Good predictors for the fixation probability on complex networks of multi-player games using territorial interactions

IF 3.1 3区 环境科学与生态学 Q2 ECOLOGY
Pedro H.T. Schimit , Fábio H. Pereira , Mark Broom
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引用次数: 3

Abstract

In 2012 Broom and Rychtar developed a new framework to consider the evolution of a population over a non-homogeneous underlying structure, where fitness depends upon multiplayer interactions amongst the individuals within the population played in groups of various sizes (including one). This included the independent model, and as a special case the territorial raider model, which has been considered in a series of subsequent papers. Here individuals are based upon the vertex of a graph but move to interact with their neighbours, sometimes meeting in large groups. The most important single property of such populations is the fixation probability, the probability of a single mutant completely replacing the existing population. In a recent paper we considered the fixation probability for the Birth Death Birth (BDB) dynamics for three games, a Public Goods game, the Hawk–Dove game and for fixed fitnesses for a large number of randomly generated graphs, in particular seeing if important underlying graph properties could be used as predictors. We found two good predictors, temperature and mean group size, but some interesting and unusual features for one type of graph, Barabasi–Albert graphs. In this paper we use a regression analysis to investigate (the usual) three alternative evolutionary dynamics (BDD, DBB, DBD) in addition to the original BDB. In particular, we find that the dynamics split into two pairs, BDB/DBD and BDD/DBB, each of which give essentially the same results and found a good fit to the data using a quadratic regression involving the above two variables. Further we find that temperature is the most important predictor for the Hawk–Dove game, whilst for the Public Goods game the group size also plays a key role, and is more important than the temperature for the BDD/DBB dynamics.

利用区域相互作用对多人博弈复杂网络的注视概率进行良好预测
在2012年,Broom和Rychtar开发了一个新的框架来考虑非同质底层结构的种群进化,其中适应度取决于种群中不同规模(包括一个)的个体之间的多人互动。这包括独立模型,以及作为特例的领土掠夺者模型,这在随后的一系列论文中得到了考虑。在这里,个体是基于一个图的顶点,但会与他们的邻居互动,有时会在大群体中会面。这类种群最重要的单一特性是固定概率,即单个突变体完全取代现有种群的概率。在最近的一篇论文中,我们考虑了三个游戏的出生-死亡-出生(BDB)动态的固定概率,一个是公共产品游戏,一个是鹰-鸽子游戏,另一个是大量随机生成图形的固定适应度,特别是看看重要的潜在图形属性是否可以用作预测因子。我们发现了两个很好的预测指标,温度和平均群体规模,但对于一类图,Barabasi-Albert图,有一些有趣而不寻常的特征。在本文中,我们使用回归分析来研究(通常)除了原始BDB之外的三种替代进化动力学(BDD, DBB, DBD)。特别是,我们发现动力学分为两对,BDB/DBD和BDD/DBB,每一对都给出了本质上相同的结果,并且使用涉及上述两个变量的二次回归发现数据很适合。我们进一步发现,温度是鹰鸽游戏中最重要的预测因素,而在公共物品游戏中,群体规模也起着关键作用,而且对BDD/DBB动态的影响比温度更重要。
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来源期刊
Ecological Complexity
Ecological Complexity 环境科学-生态学
CiteScore
7.10
自引率
0.00%
发文量
24
审稿时长
3 months
期刊介绍: Ecological Complexity is an international journal devoted to the publication of high quality, peer-reviewed articles on all aspects of biocomplexity in the environment, theoretical ecology, and special issues on topics of current interest. The scope of the journal is wide and interdisciplinary with an integrated and quantitative approach. The journal particularly encourages submission of papers that integrate natural and social processes at appropriately broad spatio-temporal scales. Ecological Complexity will publish research into the following areas: • All aspects of biocomplexity in the environment and theoretical ecology • Ecosystems and biospheres as complex adaptive systems • Self-organization of spatially extended ecosystems • Emergent properties and structures of complex ecosystems • Ecological pattern formation in space and time • The role of biophysical constraints and evolutionary attractors on species assemblages • Ecological scaling (scale invariance, scale covariance and across scale dynamics), allometry, and hierarchy theory • Ecological topology and networks • Studies towards an ecology of complex systems • Complex systems approaches for the study of dynamic human-environment interactions • Using knowledge of nonlinear phenomena to better guide policy development for adaptation strategies and mitigation to environmental change • New tools and methods for studying ecological complexity
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