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Parrondo effect: Exploring the nature-inspired framework on periodic functions

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  • Jia, Shuyi
  • Lai, Joel Weijia
  • Koh, Jin Ming
  • Xie, Neng Gang
  • Cheong, Kang Hao

Abstract

Recently, a population model has been analyzed using the framework of Parrondo’s paradox to explain how behavior-switching organisms can achieve long-term survival, despite each behavior individually resulting in extinction. By incorporating environmental noise, the model has been shown to be robust to natural variations. Apart from the role of noise, the apparent ubiquity of quasi-periodicity in nature also motivates a more comprehensive understanding of periodically-coupled models of Parrondo’s paradox. Such models can enable a wider range of applications of the Parrondo effect to biological and social systems. In this paper, we modify the canonical Parrondo’s games to show how the Parrondo effect can still be achieved despite the increased complexity in periodically-noisy environments. Our results suggest the extension of Parrondo’s paradox to real-world phenomena strongly subjected to periodic variations, such as ecological systems experiencing seasonal changes, disease in wildlife and humans, or resource management.

Suggested Citation

  • Jia, Shuyi & Lai, Joel Weijia & Koh, Jin Ming & Xie, Neng Gang & Cheong, Kang Hao, 2020. "Parrondo effect: Exploring the nature-inspired framework on periodic functions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    • Handle: RePEc:eee:phsmap:v:556:y:2020:i:c:s0378437120303538
    DOI: 10.1016/j.physa.2020.124714
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    1. Tom Baum, 1999. "Seasonality in Tourism: Understanding the Challenges," Tourism Economics, , vol. 5(1), pages 5-8, March.
    2. Soo, Wayne Wah Ming & Cheong, Kang Hao, 2014. "Occurrence of complementary processes in Parrondo’s paradox," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 412(C), pages 180-185.
    3. Cheong, Kang Hao & Soo, Wayne Wah Ming, 2013. "Construction of novel stochastic matrices for analysis of Parrondo’s paradox," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 4727-4738.
    4. Soo, Wayne Wah Ming & Cheong, Kang Hao, 2013. "Parrondo’s paradox and complementary Parrondo processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 17-26.
    5. Cánovas, Jose S. & Guillermo, María Muñoz, 2016. "Computing the topological entropy of continuous maps with at most three different kneading sequences with applications to Parrondo’s paradox," Chaos, Solitons & Fractals, Elsevier, vol. 83(C), pages 1-17.
    6. Ye, Ye & Hang, Xiao Rong & Koh, Jin Ming & Miszczak, Jarosław Adam & Cheong, Kang Hao & Xie, Neng-gang, 2020. "Passive network evolution promotes group welfare in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
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