This paper establishes existence, uniqueness, and a finite-particle mean-field error rate of order N^{-γ} for a multi-species consensus-based algorithm for multiplayer Nash games.
Evolutionary Multiplayer Games
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abstract
Evolutionary game theory has become one of the most diverse and far reaching theories in biology. Applications of this theory range from cell dynamics to social evolution. However, many applications make it clear that inherent non-linearities of natural systems need to be taken into account. One way of introducing such non-linearities into evolutionary games is by the inclusion of multiple players. An example is of social dilemmas, where group benefits could e.g.\ increase less than linear with the number of cooperators. Such multiplayer games can be introduced in all the fields where evolutionary game theory is already well established. However, the inclusion of non-linearities can help to advance the analysis of systems which are known to be complex, e.g. in the case of non-Mendelian inheritance. We review the diachronic theory and applications of multiplayer evolutionary games and present the current state of the field. Our aim is a summary of the theoretical results from well-mixed populations in infinite as well as finite populations. We also discuss examples from three fields where the theory has been successfully applied, ecology, social sciences and population genetics. In closing, we probe certain future directions which can be explored using the complexity of multiplayer games while preserving the promise of simplicity of evolutionary games.
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Well-posedness and mean-field limit estimate of a consensus-based algorithm for multiplayer games
This paper establishes existence, uniqueness, and a finite-particle mean-field error rate of order N^{-γ} for a multi-species consensus-based algorithm for multiplayer Nash games.