Effect of Aspiration and Mean Gain on the Emergence of Cooperation in Unidirectional Pedestrian Flow

When more than one pedestrian want to move to the same site,conflicts appear and thus the involved pedestrians play a motion game.In order to describe the emergence of cooperation during the conflict resolving process,an evolutionary cellular automation model is established considering the effect of aspiration and mean gain.In each game,pedestrian may be gentle cooperator or aggressive defector.We propose a set of win-stay-lose-shrift WSLS like rules for updating pedestrian's strategy.These rules prescribe that if the mean gain of current strategy between some given steps is larger than aspiration the strategy keeps,otherwise the strategy changes.The simulation results show that a high level aspiration will lead to more cooperation.With the increment of the statistic length,pedestrians will be more rational in decision making.It is also found that when the aspiration level is small enough and the statistic length is large enough all the pedestrian will turn to defectors.We use the prisoner's dilemma model to explain it.At last we discuss the effect of aspiration on fundamental diagram.     

Motion of influential players can support cooperation in Prisoner’s Dilemma

We study a spatial Prisoner’s dilemma game with two types (A and B) of players located on a square lattice. Players following either cooperator or defector strategies play Prisoner’s Dilemma games with their 24 nearest neighbors. The players are allowed to adopt one of their neighbor’s strategy with a probability dependent on the payoff difference and type of the given neighbor. Players A and B have different efficiency in the transfer of their own strategies; therefore the strategy adoption probability is reduced by a multiplicative factor (w < 1) from the players of type B. We report that the motion of the influential payers (type A) can improve remarkably the maintenance of cooperation even for their low densities.     

The use of a data envelopment analysis efficient rule to produce a steady and high cooperation frequency in a spatial evolvement game

We use the data envelopment analysis (DEA) method to estimate the relative efficiency of the current strategy for each player in a game by taking the spatial distribution of strategies as input and the total payoff as output. Based on the optimal value of the DEA model, we present a DEA efficient rule to update the strategy in evolutionary games. Simulations of the prisoner’s dilemma game (PDG) [4] and the snowdrift game (SG) on two-dimensional regular lattices of four, six, and eight neighbors with periodic boundary conditions, are carried out; the results show the emergence of high and stable cooperator frequency. The heuristic analysis of the DEA efficient rule are discussed in detail. Our work may be helpful in exploring the promotion of cooperator behavior.     

Hysteresis behavior and nonequilibrium phase transition in a one-dimensional evolutionary game model

We investigate a simple evolutionary game model in one dimension. It is found that the system exhibits a discontinuous phase transition from a defection state to a cooperation state when the b payoff of a defector exploiting a cooperator is small. Furthermore, if b is large enough, then the system exhibits two continuous phase transitions between two absorbing states and a coexistence state of cooperation and defection, respectively. The tri-critical point is roughly estimated. Moreover, it is found that the critical behavior of the continuous phase transition with an absorbing state is in the directed percolation universality class.     

零行列式策略在雪堆博弈中的演化

零行列式策略不仅可以单方面设置对手收益,而且可以对双方的收益施加一个线性关系,从而达到敲诈对手的目的.本文针对零行列式策略博弈前期与稳态期的收益存在偏差,基于Markov链理论给出零行列式策略与全合作策略博弈的瞬态分布、瞬态收益及达到稳态所需时间.发现在小的敲诈因子下,敲诈者前期收益高于稳态期收益,敲诈因子较大时,情况截然相反,并且敲诈因子越大,越不利于双方合作,达到稳态也越慢.这为现实生活中频繁更新策略的博弈提供了一种计算实时收益的方法.此外针对敲诈策略与进化人的博弈,论证了双方均背叛状态下,进化人下次博弈时一定进化为全合作策略.通过对所有状态下策略更新过程仿真,发现进化人在四种情况下的进化速度有显著差异,并最终演化为全合作策略,表明零行列式策略是合作产生的催化剂.     

Spatial snowdrift game with myopic agents

We have studied a spatially extended snowdrift game, in which the players are located on the sites of two-dimensional square lattices and repeatedly have to choose one of the two strategies, either cooperation (C) or defection (D). A player interacts with its nearest neighbors only, and aims at playing a strategy which maximizes its instant pay-off, assuming that the neighboring agents retain their strategies. If a player is not content with its current strategy, it will change it to the opposite one with probability p next round. Here we show through simulations and analytical approach that these rules result in cooperation levels, which differ to large extent from those obtained using the replicator dynamics.     

