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Population Games with Sub-Strategies and Evolutionary Nash Equilibrium Learning

Matthew S. Hankins et al.

Dynamic Games and Applications2026https://doi.org/10.1007/s13235-025-00689-5article
AJG 1ABDC B
Weight
0.37

Abstract

We establish a modified notion of Nash equilibrium learning—convergence of the population state to the Nash equilibria set—in a generalization of the standard population games and evolutionary dynamics framework using system-theoretic passivity methods. In this setting, we allow each strategy to involve a sequence of sub-tasks that must be completed before strategy revision so long as the durations of the sub-tasks can be modeled with Erlang or exponential distributions. Furthermore, several canonical classes of natural learning rules are established and useful properties are derived.

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https://doi.org/https://doi.org/10.1007/s13235-025-00689-5

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@article{matthew2026,
  title        = {{Population Games with Sub-Strategies and Evolutionary Nash Equilibrium Learning}},
  author       = {Matthew S. Hankins et al.},
  journal      = {Dynamic Games and Applications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s13235-025-00689-5},
}

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Population Games with Sub-Strategies and Evolutionary Nash Equilibrium Learning

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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