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Good predictors for the fixation probability on complex networks of multi-player games using territorial interactions
Institution:1. Institute of Aquatic Ecology, Centre for Ecological Research, Budapest, Hungary;2. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary;3. Democracy Institute, Central European University, Budapest, Hungary;4. Stazione Zoologica Anton Dohrn, Napoli, Italy;1. Department of Mathematics, Visva-Bharati Santiniketan 731235, India;2. Agricultural and Ecological Research Unit, Indian Statistical Institute 203 B. T. Road, Kolkata 700108, India;1. Institute of Plant and Animal Ecology, Russia;2. Institute of Mathematics and Mechanics Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
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.
Keywords:Complex networks  Evolutionary graph theory  Fixation probability  Game theory  Territory
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