The networked cooperative dynamics of adjusting signal strength based on information quantity

We present a computational model which mainly concentrates on the effect of adjusting the signal strength of game from an agent to its a neighbor by considering the information quantity in individual's database to study the evolutionary prisoner's dilemma game on directed-weighted square lattices. In this model, each agent considers the current and historical payoff both obtained from the same neighbor, and regulates the strength of sending signal according to these information. More specifically, for paired agents x and y in network, x will increase the signal strength from it to y in case of it obtains current income higher than the historical payoff; conversely, x will reduce the signal strength because its current income less than the historical one. The simulation results show that this evolutionary rule not only can help cooperators get out of danger of extinction, but also can hugely boost the cooperation in population. Interestingly, for a fixed cost–benefit ratio r (), there exists the minimal information quantity, resulting in the higher cooperation level (even can reach the status of full cooperation) due to the positive feedback effect in system. Besides, by a modified pair, approximation method qualitatively verifies the role of adjusting signal strength according to the information quantity on cooperation. We also explore the reason of emergence and persistence of cooperation by a few representative snapshots in system. Our results may enhance the understanding of evolutionary dynamics with stochastic interaction in graph-structured populations.