Fig. 1: Evolutionary dynamics of behavioral motivation.

a The population structure is described by a network, and each individual (node) in the population has a behavioral motivation A or B (circle) and adopts action cooperation (C) or defection (D) (square). The figure illustrates two examples of behavioral motivations: one with need α_A and motivation intensity λ_A, and the other with need α_B and motivation intensity λ_B. With the shown behavioral motivation A, the individual is more likely to cooperate when his payoff exceeds the need, i.e., u > α_A. Conversely, with the shown behavioral motivation B, the individual tends to defect when he fulfills his needs, i.e., u > α_B. b In every round t, every individual adopts cooperation or defection to play games with each neighbor and obtain an edge-weighted average payoff u_i(t). Here we consider a network where all edge weights are set to 1. c An individual (marked by “?”) is selected uniformly at random to update his action based on his own behavioral motivation, namely, to cooperate next round with the cooperating probability and to defect otherwise. d Game playing and action updates repeat in the next round, t + 1. e After T rounds of interactions, individuals obtain an average payoff, \({\bar{u}}_{i}={\sum }_{t=1}^{T}{u}_{i}(t)/T\). f An individual (marked by “?”) is selected uniformly at random to update his behavioral motivation, and all neighboring individuals, indicated by black circles, compete to be imitated by the focal individual, with probability proportional to their average payoff. g After the behavioral motivation updating, game playing, and action updates restart from round 1.