Evolution of cooperation in the spatial public goods game with the third-order reputation evaluation

In this letter, in order to deeply explore the role of individual reputation in the evolutionary game dynamics, we present a new third-order reputation evaluation model to discuss the evolution of cooperation in the spatial public goods game. In the current model, we should not only consider the strategy (cooperation, C or defection, D) of a focal player, but also take his own reputation and his opponent's reputation status into account. Among them, the individual reputation will be divided into being good and bad according to the specified threshold, and the good player will be endowed with the more influential strategy transfer ability, which further helps to create the clusters of cooperative and good players within the population and then fosters the cooperation. A large plethora of experimental simulation results indicate that four rules under the third-order reputation mechanism can lead to the promotion of cooperation when compared to the traditional public goods game model. The current work is conductive to a better understanding of the persistence and emergence of collective cooperation in real-world systems.     

Enhancement of Cooperation and Reentrant Phase of Prisoner’s Dilemma Game on Signed Networks

We studied the prisoner’s dilemma game as applied to signed networks. In signed networks, there are two types of links: positive and negative. To establish a payoff matrix between players connected with a negative link, we multiplied the payoff matrix between players connected with a positive link by −1. To investigate the effect of negative links on cooperating behavior, we performed simulations for different negative link densities. When the negative link density is low, the density of the cooperator becomes zero because there is an increasing temptation payoff, b. Here, parameter b is the payoff received by the defector from playing the game with a cooperator. Conversely, when the negative link density is high, the cooperator density becomes almost 1 as b increases. This is because players with a negative link will suffer more payoff damage if they do not cooperate with each other. The negative link forces players to cooperate, so cooperating behavior is enhanced.     

The evolution of cooperation within the multigame environment based on the Particle Swarm Optimization algorithm

This paper studies the evolution of cooperation in a so-called multigame environment based on the Particle Swarm Optimization (PSO) algorithm. In a multigame environment, players use different game payoff matrices and acquire their utilities from their interactions with their neighbors. According to the PSO algorithm, each player updates its strategy according to both the strategy adopted by the player with the highest utility in its neighborhood and the most profitable strategy in their own past actions. Simulation results show that the multigame environment is conducive to the promotion of cooperation. Besides, within the multigame environment, for any player, imitating the most profitable strategy in its past actions promotes cooperation more effectively than imitating the strategy adopted by the player with the highest utility in its neighborhood.     

Role of memory effect in the evolution of cooperation based on spatial prisoner's dilemma game

We investigate the impact of memory effect on the evolution of cooperation in the spatial prisoner's dilemma game, in which each player will record his own strategies during the previous M game rounds (here, M is also named as the memory length). At each time step, each individual will update his current strategy according to the Fermi-like probability which will be multiplied by a pre-factor, and this factor will be correlated with the fraction of previous strategy states identical with the individual strategy to be updated. The numerical simulation results demonstrate that the memory length will largely influence the cooperation level at the stationary state, and it is clearly shown that the intermediate value of M will optimally favor the emergence of cooperation and the dynamical evolution, and characteristic patterns also support these conclusions. In addition, we depict the full cooperation phase diagrams and find that the cooperation region will be broadened under the case of moderate M values. The current results also indicate that the limited memory may be enough for us to design the effective promotion mechanism and further understand the emergency of cooperation taking place upon many networked populations.     

Evolutionary snowdrift game with disordered environments in mobile societies

We investigate an evolutionary snowdrift game on a square $N=L\times L$ lattice with periodic boundary conditions, where a population of $n_{0}$ ($n_{0}\leq N$) players located on the sites of this lattice can either cooperate with or defect from their nearest neighbours. After each generation, every player moves with a certain probability $p$ to one of the player's nearest empty sites. It is shown that, when $p=0$, the cooperative behaviour can be enhanced in disordered structures. When $p>0$, the effect of mobility on cooperation remarkably depends on the payoff parameter $r$ and the density of individuals $\rho$ ($\rho=n_{0}/N$). Compared with the results of $p=0$, for small $r$, the persistence of cooperation is enhanced at not too small values of $\rho$; whereas for large $r$, the introduction of mobility inhibits the emergence of cooperation at any $\rho<1$; for the intermediate value of $r$, the cooperative behaviour is sometimes enhanced and sometimes inhibited, depending on the values of $p$ and $\rho$. In particular, the cooperator density can reach its maximum when the values of $p$ and $\rho$ reach their respective optimal values. In addition, two absorbing states of all cooperators and all defectors can emerge respectively for small and large $r$ in the case of $p>0$.     

