• COOPERATION
• EVOLUTIONARY DYNAMICS
• COMPLEX NETWORKS
• GAME THEORY
• STAG HUNT
• PREFERENTIAL ATTACHMENT
• SOCIAL INTERACTIONS

In this work we present and discuss the results of a computational study of the Stag Hunt game on a complex network, built using an evolutionary preferential attachment mechanism.
In spite of its relevance regarding the origin of complex networks, the interplay between form and function and its role during network formation remains largely unexplored. While recent studies introduce dynamics by considering rewiring processes of a pre-existent network, our model incorporates an intrinsic feedback between dynamic and topology, as the growth and formation of the network is ruled by the dynamical states of the elements of the system. The capacity of an element to attract new links, depends on its performance in the game dynamics.
In our model the relation between the emergence of cooperation and the topology of the resulting networks is highly non-trivial, showing a transition in the space of the main dynamical
parameters from homogeneous to scale free networks. The variation of the main parameters involved and the parametrization of the game dynamics strongly affect the model behavior and the main topological properties of the resulting network.
The preferential attachment mechanism has proved to be a powerful and flexible tool for an evolutionary origin of scale-free networks, and it may help understanding similar feedback problems in the dynamics of complex networks by appropriately choosing the game describing the interaction of nodes.