title: Approximating evolutionary dynamics on networks using a Neighbourhood Configuration model.
abstract: Evolutionary dynamics have been traditionally studied on homogeneously mixed and infinitely large populations. However, real populations are finite and characterised by complex interactions among individuals. Recent studies have shown that the outcome of the evolutionary process might be significantly affected by the population structure. Although an analytic investigation of the process is possible when the contact structure of the population has a simple form, this is usually infeasible on complex structures and the use of various assumptions and approximations is necessary. In this paper, we adopt an approximation method which has been recently used for the modelling of infectious disease transmission to model evolutionary game dynamics on complex networks. Comparisons of the predictions of the model constructed with the results of computer simulations reveal the effectiveness of the method and the improved accuracy that it provides when, for example, compared to well-known pair approximation methods. This modelling framework offers a flexible way to carry out a systematic analysis of evolutionary game dynamics on graphs and to establish the link between network topology and potential system behaviours. As an example, we investigate how the Hawk and Dove strategies in a Hawk-Dove game spread in a population represented by a random regular graph, a random graph and a scale-free network, and we examine the features of the graph which affect the evolution of the population in this particular game.
authors: Centre for Mathematical Science, City University London, Northampton Square, London EC1V 0HB, UK. Electronic address: Christoforos.Hadjichrysanthou.1@city.ac.uk.
authors: Christoforos
authors: C
authors: Hadjichrysanthou
authors: Centre for Mathematical Science, City University London, Northampton Square, London EC1V 0HB, UK.
authors: Mark
authors: M
authors: Broom
authors: School of Mathematical and Physical Sciences, Department of Mathematics, University of Sussex, Falmer, Brighton BN1 9RH, UK.
authors: Istvan Z
authors: IZ
authors: Kiss
mesh_terms: D005075:Biological Evolution; D005716:Game Theory; D008954:Models, Biological