Promotion of cooperation induced by the interplay between structure and game dynamics

We consider the coupled dynamics of the adaption of network structure and the evolution of strategies played by individuals occupying the network vertices. We propose a computational model in which each agent plays a n-round Prisoner's Dilemma game with its immediate neighbors, after that, based upon self-interest, partial individuals may punish their defective neighbors by dismissing the social tie to the one who defects the most times, meanwhile seek for a new partner at random from the neighbors of the punished agent. It is found that the promotion of cooperation is attributed to the entangled evolution of individual strategy and network structure. Moreover, we show that the emerging social networks exhibit high heterogeneity and disassortative mixing pattern. For a given average connectivity of the population and the number of rounds, there is a critical value for the fraction of individuals adapting their social interactions, above which cooperators wipe out defectors. Besides, the effects of the average degree, the number of rounds, and the intensity of selection are investigated by extensive numerical simulations. Our results to some extent reflect the underlying mechanism promoting cooperation.     

Cooperation and its evolution in growing systems with cultural reproduction

We explore the evolution of cooperation in the framework of the evolutionary game theory using the prisoner’s dilemma as metaphor of the problem. We present a minimal model taking into account the growing process of the systems and individuals with imitation capacity. We consider the topological structure and the evolution of strategies decoupled instead of a coevolutionary dynamic. We show conditions to build up a cooperative system with real topological structures for any natural selection intensity. When the system starts to grow, cooperation is unstable but becomes stable as soon as the system reaches a small core of cooperators whose size increases when the intensity of natural selection decreases. Thus, we reduce the evolution of cooperative systems with cultural reproduction to justify a small initial cooperative structure that we call cooperative seed. Otherwise, given that the system grows principally as cooperator whose cooperators inhabit the most linked parts of the system, the benefit-cost ratio required for cooperation evolve is drastically reduced compared to the found in static networks. In this way, we show that in systems whose individuals have imitation capacity the growing process is essential for the evolution of cooperation.     

A 2-stage strategy updating rule promotes cooperation in the prisoner's dilemma game

In this study,we propose a spatial prisoner’s dilemma game model with a 2-stage strategy updating rule,and focus on the cooperation behavior of the system.In the first stage,i.e.,the pre-learning stage,a focal player decides whether to update his strategy according to the pre-learning factor β and the payoff difference between himself and the average of his neighbors.If the player makes up his mind to update,he enters into the second stage,i.e.,the learning stage,and adopts a strategy of a randomly selected neighbor according to the standard Fermi updating rule.The simulation results show that the cooperation level has a non-trivial dependence on the pre-learning factor.Generally,the cooperation frequency decreases as the pre-learning factor increases;but a high cooperation level can be obtained in the intermediate region of 3 < β < 1.We then give some explanations via studying the co-action of pre-learning and learning.Our results may sharpen the understanding of the influence of the strategy updating rule on evolutionary games.     

Evolutionary game dynamics of combining the Moran and imitation processes

One of the assumptions of previous research in evolutionary game dynamics is that individuals use only one rule to update their strategy. In reality, an individual's strategy update rules may change with the environment, and it is possible for an individual to use two or more rules to update their strategy. We consider the case where an individual updates strategies based on the Moran and imitation processes, and establish mixed stochastic evolutionary game dynamics by combining both processes. Our aim is to study how individuals change strategies based on two update rules and how this affects evolutionary game dynamics. We obtain an analytic expression and properties of the fixation probability and fixation times(the unconditional fixation time or conditional average fixation time) associated with our proposed process. We find unexpected results. The fixation probability within the proposed model is independent of the probabilities that the individual adopts the imitation rule update strategy. This implies that the fixation probability within the proposed model is equal to that from the Moran and imitation processes. The one-third rule holds in the proposed mixed model. However, under weak selection, the fixation times are different from those of the Moran and imitation processes because it is connected with the probability that individuals adopt an imitation update rule. Numerical examples are presented to illustrate the relationships between fixation times and the probability that an individual adopts the imitation update rule, as well as between fixation times and selection intensity. From the simulated analysis, we find that the fixation time for a mixed process is greater than that of the Moran process, but is less than that of the imitation process. Moreover, the fixation times for a cooperator in the proposed process increase as the probability of adopting an imitation update increases; however, the relationship becomes more complex than a linear relationship.     

