The public good game on graphs: Can the pro-social behavior persist?

Abstract

This communication proposes new alternatives to study the pro-social behavior in artificial society of players in the context of public good game via Monte Carlo simulations. Here, the pro-social aspect is governed by a binary variable called motivation that incites the player to invest in the public good. This variable is updated according to the benefit achieved by the player, which is quantified by a return function. In this manuscript we propose a new return function in comparison with other one explored by the same author in previous contributions. We analyze the game considering different networks studying noise effects on the density of motivation. Estimates of pro-sociability survival probability were obtained as function of randomness (;;) in small world networks. We also introduced a new dynamics based on Gibbs Sampling for which the motivation of a player (now a q-state variable) is chosen according to the return of its neighbors, discarding the negative returns.