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.     

Role of population density and increasing neighborhood in the evolution of cooperation on diluted lattices

We investigate the evolution of cooperative behaviors with increasing neighborhood size on diluted lattices. For three typical pairwise game models which include prisoner’s dilemma, snowdrift and stag hunt games, all numerical results indicate that cooperation can persist or emerge around the optimal population density which is dictated by the percolation threshold on the square lattice. Meanwhile, the neighborhood size determines the interaction ranges of focal players and then dominates the percolation threshold, and extensive numerical simulations demonstrate that the intermediate neighborhood size is the most beneficial to the evolution of cooperation in the current lattice setup. The current findings can help to deeply understand the sustenance and emergence of collective cooperation in many natural, social and economic systems.     

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.     

Effects of agents' mobility on opinion spreading in Sznajd model

Under synchronous updating and allowing the agents to move in the lattice or underlying network, we find that the Sznajd model always reaches a consensus as a steady state, – because agent frustrations are removed due to their diffusion. Moreover, we succeed in obtaining the well-known phase transition of the traditional Sznajd model, which depends on the initial concentration of individuals following an opinion. How the time for reaching consensus depends on the system size, and on the topology have been exhaustively investigated. The analyzed topologies were: annealed and quenched dilution on a square lattice, as well as on a variant of the well-known Barabási-Albert model, called triad network.     

Self-organized Segregation on the Grid

We consider an agent-based model with exponentially distributed waiting times in which two types of agents interact locally over a graph, and based on this interaction and on the value of a common intolerance threshold \(\tau \), decide whether to change their types. This is equivalent to a zero-temperature ising model with Glauber dynamics, an asynchronous cellular automaton with extended Moore neighborhoods, or a Schelling model of self-organized segregation in an open system, and has applications in the analysis of social and biological networks, and spin glasses systems. Some rigorous results were recently obtained in the theoretical computer science literature, and this work provides several extensions. We enlarge the intolerance interval leading to the expected formation of large segregated regions of agents of a single type from the known size \(\epsilon >0\) to size \(\approx 0.134\). Namely, we show that for \(0.433< \tau < 1/2\) (and by symmetry \(1/2<\tau <0.567\)), the expected size of the largest segregated region containing an arbitrary agent is exponential in the size of the neighborhood. We further extend the interval leading to expected large segregated regions to size \(\approx 0.312\) considering “almost segregated” regions, namely regions where the ratio of the number of agents of one type and the number of agents of the other type vanishes quickly as the size of the neighborhood grows. In this case, we show that for \(0.344 < \tau \le 0.433\) (and by symmetry for \(0.567 \le \tau <0.656\)) the expected size of the largest almost segregated region containing an arbitrary agent is exponential in the size of the neighborhood. This behavior is reminiscent of supercritical percolation, where small clusters of empty sites can be observed within any sufficiently large region of the occupied percolation cluster. The exponential bounds that we provide also imply that complete segregation, where agents of a single type cover the whole grid, does not occur with high probability for \(p=1/2\) and the range of intolerance considered.     

Invariant strings and pattern-recognizing properties of one-dimensional cellular automata

A cellular automaton is a discrete dynamical system whose evolution is governed by a deterministic rule involving local interactions. It is shown that given an arbitrary string of values and an arbitrary neighborhood size (representing the range of interaction), a simple procedure can be used to find the rules of that neighborhood size under which the string is invariant. The set of nearestneighbor rules for which invariant strings exist is completely specified, as is the set of strings invariant under each such rule. For any automaton rule, an associated filtering rule is defined for which the only attractors are spatial sequences consisting of concatenations of invariant strings. A result is provided defining the rule of minimum neighborhood size for which an arbitrarily chosen string is the unique invariant string. The applications of filtering rules to pattern recognition problems are discussed.     

