Evolution of cooperation under a generalized death-birth process
arXiv:2301.10205 · doi:10.1103/PhysRevE.107.024303
Abstract
According to the evolutionary death-birth protocol, a player is chosen randomly to die and neighbors compete for the available position proportional to their fitness. Hence, the status of the focal player is completely ignored and has no impact on the strategy update. In this work, we revisit and generalize this rule by introducing a weight factor to compare the payoff values of the focal and invading neighbors. By means of evolutionary graph theory, we analyze the model on joint transitive graphs to explore the possible consequences of the presence of a weight factor. We find that focal weight always hinders cooperation under weak selection strength. Surprisingly, the results show a non-trivial tipping point of the weight factor where the threshold of cooperation success shifts from positive to negative infinity. Once focal weight exceeds this tipping point, cooperation becomes unreachable. Our theoretical predictions are confirmed by Monte Carlo simulations on a square lattice of different sizes. We also verify the robustness of the conclusions to arbitrary two-player prisoner's dilemmas, to dispersal graphs with arbitrary edge weights, and to interaction and dispersal graphs overlapping arbitrarily.
8 two-column pages, 2 figures, accepted for publication in Physical Review E
References in corpus (9)
- Evolutionary games on graphs
- Statistical physics of human cooperation
- Evolutionary dynamics of group interactions on structured populations: A review
- Cooperation enhanced by inhomogeneous activity of teaching for evolutionary Prisoner's Dilemma games
- Impact of aging on the evolution of cooperation in the spatial prisoner's dilemma game
- Gradual learning supports cooperation in spatial prisoner's dilemma game
- Evolution of Cooperation in the Presence of Higher-Order Interactions: from Networks to Hypergraphs
- Strategy dependent learning activity in cyclic dominant systems
- Facilitators on networks reveal the optimal interplay between information exchange and reciprocity