Cooperation under institutional incentives with perfect and imperfect observation

Understanding the evolution of cooperation is a challenging topic in various realms. Increasing attention has been paid to exploring the effects of incentives on the emergence of cooperation. Most of the extant studies have implicitly assumed that the cost of incentives is fixed and independent of the individual's payoff. In human societies, taxation is one important source of funds for supporting the government of individuals and nearly involves everyone. Inspired by this phenomenon, we institute an analytic model where the incentives are established based on the tax regime. Individuals play the prisoner's dilemma game and the ones whose payoffs are greater than zero have to contribute a part as taxes to establish an institution to monitor individuals' behaviors and execute incentives. We investigate the emergence of cooperation under the institutional incentives and make a comparative analysis between reward and punishment subject to the perfect and imperfect environment.     

Interaction stochasticity may hinder cooperation in the spatial public goods game

We propose an analytic model to explore the effect of interaction stochasticity on the evolution of cooperation in spatial public goods game. The results show that whether cooperation can dominate in populations crucially depends on the player's probability of opting-out. Stochastic opting-out enhances cooperation as long as the probability of opting-out is less than a threshold depending on the graph's degree. Otherwise the promoting effect of spatial structures on cooperation is hindered even neutralized by stochastic opting-out. Moreover, there exists an intermediate optimal probability with which the advantage of cooperation over defection is maximized in the evolutionary race. Interestingly, the optimal probability is related to the percolation threshold of the underlying graph. Our findings illustrate that spatial structures may not facilitate cooperation when stochastic opting-out is allowed, and provide a link between physics and social sciences.     

A Simple Model of Stability in Critical Mass Dynamics

Collective behaviors often spread via the self-reinforcing dynamics of critical mass. In collective behaviors with strongly self-reinforcing dynamics, incentives to participate increase with the number of participants, such that incentives are highest when the full population has adopted the behavior. By contrast, when collective behaviors have weakly self-reinforcing dynamics, incentives to participate “peak out” early, leaving a residual fraction of non-participants. In systems of collective action, this residual fraction constitutes free riders, who enjoy the collective good without contributing anything themselves. This “free rider problem” has given rise to a research tradition in collective action that shows how free riding can be eliminated by increasing the incentives for participation, and thereby making cooperation strongly self-reinforcing. However, we show that when the incentives to participate have weakly self-reinforcing dynamics, which allow free riders, collective behaviors will have significantly greater long term stability than when the incentives have strongly self-reinforcing dynamics leading to full participation.     

Cautious strategy update promotes cooperation in spatial prisoner’s dilemma game

In the realistic world, individual cautiousness can be often involved or observed when a rational individual makes a decision. However, it remains unclear how such individual cautiousness influences the evolution of cooperative behavior. To this end, we consider a Fermi strategy updating rule, where each individual is assigned a cautiousness index that controls its learning activity, and then study the evolution of cooperation in the spatial prisoner’s dilemma game. Interestingly, it is found that cooperation can be significantly promoted when individuals’ cautiousness is considered. In particular, there exists an optimal range of the degree of cautiousness resulting in the highest cooperation level. The remarkable promotion of cooperation, as well as the emerging phase transition is explained by configurational analysis. The sensitivity of cooperation to initial states with different fractions of cooperators is also discussed. The result illustrates that high densities of cooperators can be established at small initial fractions of cooperators. The detailed mechanism for such phenomenon is explained by analyzing the typical initial configurations.     

Diversity of contribution promotes cooperation in public goods games

In most previous studies of public goods game, individuals conventionally donate their contributions equally to the games they participate in. We develop an extended public goods game model, in which individuals distribute their contributions based on the groups’ qualities. Namely, the individuals are allowed to increase their investment to the superior groups at the expense of the nasty ones. The quality of a group is positively correlated with its cooperation level. In numerical simulations, synchronized stochastic strategy updating rule based on pairwise comparison for a fixed noise level is adopted. The results show that the high-quality group preference mechanism can greatly improve cooperation, compared with conventional models. Besides, the system with stronger preference toward high-quality groups performs better. Investigation of wealth distribution at equilibrium reveals that cooperators’ wealth appreciates with the increase of preference degree when cooperators take up the same fraction of the population.     