Effect of Growing Size of Interaction Neighbors on the Evolution of Cooperation in Spatial Snowdrift Game

In this paper,we study the influence of the size of interaction neighbors(k) on the evolution of cooperation in the spatial snowdrift game.At first,we consider the effects of noise K and cost-to-benefit ratio r,the simulation results indicate that the evolution of cooperation depends on the combined action of noise and cost-to-benefit ratio.For a lower r,the cooperators are multitudinous and the cooperation frequency ultimately increases to 1 as the increase of noise.However,for a higher r,the defectors account for the majority of the game and dominate the game if the noise is large enough.Then we mainly investigate how k influences the evolution of cooperation by varying the noise in detail.We find that the frequency of cooperators is closely related to the size of neighborhood and cost-to-benefit ratio r.In the case of lower r,the augmentation of k plays no positive role in promoting the cooperation as compared with that of k = 4,while for higher r the cooperation is improved for a growing size of neighborhood.At last,based on the above discussions,we explore the cluster-forming mechanism among the cooperators.The current results are beneficial to further understand the evolution of cooperation in many natural,social and biological systems.     

Memory-based evolutionary game on small-world network with tunable heterogeneity

Most papers about evolutionary games on graph assume agents have no memory. Yet, in the real world, interaction history can also affect an agent’s decision. So we introduce a memory-based agent model and investigate the Prisoner’s Dilemma game on a Heterogeneous Newman-Watts small-world network based on a Genetic Algorithm, focusing on heterogeneity’s role in the emergence of cooperative behaviors. In contrast with previous results, we find that a different heterogeneity parameter domain range imposes an entirely different impact on the cooperation fraction. In the parameter range corresponding to networks with extremely high heterogeneity, the decrease in heterogeneity greatly promotes the proportion of cooperation strategy, while in the remaining parameter range, which relates to relatively homogeneous networks, the variation of heterogeneity barely affects the cooperation fraction. Also our study provides a detailed insight into the microscopic factors that contribute to the performance of cooperation frequency.     

Cooperative Dynamics in Lattice-Embedded Scale-Free Networks

We investigate cooperative behaviors of lattice-embeddedscale-free networking agents in the prisoner's dilemma game model byemploying two initial strategy distribution mechanisms, which are specificdistribution to the most connected sites (hubs) and random distribution. Ourstudy indicates that the game dynamics crucially depends on the underlyingspatial network structure with different strategy distribution mechanism.The cooperators' specific distribution contributes to an enhanced level ofcooperation in the system compared with random one, and cooperation isrobust to cooperators' specific distribution but fragile to defectors' specific distribution. Especially, unlike the specific case, increasing heterogeneity of network does not always favor the emergence of cooperation under random mechanism. Furthermore, we study the geographical effects and find that the graphically constrained network structure tends to improve the evolution of cooperation in random case and in specific one for a large temptation to defect.     

Effects of the turnover rate on the size distribution of firms: An application of the kinetic exchange models

We address the issue of the distribution of firm size. To this end we propose a model of firms in a closed, conserved economy populated with zero-intelligence agents who continuously move from one firm to another. We then analyze the size distribution and related statistics obtained from the model. There are three well known statistical features obtained from the panel study of the firms i.e., the power law in size (in terms of income and/or employment), the Laplace distribution in the growth rates and the slowly declining standard deviation of the growth rates conditional on the firm size. First, we show that the model generalizes the usual kinetic exchange models with binary interaction to interactions between an arbitrary number of agents. When the number of interacting agents is in the order of the system itself, it is possible to decouple the model. We provide exact results on the distributions which are not known yet for binary interactions. Our model easily reproduces the power law for the size distribution of firms (Zipf’s law). The fluctuations in the growth rate falls with increasing size following a power law (though the exponent does not match with the data). However, the distribution of the difference of the firm size in this model has Laplace distribution whereas the real data suggests that the difference of the log of sizes has the same distribution.     

