Evaluating the structure-coefficient theorem of evolutionary game theory

Author:

McAvoy Alex123ORCID,Wakeley John1

Affiliation:

1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138

2. Department of Mathematics, University of Pennsylvania, Philadelphia, PA 19104

3. Center for Mathematical Biology, University of Pennsylvania, Philadelphia, PA 19104

Abstract

In order to accommodate the empirical fact that population structures are rarely simple, modern studies of evolutionary dynamics allow for complicated and highly heterogeneous spatial structures. As a result, one of the most difficult obstacles lies in making analytical deductions, either qualitative or quantitative, about the long-term outcomes of evolution. The “structure-coefficient” theorem is a well-known approach to this problem for mutation–selection processes under weak selection, but a general method of evaluating the terms it comprises is lacking. Here, we provide such a method for populations of fixed (but arbitrary) size and structure, using easily interpretable demographic measures. This method encompasses a large family of evolutionary update mechanisms and extends the theorem to allow for asymmetric contests to provide a better understanding of the mutation–selection balance under more realistic circumstances. We apply the method to study social goods produced and distributed among individuals in spatially heterogeneous populations, where asymmetric interactions emerge naturally and the outcome of selection varies dramatically, depending on the nature of the social good, the spatial topology, and the frequency with which mutations arise.

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A novel optimization approach based on unstructured evolutionary game theory;Mathematics and Computers in Simulation;2024-05

2. Symmetry in models of natural selection;Journal of The Royal Society Interface;2023-11

3. Strategy evolution on dynamic networks;Nature Computational Science;2023-09-11

4. Integrating eco‐evolutionary dynamics into matrix population models for structured populations: Discrete and continuous frameworks;Methods in Ecology and Evolution;2023-04-24

5. Evaluating the structure-coefficient theorem of evolutionary game theory;Proceedings of the National Academy of Sciences;2022-07-05

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