Kroumi Dhaker, Lessard Sabin
Département de mathématiques et de statistique, Université de Montréal, Montréal, Québec H3C 3J7, Canada.
Département de mathématiques et de statistique, Université de Montréal, Montréal, Québec H3C 3J7, Canada.
Theor Popul Biol. 2015 Jun;102:60-75. doi: 10.1016/j.tpb.2015.03.007. Epub 2015 Apr 3.
The emergence of cooperation in populations of selfish individuals is a fascinating topic that has inspired much theoretical work. An important model to study cooperation is the phenotypic model, where individuals are characterized by phenotypic properties that are visible to others. The phenotype of an individual can be represented for instance by a vector x = (x1,…,xn), where x1,…,xn are integers. The population can be well mixed in the sense that everyone is equally likely to interact with everyone else, but the behavioral strategies of the individuals can depend on their distance in the phenotype space. A cooperator can choose to help other individuals exhibiting the same phenotype and defects otherwise. Cooperation is said to be favored by selection if it is more abundant than defection in the stationary state. This means that the average frequency of cooperators in the stationary state strictly exceeds 1/2. Antal et al. (2009c) found conditions that ensure that cooperation is more abundant than defection in a one-dimensional (i.e. n = 1) and an infinite-dimensional (i.e. n = ∞) phenotype space in the case of the Prisoner's Dilemma under weak selection. However, reality lies between these two limit cases. In this paper, we derive the corresponding condition in the case of a phenotype space of any finite dimension. This is done by applying a perturbation method to study a mutation-selection equilibrium under weak selection. This condition is obtained in the limit of a large population size by using the ancestral process. The best scenario for cooperation to be more likely to evolve is found to be a high population-scaled phenotype mutation rate, a low population-scaled strategy mutation rate and a high phenotype space dimension. The biological intuition is that a high population-scaled phenotype mutation rate reduces the quantity of interactions between cooperators and defectors, while a high population-scaled strategy mutation rate introduces newly mutated defectors that invade groups of cooperators. Finally it is easier for cooperation to evolve in a phenotype space of higher dimension because it becomes more difficult for a defector to migrate to a group of cooperators. The difference is significant from n = 1 to n = 2 and from n = 2 to n = 3, but becomes small as soon as n ≥ 3.
自私个体群体中合作行为的出现是一个引人入胜的话题,激发了大量的理论研究工作。研究合作的一个重要模型是表型模型,其中个体由其他个体可见的表型属性来表征。例如,个体的表型可以用向量(x = (x1,…,xn))表示,其中(x1,…,xn)为整数。在群体充分混合的情况下,每个人与其他任何人互动的可能性相等,但个体的行为策略可能取决于它们在表型空间中的距离。合作者可以选择帮助表现出相同表型的其他个体,否则就选择背叛。如果在稳态中合作比背叛更普遍,那么就可以说选择有利于合作。这意味着在稳态中合作者的平均频率严格超过(1/2)。安塔尔等人(2009c)发现,在弱选择下的囚徒困境中,在一维(即(n = 1))和无限维(即(n = ∞))表型空间中,存在确保合作比背叛更普遍的条件。然而,现实介于这两种极限情况之间。在本文中,我们推导了任意有限维表型空间情况下的相应条件。这是通过应用微扰方法来研究弱选择下的突变 - 选择平衡来完成的。这个条件是在大种群规模的极限情况下通过使用祖先过程得到的。发现合作更有可能进化的最佳情况是高种群规模表型突变率、低种群规模策略突变率和高表型空间维度。生物学直觉是,高种群规模表型突变率减少了合作者与背叛者之间的互动数量,而高种群规模策略突变率引入了新突变的背叛者,这些背叛者会侵入合作者群体。最后,在更高维的表型空间中合作更容易进化,因为背叛者迁移到合作者群体变得更加困难。从(n = 1)到(n = 2)以及从(n = 2)到(n = 3)时差异显著,但一旦(n ≥ 3)差异就会变小。