Emergence Of Cooperative Behavior Research Articles

An evolutionary game with revengers and sufferers on complex networks

The networked evolutionary game theory makes investigations on how the emergence of cooperative behaviors in the real world possible. Many researches based on costly punishment have found profound achievements. However, the order of punishment and timely update of payoff in the process of punishment do not get much attention. Therefore, based on the above deficiencies, we study a revenge-based prisoner’s dilemma game on square-lattice and small-world networks to explore how it affects the emergence and maintenance of cooperation behaviors on complex networks. In simulations, we exhibit the evolution of the cooperation frequency as the population processes and probe the effects of the loss function, the number of players updating strategies, the cost-to-benefit ratio, and network size on the cooperation frequency, and further demonstrate the evolution of the number of revengers and sufferers over time, which may help to understand the role of them played in networks. By varying corresponding revenge parameters, our proposed mechanism helps to overcome social dilemmas. Moreover, we find the phenomenon that the cooperation frequency declines and then rises in small-world networks under certain conditions, which we validate from the variance of the numbers of revengers and sufferers over time. Our work may help to illuminate the study of evolutionary games with revengers and sufferers.

A Strategy Adaptive Evolution Approach Based on the Public Goods Game

Cluster behavior is prevalent in nature. Many individuals change their behavior to adapt to a dynamically changing environment by following simple rules of behavior and interacting with information from neighboring individuals. In this study, the traditional public goods game model is improved by combining the advantages of game theory and interactive learning. A strategy adaptive evolution method based on a public goods game is proposed. The emergence of cooperative behavior in weighted networks under the co-evolution of game strategies and node weights is explored in conjunction with multi-agent interactive learning. The results show that in a public goods game with strategic adaptation, a person’s influence becomes greater if their level of adaptation exceeds the desired level, and less otherwise. This weight adjustment is defined by the intensity parameter δ. A moderate δ value can effectively facilitate the occurrence of cooperative evolution. The level of cooperation depends mainly on the weight distribution of participants, which leads to the formation of cooperative clusters controlled by high-weighted cooperators. Even with the great temptation to defect, these cooperators can prevail over defectors. The adjustment of node weights increases the heterogeneity of individuals. This research provides a viable pathway to solve social dilemmas and will further promote the application of multi-agent intelligent decision making.

Evolutionary public goods games on hypergraphs with heterogeneous multiplication factors

The spatial structure and social diversity playing a nontrivial role in the emergence and maintenance of cooperation among selfish individuals have been verified. Their effects on the evolution of cooperation have attracted great attention in recent years. Most of previous evolutionary game dynamics is based on pairwise interactions. However, the interactions often take place within groups of people in many real situations and cannot be described simply by dyads. The dynamics of evolutionary games in systems with higher-order interactions has not yet been explored as deserved. In this paper, we introduce heterogeneous multiplication factors into the spatial public goods game to investigate the cooperative behaviors on the hypergraphs. In addition to the original model in which all groups have the same multiplication factor, three types of heterogeneous multiplication factor distributions including uniform, exponential and power-law distributions are considered. The numerical simulation results show that the increase of the order <i>g</i> of the uniform random hypergraphs is conducive to the emergence and prosperity of the individuals' cooperative behavior no matter what types these distributions belong to. Furthermore, compared with the results of the original spatial public goods games on hypergraphs, the heterogeneous multiplication factors following three different distributions can remarkably promote the evolution of cooperation. In particular, for most of ranges of the average rescaling multiplication factor <inline-formula><tex-math id="M4">\begin{document}$r_0$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="11-20212436_M4.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="11-20212436_M4.png"/></alternatives></inline-formula>, the highest cooperation level can be obtained under the power-law distribution, while the uniform distribution leads to the lowest cooperation level. We provide an explanation through investigating the number of cooperators in each group. In addition, to probe into the essence that influences the survival of cooperative behaviors, we study the time series of the fraction of groups with different numbers of cooperators. Besides, we also investigate the influence of the number of hyperlinks on cooperation evolution. We find that the results are robust against the number of hyperlinks <i>L</i>, and the emergence of cooperative behaviors in public goods games on hypergraphs is hindered with the value of <i>L</i> increasing. To some extent, these results are helpful in the better understanding of the evolutionary dynamics of the spatial public goods games on hypergraphs with social diversity.

