Evolutionary dynamics of complex multiple games

Abstract

Evolutionary game theory has been successful in describing phenomena from bacterial population dynamics to the evolution of social behaviour. However, it has typically focused on a single game describing the interactions between individuals. Organisms are simultaneously involved in many intraspecies and interspecies interactions. Therefore, there is a need to move from single games to multiple games. However, these interactions in nature involve many players. Shifting from 2-player games to multiple multiplayer games yield richer dynamics closer to natural settings. Such a complete picture of multiple game dynamics (MGD), where multiple players are involved, was lacking. For multiple multiplayer games—where each game could have an arbitrary finite number of players and strategies, we provide a replicator equation for MGD having many players and strategies. We show that if the individual games involved have more than two strategies, then the combined dynamics cannot be understood by looking only at individual games. Expected dynamics from single games is no longer valid, and trajectories can possess different limiting behaviour. In the case of finite populations, we formulate and calculate an essential and useful stochastic property, fixation probability. Our results highlight that studying a set of interactions defined by a single game can be misleading if we do not take the broader setting of the interactions into account. Through our results and analysis, we thus discuss and advocate the development of evolutionary game(s) theory, which will help us disentangle the complexity of multiple interactions.

Fitness and community feedbacks: the two axes that drive long-term invasion impacts

“…Our work constitutes a promising avenue to refine predictions on the role of other large-scale properties of an interaction network, such as conectance, directedness, nestedness or modularity , Barbier et al 2018), on invasion vulnerability. While we interpreted our results in the context of ecological dynamics, this method is readily extended to other biological dynamics, and could shed light on complex evolutionary dynamics in the presence of phenotypic diversity (Venkateswaran & Gokhale 2019, Kotil & Vetsigian 2018.…”

Consequences of combining sex-specific life-history traits

“…Note that for certain ornamentation and PI values, the adult sex ratios are equal. As shown by previous studies on multiple interactions between traits (Venkateswaran and Gokhale, 2019), even in the case where the cost of ornamentation is equal to zero in the mating competition game, the mere presence of that game will deviate the frequency of Sex 2 from a scenario where there is no ornamentation game.…”

“…We amalgamated approaches from standard population genetics and eco-evolutionary processes (Free-man and Herron, 2007; Otto and Day, 2007; Venkateswaran and Gokhale, 2019) (within and between populations) to investigate the interaction dynamics of multiple life-history traits (with sex-specific differences). We first developed a robust method (illustrated in Figure 1 to study the lifetime reproductive success (LRS) that arises from immune response, mating competition through ornaments and parental investment.…”

Knowledge Input or Not in the Dynamic Cooperation Behavior of Top Management Teams? From the Perspective of Knowledge Flow

“…This moment, as a dynamic method to explore the evolution law of population, evolutionary game theory can effectively simulate the development law of heterogeneous population. Evolutionary game theory has a long history since it is put forward and it has been applied to explain many dynamic evolution phenomenon, such as human cooperative behavior [33], the evolutionary choice of nature [34], and social games [35] etc. Specifically speaking, in terms of human behavior, Marialisa et al [36] used social network and game theory to investigate the evolution of human cooperation based on the perspective of the homophily, and they explained the phenomenon of the "birds of a feather flock together"; Liu and Chen [37] explored the prosocial exclusion and the antisocial exclusion in the human cooperation; Dong et al [38] considered that self-interest, the behavior of others, the reaction to rewards, and the reaction to punishment can effect the cooperation of human behavior; furthermore, some scholars have studied human cooperative behavior from reputation mechanism [39] and moral hazard [40,41].…”