Narrow Results

We study the evolution of cooperation in public goods games on the square lattice, focusing on the co-player learning mechanism based on the preferential selection that are brought about by wealthy information of groups where participants collect and search for potential imitators from those groups. We find that co-player learning mechanism based on the choice of weighted group can lead to the promotion of public cooperation by means of the information of wealthy groups that is obtained by participants, and after that the partial choice of public goods groups is enhanced with the tunable preferential parameter. Our results highlight that the learning interactions is not solely confined to the restricted connection among players, but co-players of wealthy groups have the opportunity to be as a role model in the promotion of cooperative evolution. Moreover, we also find the size of learning affects the choice of distant players, cooperators (defectors) having more paths to exploit the phalanx of opponents to survive when the value of preferential parameter is small. Besides, the extinction thresholds of cooperators and defectors for different values of noise are also investigated.

Most of the previous studies research cooperation mainly based on donating money in social public goods games. Owing to the lack of income, some people prefer to donate time instead of money to promote the activity, in our daily life. Motivated by this fact, we here investigate the influence of the encouragement of donating time on the evolution of cooperation based on village opera. In our study, we set up two models: one is money-only model (MOM). Donating money is the only choice in MOM. The other is money–time model (MTM). Besides donating money, donating time is an alternative in MTM. Through numerical simulations, we find that compared to MOM, MTM has a faster speed to reach cooperation equilibrium and cost advantage to sustain the same cooperation level, without the effects of income, reputation, satisfaction, emotion and maximum nonmonetary input. However, it should be noted that MTM is better than MOM in a moderate interval of general budget $V$. Our results provide stark evidence that the encouragement of donating time can promote and sustain cooperation better than only donating money.

This paper investigates the evolutionary public goods game on a network and studies the effect of the leaders rewiring mechanism (LRM) on the evolution of cooperation. A trust mechanism is introduced to give information to the leader about the sincerity of the group. The network dynamics is driven by the LRM, allowing leaders to change their game groups if these groups are not trusted anymore. We investigate how the emergence of the network guided by LRM affects the transformation of individuals’ strategies and empowers them to cooperate. We find that LRM plays a crucial role in the emergence of cooperation, by clustering the graph into regions with high clusters of cooperators and small one of defectors. LRM enables cooperators to form compact big clusters, thus reducing exploitation by defectors.

How to effectively maintain costly intra-group cooperation has always been a topic of great concern in society. As we all know, inter-group competition is a constructive way to promote intra-group cooperation. Previous inter-group competition mechanisms are primarily based on the total contribution value of all individuals in the group in the current round, which ignore the historical behavior of the group and its members. For comparison, we argue that the introduction of collective reputation which reflects not only the total contribution of the groups in the current round but also the group’s historical behavior is necessary for the construction of an inter-group competition model. Furthermore, an excellent collective reputation can help the group obtain more high-quality resources and help the group stand out from the competition. To this end, we introduce a new mechanism of inter-group transfer payment driven by collective reputation and construct a public goods game (PGG) model of inter-group competition to study the dynamic behavior of intra-group cooperation evolution. Two parameters, the proportion of accepted groups $a$ and the proportion of transferred groups $b,$ are introduced. The simulation results suggest that the effectiveness of this model in promoting cooperation depends largely on the joint settings of $a$ and $b$. And in most cases, the combination of small $a$ and medium $b$ plays the significant role.

Incentive institutions that reward cooperators and punish free-riders are often used to promote cooperation in public goods games. We show that for incentives of intermediate size, a sanctioning institution that punishes the worst players can sustain full cooperation and that a rewarding institution can promote cooperation only if lower contributors also have the chance to win the reward. Furthermore, if the incentive institution can provide both reward and punishment, then it should use reward as much as possible. The group welfare is maximized when the punishment is just barely larger than the minimum required to obtain the full contribution.

The emergence and evolution of cooperation in complex natural, social and economical systems is an interdisciplinary topic of recent interest. This paper focuses on the cooperation on complex networks using the approach of evolutionary games. In particular, the phenomenon of diversity-optimized cooperation is briefly reviewed and the effect of network clustering on cooperation is treated in detail. For the latter, a general type of public goods games is used with the result that, for fixed average degree and degree distributions in the underlying network, a high clustering coefficient can promote cooperation. Basic quantities such as the cooperator and defector clusters, mean payoffs of cooperators and defectors along their respective boundaries, the fraction of cooperators for different classes as well as the mean payoffs of hubs in scale-free networks are also investigated. Since strong clustering is typical in many social networks, these results provide insights into the emergence of cooperation in such networks.

In public goods games, individuals contribute to create a benefit for a group. However this attracts free-riders, who enjoy the benefits without necessarily contributing. Nonetheless, in real-life scenarios cooperation does not collapse. Several explanations have been proposed in order to explain this phenomenon, such as punishment and signaling. In the present work, we investigate the effects of new elements associated with punishment upon signaling such as gossiping and bribery. Agents may denounce free-riders (who on their turn get punished) or may be bribed to remain silent and even spread rumors of false good behavior. Having a model with richer social mechanisms enable us to test how cooperation develops in situations in which players have social attachments. Our results show that when punishment and bribery are present, the levels of contribution are kept at a relatively higher value compared to the situation when no punishment is exercised. As to what regards gossiping, if the number of free-riders is high, finding mechanisms to prevent gossiping could be an important step in order to increase the contribution. If the ratio between free-riders and other agents is about one, then gossiping does affect contribution in a positive way.

Evolution of cooperation has attracted considerable attention but so far no definitive answer exists. Probably each kind of problem has specific answers. This paper deals with evolution of cooperation in public goods games. We use random Boolean networks to formalize the non-local influence of K agents over a given agent. This formalism allows the representation of a variety of network regulation mechanisms by means of (i) different topologies and (ii) Boolean functions that do the regulation proper. However, random functions and connections do not necessarily lead to cooperation. Thus, it is necessary to find what kind of network structure is prone to promote cooperation. We employ an evolutionary approach to show that evolving the topologies and the random functions leads to much fitter structures.