Promoting cooperation by local contribution under stochastic win-stay-lose-shift mechanism

We introduce a stochastic win-stay-lose-shift (WSLS) mechanism into evolutionary Prisoner’s Dilemma on small-world networks. At each time step, after playing with all its immediate neighbors, each individual gets a score to evaluate its performance in the game. The score is a linear combination of an individual’s total payoff (i.e., individual gain from the group) and local contribution to its neighbors (i.e., individual donation to the group). If one’s actual score is not larger than its desired score aspiration, it switches current strategy to the opposite one with the probability depending on the difference between the two scores. Under this stochastic WSLS regime, we assume that each focal individual gains its fixed score aspiration under the condition of full cooperation in its neighborhood, and find that cooperation is significantly enhanced under some certain parameters of the model by studying the evolution of cooperation. We also explore the influences of different values of learning rate and intensity of deterministic switch on the evolution of cooperation. Simulation results show that cooperation level monotonically increases with the relative weight of the local contribution to the score. For much low intensity of deterministic switch, cooperation is to a large extent independent of learning rate, and full cooperation can be reached when relative weight is not less than 0.5. Otherwise, cooperation level is affected by the value of learning rate. Besides, we find that the cooperation level is not sensitive to the topological parameters. To explain these simulation results, we provide corresponding analytical results based on mean-field approximation, and find out that simulation results are in close agreement with the analytical ones. Our work may be helpful in understanding the cooperative behavior in social systems based on this stochastic WSLS mechanism.     

Gradual learning and the evolution of cooperation in the spatial Continuous Prisoner’s Dilemma

The usual mechanism for modeling learning in spatially structured evolutionary games has to date been imitation of some successful neighbor. However, it seems natural that individuals hesitate to imitate their neighbor’s acts, specially if they can imply high costs. Here we study the effect of incorporating resistance to imitation on these models. Our framework is the spatial Continuous Prisoner’s Dilemma. For this evolutionary game, it has been reported that occasional errors in the imitation process can explain the emergence of cooperation from a non-cooperative initial state. In this work, we show that this only occurs for particular regimes of low costs of cooperation. Furthermore, we display how resistance gets greater the range of scenarios where cooperative individuals can invade selfish populations. In this context, where resistance to imitation can be interpreted as a general rule of gradual learning, our results show that the less that is learnt in a single step from a successful neighbors, the larger the degree of global cooperation finally attained. In general, the effect of step-by-step learning can be more efficient for the evolution of cooperation than a full blast one.     

Evolution of cooperation among mobile agents

We study the effects of mobility on the evolution of cooperation among mobile players, which imitate collective motion of biological flocks and interact with neighbors within a prescribed radius R. Adopting the the prisoner’s dilemma game and the snowdrift game as metaphors, we find that cooperation can be maintained and even enhanced for low velocities and small payoff parameters, when compared with the case that all agents do not move. But such enhancement of cooperation is largely determined by the value of R, and for modest values of R, there is an optimal value of velocity to induce the maximum cooperation level. Besides, we find that intermediate values of R or initial population densities are most favorable for cooperation, when the velocity is fixed. Depending on the payoff parameters, the system can reach an absorbing state of cooperation when the snowdrift game is played. Our findings may help understanding the relations between individual mobility and cooperative behavior in social systems.     

Simulation study on cooperation behaviors and crowd dynamics in pedestrian evacuation

Pedestrian evacuation is actually a process of behavioral evolution. Interaction behaviors between pedestrians affect not only the evolution of their cooperation strategy, but also their evacuation paths-scheduling and dynamics features. The existence of interaction behaviors and cooperation evolution is therefore critical for pedestrian evacuation. To address this issue, an extended cellular automaton(CA) evacuation model considering the effects of interaction behaviors and cooperation evolution is proposed here. The influence mechanism of the environment factor and interaction behaviors between neighbors on the decision-making of one pedestrian to path scheduling is focused. Average payoffs interacting with neighbors are used to represent the competitive ability of one pedestrian, aiming to solve the conflicts when more than one pedestrian competes for the same position based on a new method. Influences of interaction behaviors, the panic degree and the conflict cost on the evacuation dynamics and cooperation evolution of pedestrians are discussed. Simulation results of the room evacuation show that the interaction behaviors between pedestrians to a certain extent are beneficial to the evacuation efficiency and the formation of cooperation behaviors as well. The increase of conflict cost prolongs the evacuation time. Panic emotions of pedestrians are bad for cooperation behaviors of the crowd and have complex effects on evacuation time. A new self-organization effect is also presented.     

