scenarios where the network experiences positive gain—a Parrondo’s paradox-like phenomenon has therefore emerged. It is elucidated that agitation from Case A increases the probability of individuals to play the favorable branch of Case B, leading to unexpected gains in two distinct parameter regimes. In the paradoxical regions, our analysis suggests strongly that neighboring niches are the cause for evolution toward social or solitary lifestyle behaviors, and we present important connections to real-world biological life.

Keywords

Population dynamics Parrondo’s paradox Nonlinear dynamics Ratcheting Neighboring niches 

Notes

Acknowledgements

This project was supported by the National Natural Science Foundation of China (Grant No.11705002); Ministry of Education, Humanities and Social Sciences research projects (15YJCZH210; 19YJAZH098). KHC and JMK were supported by the SUTD Start-up Research Grant (SRG SCI 2019 142).

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Gardner, M.: Mathematical games: the fantastic combinations of John Conway’s new solitaire game ‘life. Sci. Am. 223, 120–123 (1970)CrossRefGoogle Scholar
  2. 2.
    Berlekamp, E., Conway, J., Guy, R.: Winning Ways for Your Mathematical Plays, Vol. 2: Games in Particular. Academic Press, New York (1982)zbMATHGoogle Scholar
  3. 3.
    Bosch, R.A.: Maximum density stable patterns in variants of Conway’s game of Life. Oper. Res. Lett. 27(1), 7–11 (2000)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Tkachenko, A., Rabin, Y.: Effect of Boundary Conditions on Fluctuations and. Phys. Rev. E 7463(1), 7146–7150 (1997)Google Scholar
  5. 5.
    Nordfalk, J., Alstrøm, P.: Phase transitions near the ‘game of Life. Phys. Rev. E 54(2), R1025–R1028 (1996)CrossRefGoogle Scholar
  6. 6.
    Monetti, R.A., Albano, E.V.: Critical edge between frozen extinction and chaotic life. Phys. Rev. E 52(6), 5825–5831 (1995)CrossRefGoogle Scholar
  7. 7.
    Bak, P., Chen, K., Creutz, M.: Self-organized criticality in the Game of Life. Nature 342(6251), 780–782 (1989)CrossRefGoogle Scholar
  8. 8.
    Jin, W., Chen, F.: Topological chaos of universal elementary cellular automata rule. Nonlinear Dyn. 63(1), 217–222 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Souyah, A., Faraoun, K.M.: An image encryption scheme combining chaos-memory cellular automata and weighted histogram. Nonlinear Dyn. 86(1), 639–653 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Souyah, A., Faraoun, K.M.: Fast and efficient randomized encryption scheme for digital images based on Quadtree decomposition and reversible memory cellular automata. Nonlinear Dyn. 84(2), 715–732 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wang, X., Xu, D.: A novel image encryption scheme using chaos and Langton’s Ant cellular automaton. Nonlinear Dyn. 79(4), 2449–2456 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Toral, R.: Cooperative Parrondo’s games. Fluct. Noise Lett. 1(01), L7–L12 (2001)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Mihailović, Z., Rajković, M.: One dimensional asynchronous cooperative Parrondo’s games. Fluct. Noise Lett. 3(04), L389–L398 (2003)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ethier, S.N., Lee, J.: Parrondo games with spatial dependence and a related spin system, II. Markov Process. Relat. Fields 19(4), 667–692 (2013)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Mihailović, Z., Rajković, M.: Cooperative Parrondo’s games on a two-dimensional lattice. Phys. A Stat. Mech. Appl. 365(1), 244–251 (2006)CrossRefGoogle Scholar
  16. 16.
    Ethier, S.N., Lee, J.: Parrondo games with two-dimensional spatial dependence. Fluct. Noise Lett. 16(01), 1750005 (2017)CrossRefGoogle Scholar
  17. 17.
    Parrondo, J.M.R., Harmer, G.P., Abbott, D.: New paradoxical games based on Brownian ratchets. Phys. Rev. Lett. 85(24), 5226–5229 (2000)CrossRefGoogle Scholar
  18. 18.