Emergence of cooperation in spatial public goods game with conditional participation

Conditional interactions are common in both human and animal societies. To understand the impacts of this feature on the evolution of cooperation, we propose a modified public goods game combined with conditional interactions in terms of the aspiration payoffs. Through simulations, we find that the function of the fraction of cooperators and the synergy factor is non-monotonic. This indicates that a large synergy factor is not always in favor of the promotion of cooperation. In addition, for a high aspiration, the typical coexistence state of cooperators and defectors could disappear, and the system demonstrates a sharp transition from the complete defection state to the complete cooperation state as the synergy factor increases. Furthermore, an interesting critical phenomenon is found in a finite system, i.e., the system can randomly evolve into a complete defection state or a complete cooperation state. An explanation of these evolutionary outcomes is provided in this paper, which is in agreement with the simulation results.     

Environmental dilemma game to establish a sustainable society dealing with an emergent value system

To induce whether we can obtain a sustainable society by shifting our paradigm from the materialistic to the eco-conscientious, we established a multi-agent simulation model. The model primarily featured a dilemma structure encouraged by a conflict between each agent's private desire to earn more and the need for environmental conservation. Another important feature is that the model has two evolutionary layers. The subordinate layer is a learning system comprised of a finite state machine (FSM) and a genetic algorithm (GA) primarily, which is carried with each individual agent to determine his/her next behavior and how much he/she must earn to maximize an individual fitness function. The supra layer is the so-called value system, the gene pool of which is shared within the society. The value system stipulates an agent's fitness function, which in turn affects the agent's behavior. The value system of each agent was set up to be entirely ego-oriented at the beginning of the simulation episode. A numerical experiment based on the model reveals a scene in which, under a certain condition related to assumptions of the value system, a group of agents undergoes a paradigm shift from the ego-oriented materialism to the eco-conscious sustainable society. The key condition is a latent existence of several values that ultimately lead to sustainability, even though they do not work at all at the beginning of the episode. In terms of the evolutionary game theory, this implies that changing game structure on the way of a simulation episode by transforming the fitness function seems to be much powerful measures for the emergent collective cooperation among the agents than ordinal options to support cooperation. In addition, we made a detailed analysis on how assumed agents have obtained a sustainable value system. Each agent has an individual decision-making process based on the input with a learning mechanism. We focus here on two types of learning system, the finite state machine (FSM) plus genetic algorithm (GA), and profit shearing (PS). Observation of the generative trails of FSM and the value table of PS lead us to a profound understanding of what kind of inception triggers the emergence of a sustainable society.     

Decentralized Cooperation Strategies in Two-Dimensional Traffic of Cellular Automata

We study the two-dimensional traffic of cellular automata using computer simulation. We propose two type of decentralized cooperation strategies, which are called stepping aside (CS-SA) and choosing alternative routes (CS-CAR) respectively. We introduce them into an existing two-dimensional cellular automata (CA) model. CS-SA is designed to prohibit a kind of ping-pong jump when two objects standing together try to move in opposite directions. CS-CAR is designed to change the solution of conflict in parallel update. CS-CAR encourages the objects involved in parallel conflicts choose their alternative routes instead of waiting. We also combine the two cooperation strategies (CS-SA-CAR) to test their combined effects. It is found that the system keeps on a partial jam phase with nonzero velocity and flow until the density reaches one. The ratios of the ping-pong jump and the waiting objects involved in conflict are decreased obviously, especially at the free phase. And the average flow is improved by the three cooperation strategies. Although the average travel time is lengthened a bit by CS-CAR, it is shorten by CS-SA and CS-SA-CAR. In addition, we discuss the advantage and applicability of decentralized cooperation modeling.     

Decentralized Cooperation Strategies in Two-Dimensional Traffic of Cellular Automata

We study the two-dimensional traffic of cellular automata using computer simulation. We propose two type of decentralized cooperation strategies, which are called stepping aside (CS-SA) and choosing alternative routes (CS-CAR) respectively. We introduce them into an existing two-dimensional cellular automata (CA) model. CS-SA is designed to prohibit a kind of ping-pong jump when two objects standing together try to move in opposite directions. CS-CAR is designed to change the solution of conflict in parallel update. CS-CAR encourages the objects involved in parallel conflicts choose their alternative routes instead of waiting. We also combine the two cooperation strategies (CS-SA-CAR) to test their combined effects. It is found that the system keeps on a partial jam phase with nonzero velocity and flow until the density reaches one. The ratios of the ping-pong jump and the waiting objects involved in conflict are decreased obviously, especially at the free phase. And the average flow is improved by the three cooperation strategies. Although the average travel time is lengthened a bit by CS-CAR, it is shorten by CS-SA and CS-SA-CAR. In addition, we discuss the advantage and applicability of decentralized cooperation modeling.     