Heterogeneous multiplication factors promote the evolution of cooperation in public goods game on hypergraphs

In recent years, the effects of spatial structure and social diversity on the evolution of cooperation have been attracted great attention. Both of them have been verified to play a nontrivial role in the emergence and maintenance of cooperation among selfish individuals. And most previous evolutionary game dynamics are based on pairwise interactions. However, interactions can often take place within groups of people in many real situations and cannot be described simply by dyads. While the dynamics of evolutionary games in systems with higher-order interactions have not yet been explored as deserved. In this paper, we introduce heterogeneous multiplication factors into the spatial public goods game to investigate the cooperative behaviors on the hypergraphs. In addition to the original model in which all groups have the same multiplication factor, three types of heterogeneous multiplication factor distributions including uniform, exponential and power-law distributions are considered. The numerical simulation results show that, the increase of the order $g$ of the uniform random hypergraphs is conducive to the emergence and prosperity of the individuals' cooperative behavior no matter what types of the distributions are. Furthermore, compared with the results of the original spatial public goods games on hypergraphs, the heterogeneous multiplication factors following three different distributions can remarkably promote the evolution of cooperation. In particular, for most ranges of the average rescaling multiplication factor $r_0$, the highest cooperation level can be obtained under the power-law distribution, while the uniform distribution induces to the lowest enhancement. We provide an explanation through investigating the number of cooperators in each group. In addition, to probe into the essence that influences the survival of cooperative behaviors, we have studied the time series of the fraction of groups with different numbers of cooperators. Besides, we also study the impact of the number of hyperlinks on the evolution of cooperation. We find that the results are robust against the number of hyperlinks $L$, and the emergence of cooperative behaviors in public goods games on hypergraphs is hindered with increasing of $L$. To some extent, these results are helpful for better understanding the evolutionary dynamics of the spatial public goods games on hypergraphs with social diversity.

Reciprocal rewards promote the evolution of cooperation in spatial prisoner's dilemma game

To investigate the incentive mechanisms of cooperation, we propose an other-regarding preference based reciprocal rewarding mechanism by incorporating the dynamic adjustment of link weight into the spatial prisoner's dilemma game. Under the influence of other-regarding preference, players are willing to sacrifice personal payoff to reward the cooperative neighbor who has the greatest link weight. The reciprocal reward is dynamically adjusted according to the adaptive adjustment of link weight and the intensity of other-regarding preference. Extensive simulations reveal that the proposed rewarding mechanism significantly promotes the evolution of cooperation. Both the dynamic adjustment of link weight and the other-regarding preference impact factor play an essential role in the emergence of cooperative behavior. To further validate the above outcome, the evolution of cooperators' density and the alteration of average payoff and fitness are explored in a microscopic perspective.

Reputational preference and other-regarding preference based rewarding mechanism promotes cooperation in spatial social dilemmas* *Project supported by the National Natural Science Foundation of China (Grant No. 62062049), the Social Science Project of the Ministry of Education of China (Grant No. 20YJCZH212), and the Natural Science Foundation of Gansu Province, China (Grant No. 20JR5RA390).

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.

Toward Understanding the Interplay between Public and Private Healthcare Providers and Patients: An Agent-based Simulation Approach

Few modelling studies have been carried out to investigate patients’ involvement in the decision-making process in a healthcare system. Here we perform theoretical and simulation analysis of a healthcare business model involving three populations: Public Healthcare Providers, Private Healthcare Providers and Patients. The analysis contributes to healthcare economic modelling by analyzing the dynamics and emergence of cooperative behavior of agents within the three populations. Resorting to agent-based simulations, we examine the effect of increasing behavioural mutation and providers’ capacity on patients’ cooperative behaviour. We show that the former introduces more randomness in agents’ behaviors enabling cooperation to emerge in more difficult conditions. Moreover, when the providers’ capacity to meet patients’ demand is limited, patients exhibit low levels of cooperation, implying a more difficult cooperation dilemma in a healthcare system that needs addressing.