The evolution of cooperation on structured population

In recent studies, it has been shown that cooperation can been improved when the population of selfish players is located on a heterogenous network. However, the theoretical analysis for the enhancement of cooperation on a heterogenous network is still short. In this work, we first model a heterogenous network by three simple networks with increasing complexity on their network structures. Then according to a master equation we develop the replication equations for the evolution of cooperation on these networks. The explicit formulations for the cooperator frequency on these networks are deduced and the relationship between the cooperator frequency and the network heterogeneity is discussed.     

Other-regarding preference and the evolutionary prisoner’s dilemma on complex networks

Prevailing models of the evolutionary prisoner’s game on networks always assume that agents are pursuing their own profit maximization. But the results from experimental games show that many agents have other-regarding preference. In this paper, we study the emergence of cooperation from the prisoner’s dilemma game on complex networks while some agents exhibit other-regarding preference such as inequality aversion, envious and guilty emotions. Contrary to common ideas, the simulation results show that the existence of inequality aversion agents does not promote cooperation emergence on a BA (Barabási and Albert) scale-free network in most situations. If the defection attraction is big and agents exhibit strong preference for inequality aversion, the frequency of cooperators will be lower than in situations where no inequality aversion agents exist. In some cases, the existence of the inequality agents will even induce the frequency of cooperators to zero, a feature which is not observed in previous research on the prisoner’s dilemma game when the underlying interaction topology is a BA scale-free network. This means that if an agent cares about equality too much, it will be difficult for cooperation to emerge and the frequency of cooperators will be low on BA networks. The research on the effect of envy or guilty emotions on the emergence of cooperation in the prisoner’s dilemma game on BA networks obtains similar results, though some differences exist. However, simulation results on a WS (Watts and Strogatz) small-world network display another scenario. If agents care about the inequality of agents very much, the WS network favors cooperation emergence in the prisoners’ dilemma game when other-regarding agents exist. If the agent weight on other-regarding is lowered, the cooperation frequencies emerging on a WS network are not much different from those in situations without other-regarding agents, although the frequency of cooperators is lower than those of the situation without other-regarding preference agents sometimes. All the simulation results imply that inequality aversion and its variations can have important effects on cooperation emergence in the prisoner’s dilemma game, and different network topologies have different effects on cooperation emergence in the prisoner’s dilemma game played on complex networks.     

Diversity of reproduction rate supports cooperation in the prisoner's dilemma game on complex networks

In human societies the probability of strategy adoption from a given person may be affected by the personal features. Now we investigate how an artificially imposed restricted ability to reproduce, overruling ones fitness, affects an evolutionary process. For this purpose we employ the evolutionary prisoner's dilemma game on different complex graphs. Reproduction restrictions can have a facilitative effect on the evolution of cooperation that sets in irrespective of particularities of the interaction network. Indeed, an appropriate fraction of less fertile individuals may lead to full supremacy of cooperators where otherwise defection would be widespread. By studying cooperation levels within the group of individuals having full reproduction capabilities, we reveal that the recent mechanism for the promotion of cooperation is conceptually similar to the one reported previously for scale-free networks. Our results suggest that the diversity in the reproduction capability, related to inherently different attitudes of individuals, can enforce the emergence of cooperative behavior among selfish competitors.     

Elimination mechanism promotes cooperation in coevolutionary prisoner’s dilemma games

We propose an elimination mechanism in the study of the evolutionary prisoner’s dilemma games on evolving networks. It assumes that after each round of playing, players whose payoffs are below a certain threshold will be eliminated from the game and the same number of new nodes will be added to the network to maintain the size of the network constant. Numerical results show that moderate values of elimination threshold can result in a maximum cooperation level in the evolutionary prisoner’s dilemma game. Moreover, the elimination mechanism can make the network structure evolve into a high heterogeneity in degree distribution, which is considered to be helpful in promoting cooperation in evolutionary games. The present study may provide new insight for understanding the evolution of cooperation in light of the law ‘survival of the fittest’ in nature.     

On fairness,full cooperation,and quantum game with incomplete information

Quantum entanglement has emerged as a new resource to enhance cooperation and remove dilemmas. This paper aims to explore conditions under which full cooperation is achievable even when the information of payoff is incomplete.Based on the quantum version of the extended classical cash in a hat game, we demonstrate that quantum entanglement may be used for achieving full cooperation or avoiding moral hazards with the reasonable profit distribution policies even when the profit is uncertain to a certain degree. This research further suggests that the fairness of profit distribution should play an important role in promoting full cooperation. It is hopeful that quantum entanglement and fairness will promote full cooperation among distant people from various interest groups when quantum networks and quantum entanglement are accessible to the public.     