Nanomachinery: A general approach to inducing directed motion at the atomic level

The motion of bodies in a periodic potential relief with weak attenuation is considered. When subjected to various periodic external effects, the bodies may spontaneously move with a velocity uniquely defined by the frequency of a periodic action and the space period of the potential. Principles of inducing directed motion with a strictly controllable velocity that are described in this paper can be used for (1) handling individual molecules or molecular clusters on crystal surfaces, (2) creating nanomachines—objects that are free to spontaneously move both in the absence of an external force and in the presence of a force opposite to the direction of motion (and thus capable of transporting other objects), (3) designing actuators providing a strictly controllable velocity of motion, and (4) designing controllable tribological systems by appropriately profiling tribosurfaces and applying ultrasonic actions. Under periodic external perturbations, the dependence of the mean velocity of a system on the mean applied force (which macroscopically appears as the “friction law” for the system) is shown to contain plateaus of constant velocity not only when the velocity of motion is zero but also when a set of discrete equidistant velocities is present. The problem of creating totally controllable nanomachines can be posed as the problem of controlling the width and position of these plateaus.     

Variations in epidemic distribution with some characteristic parameters

Considering the spread of an epidemic among a population of mobile agents that can get infected and maintain the infection for a period, we investigate the variation in the homogeneity of the distribution of the epidemic with the remaining time of infection τ, the velocity modulus of the agent v, and the infection rate α. We find that the distribution of the infected cluster size is always exponential. By analyzing the variation of the characteristic infected cluster size coefficient, we show that the inhomogeneity of epidemic distribution increases with an increase in τ for very low v, while it decreases with an increase in τ for moderate v. The epidemic distribution also tends to a homogeneous state as both v and α increase.     

Integrating neighborhoods in the evaluation of fitness promotes cooperation in the spatial prisoner’s dilemma game

A fundamental question of human society is the evolution of cooperation. Many previous studies explored this question via setting spatial background, where players obtain their payoffs by playing game with their nearest neighbors. Another undoubted fact is that the environment plays an important role in the individual development. Inspired by these phenomena, we reconsider the definition of individual fitness which integrates the environment, denoted by the average payoff of all individual neighbors, with the traditional individual payoffs by introducing a selection parameter u. Tuning u equal to zero returns the traditional version, while increasing u bears the influence of environment. We find that considering the environment, i.e., integrating neighborhoods in the evaluation of fitness, promotes cooperation. If we enhance the value of u, the invasion of defection could be resisted better. We also provide quantitative explanations and complete phase diagrams presenting the influence of the environment on the evolution of cooperation. Finally, the universality of this mechanism is testified for different neighborhood sizes, different topology structures and different game models. Our work may shed light on the emergence and persistence of cooperation in our life.     

Coevolving agent strategies and network topology for the public goods games

Much of human cooperation remains an evolutionary riddle. Coevolutionary public goods games in structured populations are studied where players can change from an unproductive public goods game to a productive one, by evaluating the productivity of the public goods games. In our model, each individual participates in games organized by its neighborhood plus by itself. Coevolution here refers to an evolutionary process entailing both deletion of existing links and addition of new links between agents that accompanies the evolution of their strategies. Furthermore, we investigate the effects of time scale separation of strategy and structure on cooperation level. This study presents the following: Foremost, we observe that high cooperation levels in public goods interactions are attained by the entangled coevolution of strategy and structure. Presented results also confirm that the resulting networks show many features of real systems, such as cooperative behavior and hierarchical clustering. The heterogeneity of the interaction network is held responsible for the observed promotion of cooperation. We hope our work may offer an explanation for the origin of large-scale cooperative behavior among unrelated individuals.