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Prisoner's dilemma in an RNA virus

Author

Listed:
  • Paul E. Turner

    (University of Maryland
    Universidad de València)

  • Lin Chao

    (University of Maryland
    University of California San Diego)

Abstract

The evolution of competitive interactions among viruses1 was studied in the RNA phage φ6 at high and low multiplicities of infection (that is, at high and low ratios of infecting phage to host cells). At high multiplicities, many phage infect and reproduce in the same host cell, whereas at low multiplicities the viruses reproduce mainly as clones. An unexpected result of this study1 was that phage grown at high rates of co-infection increased in fitness initially, but then evolved lowered fitness. Here we show that the fitness of the high-multiplicity phage relative to their ancestors generates a pay-off matrix conforming to the prisoner's dilemma strategy of game theory2,3. In this strategy, defection (selfishness) evolves, despite the greater fitness pay-off that would result if all players were to cooperate. Viral cooperation and defection can be defined as, respectively, the manufacturing and sequestering of diffusible (shared) intracellular products. Because the low-multiplicity phage did not evolve lowered fitness, we attribute the evolution of selfishness to the lack of clonal structure and the mixing of unrelated genotypes at high multiplicity4,5,6.

Suggested Citation

  • Paul E. Turner & Lin Chao, 1999. "Prisoner's dilemma in an RNA virus," Nature, Nature, vol. 398(6726), pages 441-443, April.
    • Handle: RePEc:nat:nature:v:398:y:1999:i:6726:d:10.1038_18913
    DOI: 10.1038/18913
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    Cited by:

    1. Diana Richards, 2001. "Reciprocity and Shared Knowledge Structures in the Prisoner's Dilemma Game," Journal of Conflict Resolution, Peace Science Society (International), vol. 45(5), pages 621-635, October.
    2. Roberto Galbiati & Emeric Henry & Nicolas Jacquemet, 2018. "Dynamic effects of enforcement on cooperation," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(49), pages 12425-12428, December.
    3. Yunsheng Deng & Jihui Zhang, 2022. "The choice-decision based on memory and payoff favors cooperation in stag hunt game on interdependent networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(2), pages 1-13, February.
    4. Zhang, Liming & Li, Haihong & Dai, Qionglin & Yang, Junzhong, 2022. "Adaptive persistence based on environment comparison enhances cooperation in evolutionary games," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    5. Jürgen Fleiß & Ulrike Leopold‐Wildburger, 2014. "Once Nice, Always Nice? Results on Factors Influencing Nice Behavior from an Iterated Prisoner's Dilemma Experiment," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(2), pages 327-334, March.
    6. Roberto Galbiati & Emeric Henry & Nicolas Jacquemet, 2018. "Dynamic effects of enforcement on cooperation," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(49), pages 12425-12428, December.
    7. Asher Leeks & Stuart A. West & Melanie Ghoul, 2021. "The evolution of cheating in viruses," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    8. Zhang, Lan & Huang, Changwei, 2023. "Preferential selection to promote cooperation on degree–degree correlation networks in spatial snowdrift games," Applied Mathematics and Computation, Elsevier, vol. 454(C).

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