    Harmer, G.P., Abbott, D.: A review of Parrondo’s paradox. Fluct. Noise Lett. 02, R71–R107 (2002)CrossRefGoogle Scholar
  19. 19.
    Abbott, D.: Asymmetry and disorder: a decade of parrondo’s paradox. Fluct. Noise Lett. 09(01), 129–156 (2010)CrossRefGoogle Scholar
  20. 20.
    Abbott, D., Harmer, G.P.: Game theory: losing strategies can win by Parrondo’s paradox. Nature 402(6764), 864–864 (1999)CrossRefGoogle Scholar
  21. 21.
    Soo, W.W.M., Cheong, K.H.: Parrondo’s paradox and complementary Parrondo processes. Phys. A Stat. Mech. Appl. 392(1), 17–26 (2013)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Soo, W.W.M., Cheong, K.H.: Occurrence of complementary processes in Parrondo’s paradox. Phys. A Stat. Mech. Appl. 412, 180–185 (2014)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Cheong, K.H., Soo, W.W.M.: Construction of novel stochastic matrices for analysis of Parrondo’s paradox. Phys. A Stat. Mech. Appl. 392(20), 4727–4738 (2013)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Cheong, K.H., Koh, J.M., Jones, M.C.: Paradoxical survival: examining the Parrondo effect across biology. BioEssays 41(6), 1900027 (2019)CrossRefGoogle Scholar
  25. 25.
    Toral, R.: Capital redistribution brings wealth by Parrondo’s paradox. Fluct. Noise Lett. 2(04), L305–L311 (2002)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Ye, Y., Xie, N.-G., Wang, L.-G., Meng, R., Cen, Y.-W.: Study of biotic evolutionary mechanisms based on the multi-agent parrondo’s games. Fluct. Noise Lett. 11(02), 1250012 (2012)CrossRefGoogle Scholar
  27. 27.
    Szolnoki, A., Perc, M.: Information sharing promotes prosocial behaviour. New J. Phys. 15(5), 53010 (2013)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Cheong, K.H., Koh, J.M., Jones, M.C.: Multicellular survival as a consequence of Parrondo’s paradox. Proc. Natl. Acad. Sci. 115(23), E5258–E5259 (2018)CrossRefGoogle Scholar
  29. 29.
    Koh, J.M., Xie, N., Cheong, K.H.: Nomadic-colonial switching with stochastic noise: subsidence-recovery cycles and long-term growth. Nonlinear Dyn. 94(2), 1467–1477 (2018)CrossRefGoogle Scholar
  30. 30.
    Koh, J.M., Cheong, K.H.: Automated electron-optical system optimization through switching Levenberg–Marquardt algorithms. J. Electron Spectros. Relat. Phenomena 227, 31–39 (2018)CrossRefGoogle Scholar
  31. 31.
    Koh, J.M., Cheong, K.H.: New doubly-anomalous Parrondo’s games suggest emergent sustainability and inequality. Nonlinear Dyn. 96, 257–266 (2019)CrossRefGoogle Scholar
  32. 32.
    Cheong, K.H., Koh, J.M., Jones, M.C.: Do arctic hares play Parrondo’s games? Fluct. Noise Lett. 18(03), 1971001 (2019)CrossRefGoogle Scholar
  33. 33.
    Wu, D., Szeto, K.Y.: Extended Parrondo’s game and Brownian ratchets: strong and weak Parrondo effect. Phys. Rev. E 89(2), 22142 (2014)CrossRefGoogle Scholar
  34. 34.
    Gilbert, C., Robertson, G., Le Maho, Y., Naito, Y., Ancel, A.: Huddling behavior in emperor penguins: dynamics of huddling. Physiol. Behav. 88(4–5), 479–488 (2006)CrossRefGoogle Scholar
  35. 35.
    Ancel, A., Beaulieu, M., Le Maho, Y., Gilbert, C.: Emperor penguin mates: keeping together in the crowd. Proc. R. Soc. B Biol. Sci. 276(1665), 2163–2169 (2009)CrossRefGoogle Scholar
  36. 36.
    Ahsan, M.M., Tahir, H.M., Mukhtar, M.K., Ali, A., Kahan, Z.I., Ahmed, K.: Intra-and inter-specific foraging in three scorpion species. Punjab Univ. J. Zool. 31(1), 69–76 (2016)Google Scholar
  37. 37.
    Huntingford, F.A.: Animal Conflict. Springer, Berlin (2013)Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringAnhui University of TechnologyMa’anshanChina
  2. 2.School of Management Science and EngineeringAnhui University of TechnologyMa’anshanChina
  3. 3.Science and Math ClusterSingapore University of Technology and DesignSingaporeSingapore
  4. 4.Institute of Theoretical and Applied InformaticsPolish Academy of SciencesGliwicePoland

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