Promoting cooperation in social dilemmas via simple coevolutionary rules

We study the evolution of cooperation in structured populations within popular models of social dilemmas, whereby simple coevolutionary rules are introduced that may enhance players abilities to enforce their strategy on the opponent. Coevolution thus here refers to an evolutionary process affecting the teaching activity of players that accompanies the evolution of their strategies. Particularly, we increase the teaching activity of a player after it has successfully reproduced, yet we do so depending on the disseminated strategy. We separately consider coevolution affecting either only the cooperators or only the defectors, and show that both options promote cooperation irrespective of the applied game. Opposite to intuitive reasoning, however, we reveal that the coevolutionary promotion of players spreading defection is, in the long run, more beneficial for cooperation than the likewise promotion of cooperators. We explain the contradictive impact of the two considered coevolutionary rules by examining the differences between resulting heterogeneities that segregate participating players, and furthermore, demonstrate that the influential individuals completely determine the final outcome of the games. Our findings are immune to changes defining the type of considered social dilemmas and highlight that the heterogeneity of players, resulting in a positive feedback mechanism, is a fundamental property promoting cooperation in groups of selfish individuals.     

Critical transition induced by neighbourhood size in evolutionary spatial games

We consider an evolutionary two-strategy two-player game where individual strategies evolve by imitation of players with large payoffs, and affected by noise. In a well-mixed population, the system approaches a mixed state with a part of the population in each strategy. On the other hand, when the population is distributed along a one-dimensional array and interactions are limited to a small neighbourhood of each player, the system falls in an absorbing state with a pure dominant strategy. We characterize the transition between these two regimes as a function of the neighbourhood size, providing evidence that it belongs to the universality class of directed percolation. Critical exponents are numerically evaluated, and the dependence of the critical point on the payoff and noise parameters is analyzed.     

Spatial Games Based on Pursuing the Highest Average Payoff

We propose a strategy updating mechanism based on pursuing the highest average payoff to investigate the prisoner＇s dilemma game and the snowdrift game. We apply the new rule to investigate cooperative behaviours on regular, small-world, scale-free networks, and find spatial structure can maintain cooperation for the prisoner＇s dilemma game. fn the snowdrift game, spatial structure can inhibit or promote cooperative behaviour which depends on payoff parameter. We further study cooperative behaviour on scale-free network in detail. Interestingly, non-monotonous behaviours observed on scale-free network with middle-degree individuals have the lowest cooperation level. We also find that large-degree individuals change their strategies more frequently for both games.     

Fitness of others' evaluation effect promotes cooperation in spatial public goods game

Payoff-driven strategy updating rule has always been adopted as a classic mechanism, but up to now, there have been a great many of researches on considering other forms of strategy updating rules, among which pursuing high fitness is one of the most direct and conventional motivations in the decision-making using game theory. But there are few or no researches on fitness from the perspective of others' evaluation. In view of this, we propose a new model in which the evaluation effect with fitness-driven strategy updating rule is taken into consideration, and introduce an evaluation coefficient to present the degree of others' evaluation on individual's behavior. The cooperative individuals can get positive evaluation, otherwise defective individuals get negative evaluation, and the degree of evaluation is related to the number of neighbors who have the same strategy of individual. Through numerical simulation, we find that the evaluation effect of others can enhance the network reciprocity, thus promoting the cooperation. For a strong dilemma, the higher evaluation coefficient can greatly weaken the cooperation dilemma; for a weak one, the higher evaluation coefficient can make cooperator clusters spread faster, however, there is no significant difference in the level of cooperation in the final stable state among different evaluation coefficients. The cooperation becomes more flourish as the number of fitness-driven individuals increases, when all individuals adopt fitness-driven strategy updating rule, the cooperators can quickly occupy the whole population. Besides, we demonstrate the robustness of the results on the WS small-world network, ER random network, and BA scale-free network.     

Cooperation in networked populations of selfish adaptive agents: Sensitivity to network structure

This paper investigates the adaptation of cooperating strategies in an iterated prisoner's dilemma (IPD) game with individually learning agents, subject to the structure of the interaction network. In particular, we study how cooperation or defection comes to dominate the population on Watts–Strogatz networks, under varying average path lengths. Our results are in good agreement with previous works on discrete choice dynamics on networks, but are in stark contrast with results from the evolution of cooperation literature. We argue that the latter is because the different adaptation method used (i.e., adaptive learning instead of ‘evolutionary’ strategy switching).