  Various social contexts ranging from public goods provision to information
collection can be depicted as games of strategic interactions, where a player's
well-being depends on her own action as well as on the actions taken by her
neighbors. Whereas much attention has been devoted to the identification and
characterization of Bayes-Nash equilibria of such games, in this work we look
at strategic interactions from an evolutionary perspective. Starting from a
recent mean-field analysis of the evolutionary dynamics in these games, here we
present results of numerical simulations designed to find out whether Nash
equilibria are accessible by adaptation of players' strategies, and in general
to find the attractors of the evolution. Simulations allow us to go beyond a
global characterization of the cooperativeness of the equilibria and probe into
the individual behavior. We find that when players imitate each other, the
evolution does not reach Nash equilibria and, worse, leads to very unfavorable
states in terms of welfare. On the contrary, when players update their behavior
rationally, they self-organize into a rich variety of Nash equilibria, where
individual behavior and payoffs are shaped by the nature of the game, the
structure of the social network and the players' position within the topology.
Our results allow us to assess the validity of the mean-field approaches and
also show qualitative agreement with theoretical predictions for equilibria in
the context of one-shot games under incomplete information.
