Benefits of tolerance in public goods games

Leaving the joint enterprise when defection is unveiled is always a viable option to avoid being exploited. Although loner strategy helps the population not to be trapped into the tragedy of the commons state, it could offer only a modest income for non-participants. In this paper we demonstrate that showing some tolerance toward defectors could not only save cooperation in harsh environments, but in fact results in a surprisingly high average payoff for group members in public goods games. Phase diagrams and the underlying spatial patterns reveal the high complexity of evolving states where cyclic dominant strategies or two-strategy alliances can characterize the final state of evolution. We identify microscopic mechanisms which are responsible for the superiority of global solutions containing tolerant players. This phenomenon is robust and can be observed both in well-mixed and in structured populations highlighting the importance of tolerance in our everyday life.Comment: 10 two-column pages, 8 figures; accepted for publication in Physical Review

Optimal distribution of incentives for public cooperation in heterogeneous interaction environments

In the framework of evolutionary games with institutional reciprocity, limited incentives are at disposal for rewarding cooperators and punishing defectors. In the simplest case, it can be assumed that, depending on their strategies, all players receive equal incentives from the common pool. The question arises, however, what is the optimal distribution of institutional incentives? How should we best reward and punish individuals for cooperation to thrive? We study this problem for the public goods game on a scale-free network. We show that if the synergetic effects of group interactions are weak, the level of cooperation in the population can be maximized simply by adopting the simplest "equal distribution" scheme. If synergetic effects are strong, however, it is best to reward high-degree nodes more than low-degree nodes. These distribution schemes for institutional rewards are independent of payoff normalization. For institutional punishment, however, the same optimization problem is more complex, and its solution depends on whether absolute or degree-normalized payoffs are used. We find that degree-normalized payoffs require high-degree nodes be punished more lenient than low-degree nodes. Conversely, if absolute payoffs count, then high-degree nodes should be punished stronger than low-degree nodes.Comment: 19 pages, 8 figures; accepted for publication in Frontiers in Behavioral Neuroscienc

Role of the effective payoff function in evolutionary game dynamics

In most studies regarding evolutionary game dynamics, the effective payoff, a quantity that translates the payoff derived from game interactions into reproductive success, is usually assumed to be a specific function of the payoff. Meanwhile, the effect of different function forms of effective payoff on evolutionary dynamics is always left in the basket. With introducing a generalized mapping that the effective payoff of individuals is a non-negative function of two variables on selection intensity and payoff, we study how different effective payoff functions affect evolutionary dynamics in a symmetrical mutation-selection process. For standard two-strategy two-player games, we find that under weak selection the condition for one strategy to dominate the other depends not only on the classical {\sigma}-rule, but also on an extra constant that is determined by the form of the effective payoff function. By changing the sign of the constant, we can alter the direction of strategy selection. Taking the Moran process and pairwise comparison process as specific models in well-mixed populations, we find that different fitness or imitation mappings are equivalent under weak selection. Moreover, the sign of the extra constant determines the direction of one-third law and risk-dominance for sufficiently large populations. This work thus helps to elucidate how the effective payoff function as another fundamental ingredient of evolution affect evolutionary dynamics.Comment: This paper has been accepted to publish on EP

Competition and cooperation among different punishing strategies in the spatial public goods game

Inspired by the fact that people have diverse propensities to punish wrongdoers, we study a spatial public goods game with defectors and different types of punishing cooperators. During the game, cooperators punish defectors with class-specific probabilities and subsequently share the associated costs of sanctioning. We show that in the presence of different punishing cooperators the highest level of public cooperation is always attainable through a selection mechanism. Interestingly, the selection not necessarily favors the evolution of punishers who would be able to prevail on their own against the defectors, nor does it always hinder the evolution of punishers who would be unable to prevail on their own. Instead, the evolutionary success of punishing strategies depends sensitively on their invasion velocities, which in turn reveals fascinating examples of both competition and cooperation among them. Furthermore, we show that under favorable conditions, when punishment is not strictly necessary for the maintenance of public cooperation, the less aggressive, mild form of sanctioning is the sole victor of selection process. Our work reveals that natural strategy selection can not only promote, but sometimes also hinder competition among prosocial strategies.Comment: 6 two-column pages, 5 figures; accepted for publication in Physical Review

Influence of initial distributions on robust cooperation in evolutionary Prisoner's Dilemma

We study the evolutionary Prisoner's Dilemma game on scale-free networks for different initial distributions. We consider three types of initial distributions for cooperators and defectors: initially random distribution with different frequencies of defectors; intentional organization with defectors initially occupying the most connected nodes with different fractions of defectors; intentional assignment for cooperators occupying the most connected nodes with different proportions of defectors at the beginning. It is shown that initial configurations for cooperators and defectors can influence the stationary level of cooperation and the evolution speed of cooperation. Organizations with the vertices with highest connectivity representing individuals cooperators could exhibit the most robust cooperation and drive evolutionary process to converge fastest to the high steady cooperation in the three situations of initial distributions. Otherwise, we determine the critical initial frequencies of defectors above which the extinction of cooperators occurs for the respective initial distributions, and find that the presence of network loops and clusters for cooperators can favor the emergence of cooperation.Comment: Submitted to EP

Probabilistic sharing solves the problem of costly punishment

Cooperators that refuse to participate in sanctioning defectors create the second-order free-rider problem. Such cooperators will not be punished because they contribute to the public good, but they also eschew the costs associated with punishing defectors. Altruistic punishers - those that cooperate and punish - are at a disadvantage, and it is puzzling how such behaviour has evolved. We show that sharing the responsibility to sanction defectors rather than relying on certain individuals to do so permanently can solve the problem of costly punishment. Inspired by the fact that humans have strong but also emotional tendencies for fair play, we consider probabilistic sanctioning as the simplest way of distributing the duty. In well-mixed populations the public goods game is transformed into a coordination game with full cooperation and defection as the two stable equilibria, while in structured populations pattern formation supports additional counterintuitive solutions that are reminiscent of Parrondo's paradox.Comment: 15 pages, 5 figures; accepted for publication in New Journal of Physic

Generalized generalized gradient approximation: An improved density-functional theory for accurate orbital eigenvalues

The generalized gradient approximation (GGA) for the exchange functional in conjunction with accurate expressions for the correlation functional have led to numerous applications in which density-functional theory (DFT) provides structures, bond energies, and reaction activation energies in excellent agreement with the most accurate ab initio calculations and with the experiment. However, the orbital energies that arise from the Kohn-Sham auxiliary equations of DFT may differ by a factor of 2 from the ionization potentials, indicating that excitation energies and properties involving sums over excited states (nonlinear-optical properties, van der Waals attraction) may be in serious error.mWe propose herein a generalization of the GGA in which the changes in the functionals due to virtual changes in the orbitals are allowed to differ from the functional used to map the exact density onto the exact energy. Using the simplest version of this generalized GGA we show that orbital energies are within ∼5% of the correct values and the long-range behavior has the correct form