Prisoner's Dilemma Game on Two Types of Positively Correlated Networks

We study the effects of degree correlations on the evolution of cooperation in the prisoner＇s dilemma game with individuals located on two types of positively correlated networks. It is shown that the positive degree correlation can either promote or inhibit the emergence of cooperation depending on network configurations. Furthermore, we investigate the probability to cooperate as a function of connectivity degree, and find that high-degree individuals generally have a higher tendency to cooperate. Finally, it is found that small-degree individuals usually change their strategy more frequently, and such change is shown to be unfavourable to cooperation for both kinds of networks.     

Influence of the variance of degree distributions on the evolution of cooperation in complex networks

We study how initial network structure affects the evolution of cooperation in a spatial prisoner’s dilemma game. The network structure is characterized by various statistical properties. Among those properties, we focus on the variance of the degree distribution, and inquire how it affects the evolution of cooperation by three methods of imitation. For every method, it was found that a scale-free network does not always promote the evolution of cooperation, and that there exists an appropriate value of the variance, at which cooperation is optimal.     

Promotion of cooperation induced by nonuniform payoff allocation in spatial public goods game

A nonuniform payoff allocation mechanism is proposed for spatial public goods games where individuals are nodes on a scale-free network. Each individual is assigned a weight k_i ^α, where k_i is the degree of individual i and α is an adjustable parameter that controls the degree of diversity in individuals’ profits. During the evolution progress, the allocation of payoff on individual i is assumed to be proportional to its weight. Individuals synchronously update their strategies according to the stochastic rule with a fixed noise level. It is found that there exists an optimal value of α which yields the highest level of cooperation. Other pertinent quantities, including the payoff and the probability of finding a node playing as cooperator versus the degree, are also investigated computationally and analytically. Our results suggest that a suitable degree of diversity among individuals can promote the emergence of cooperation.     

Reputational preference and other-regarding preference based rewarding mechanism promotes cooperation in spatial social dilemmas

To study the incentive mechanisms of cooperation, we propose a preference rewarding mechanism in the spatial prisoner's dilemma game, which simultaneously considers reputational preference, other-regarding preference and the dynamic adjustment of vertex weight. The vertex weight of a player is adaptively adjusted according to the comparison result of his own reputation and the average reputation value of his immediate neighbors. Players are inclined to pay a personal cost to reward the cooperative neighbor with the greatest vertex weight. The vertex weight of a player is proportional to the preference rewards he can obtain from direct neighbors. We find that the preference rewarding mechanism significantly facilitates the evolution of cooperation, and the dynamic adjustment of vertex weight has powerful effect on the emergence of cooperative behavior. To validate multiple effects, strategy distribution and the average payoff and fitness of players are discussed in a microcosmic view.     

A random growth model for power grids and other spatially embedded infrastructure networks

We propose a model to create synthetic networks that may also serve as a narrative of a certain kind of infrastructure network evolution. It consists of an initialization phase with the network extending tree-like for minimum cost and a growth phase with an attachment rule giving a trade-off between cost-optimization and redundancy. Furthermore, we implement the feature of some lines being split during the grid's evolution. We show that the resulting degree distribution has an exponential tail and may show a maximum at degree two, suitable to observations of real-world power grid networks. In particular, the mean degree and the slope of the exponential decay can be controlled in partial independence. To verify to which extent the degree distribution is described by our analytic form, we conduct statistical tests, showing that the hypothesis of an exponential tail is well-accepted for our model data.     

Memory-based prisoner’s dilemma on square lattices

We investigate the evolutionary prisoner’s dilemma with memory-based agents on a square lattice. By introducing memory effects into this game, we assume that individuals’ performance is evaluated in terms of the accumulative payoffs in their memories. It is shown that if individuals behave as their successful neighbors, then cooperation can be significantly promoted. The mechanism responsible for the promotion of cooperation is discussed in detail. We confirm that the promotion of cooperation induced by memory effects remains effective when a preferential selection rule or an asynchronous updating rule is employed. Our work may shed some new light on the study of evolutionary games in real-world situations where the effects of individuals’ memories play a key role in the evolution of cooperation.