Cooperation Could Evolve in Complex Networks when Activated Conditionally on Network Characteristics

The investigation of how cooperation is achieved on graphs in the field of spatial game or network reciprocity has received proliferating attention in the biological and sociological literature. In line of the research, this paper provides an new account of how cooperation could evolve in complex networks when actors use information of network characteristics to strategize whether to cooperate or not. Different from past work that focuses exclusively on the evolution of unconditional cooperation, we are proposing new strategies that are choosy in whom to cooperate with, conditional on the structural attributes of the nodes occupied by actors. In a series of evolutionary tournaments conducted by computer simulation, the model shows that a pair of simple strategies-cooperating respectively with higher and lower nodal-attribute neighbors-can be advantageous in adaptive fitness when competing against unconditional cooperation and defection. In particular, these strategies of conditional cooperation work well in random graphs-a network known for being unfavorable to the selection of cooperation. This paper contributes to the literature by showing how network characteristics can serve as a mechanism to sustain cooperation in some hostile network environments where unconditional cooperation is unable to evolve. The cognitive foundations of the mechanism and its implications are discussed.