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Evolutionary stability for large populations.

Daniel B Neill1

  • 1Department of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3891, USA. neill@cs.cmu.edu

Journal of Theoretical Biology
|March 17, 2004
PubMed
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We introduce the large population evolutionary stability strategy (ESS), a new model for finite yet large populations. This ESS differs from infinite population models, offering new criteria for evolutionary game theory.

Area of Science:

  • Evolutionary biology
  • Game theory
  • Population genetics

Background:

  • John Maynard Smith's evolutionary stability criteria are foundational for understanding strategy evolution in populations.
  • Existing models often assume infinite or small finite populations, limiting applicability to large, realistically finite populations.

Purpose of the Study:

  • To revise evolutionary stability criteria for large, finite populations of unknown size.
  • To introduce the concept of the large population ESS and differentiate it from existing models.
  • To provide a practical set of criteria for identifying large population ESS.

Main Methods:

  • Building upon Schaffer's finite population model.
  • Defining the large population ESS as a strategy resistant to invasion by any finite number of mutants in sufficiently large populations.

Related Experiment Videos

  • Developing a set of two criteria analogous to Maynard Smith's original criteria.

    • Main Results:

    • The large population ESS is distinct from the infinite population ESS, with examples provided where one exists and the other does not.
    • A novel set of two criteria for identifying the large population ESS has been established.
    • These criteria are similar but not identical to Maynard Smith's original criteria for infinite populations.

    Conclusions:

    • The large population ESS offers a more realistic framework for evolutionary stability in many biological contexts.
    • The new criteria provide a simplified method for analyzing evolutionary stability in large populations.
    • This work bridges the gap between infinite and finite population models in evolutionary game theory.