Helping, fast and slow: Exploring intuitive cooperation in early ontogeny

Cooperative behavior is central to human societies. Human adults who reach their cooperative decisions more rapidly and independently of cognitive control display greater levels of prosocial behavior. This is taken to show that cooperation is guided by intuitive processes rather than by active control of selfish impulses. The current study investigated the emergence of intuitive cooperation in early human ontogeny. We measured helping behavior (latency and frequency) in a longitudinal sample of infants at ages 14 and 18 months. Between 14 and 18 months, the frequency of helping significantly increased and latency to help significantly decreased, suggesting advances in helping behavior during this period of development. Moreover, at 18 months and to some extent, even at 14 months, infants who helped more rapidly (as indexed by a shorter latency) acted more prosocially (as indexed by a greater frequency of helping) than infants who were slower to help. This link between latency and frequency of prosocial behavior was independent of infants' ability for inhibitory control and general sociability levels. Prosocial behavior thus begins to be governed by intuitive processes that operate independently of cognitive control early in human ontogeny. This informs our understanding of the nature and emergence of cooperative behavior by supporting accounts that assign a central role to intuition in the evolution of human cooperation.

Experiment research on the evolution of cooperation and network game theory

<p indent=0mm>Focusing on the mechanisms of the evolution and emergence of cooperative behavior, this paper reviews the recent development of direct reciprocity and network reciprocity from perspectives of both theoretical and experimental research based on two-player Prisoner’s Dilemma and multi-player Public Goods games. Repeated interactions provide the necessary condition for the emergence of direct reciprocity. The zero-determinant strategy, which is a new theory in direct reciprocity, has been deeply investigated from both theory and experiment. The zero-determinant strategy is a kind of memory-one strategies, which can unilaterally determine the payoff relationship in repeated games and may provide a novel tool to explore human decision behavior. Investigations on networked game experiments revealed that dynamic networks can facilitate and maintain human cooperation, while the influence of static networks on cooperation remains controversial. Furthermore, it is summarized that networked game experiments provide an efficient approach to studying the formation mechanism of network structure and the emergence of cooperation in complex systems.

The role of multiplex network structure in cooperation through generalized reciprocity

Recent studies suggest that the emergence of cooperative behavior can be explained by generalized reciprocity, a behavioral mechanism based on the principle of “help anyone if helped by someone”. In complex systems, the cooperative dynamics is largely determined by the network structure which dictates the interactions among neighboring individuals. These interactions often exhibit multidimensional features, either as relationships of different types or temporal dynamics, both of which may be modeled as a “multiplex” network. Against this background, here we advance the research on cooperation models inspired by generalized reciprocity by considering a multidimensional networked society. Our results reveal that a multiplex network structure may enhance the role of generalized reciprocity in promoting cooperation, whereby some of the network dimensions act as a latent support for the others. As a result, generalized reciprocity forces the cooperative contributions of the individuals to concentrate in the dimension which is most favorable for the existence of cooperation.

Temporal cooperativity of motor proteins under constant force: insights from Kramers’ escape problem

The microtubule-bound motors kinesin and dynein differ in many respects, a striking difference being that while kinesin is known to function mostly alone, dynein operates in large groups, much like myosinV in actin. Optical tweezer experiments in vitro have shown that the mean detachment time of a bead attached to kinesins under stall conditions is a slowly decreasing function of , while for dyneins, the time increases almost linearly with . This makes dynein a team worker, capable of producing and sustaining a large collective force without detaching. We characterize this phenomenon as ‘temporal cooperativity’ under load. In general, it is unclear which biophysical properties of a single motor determine whether it behaves cooperatively or not in a group. Our theoretical analysis shows that this is determined by two dimensionless parameters: (i) the ratio of single molecule, load-independent detachment and attachment rates and (ii) the ratio of the applied force per motor to the detachment force of a single motor. We show that the attachment-detachment dynamics of a motor assembly may be mapped to the motion of a hypothetical, overdamped Brownian particle in an effective potential, the form of which depends on the load-dependence of binding and unbinding rate of the motor. In this picture, the total number of motors is proportional to the inverse temperature and cooperative behaviour arises from the trapping of the particle in the minima of the potential, when present. In the latter case, application of results from Kramers’ theory predicts that the mean time of escape of the particle, equivalent to the mean detachment time of the bead under stall, increases exponentially with the number of motors, indicating cooperative behaviour. If the potential does not have minima, the detachment time depends only weakly on , which suggests non-cooperative behaviour. In the large limit, the emergence of cooperative behaviour is shown to be similar to a continuous phase transition.

Effect of strategy-assortativity on investor sharing games in the market

An investor sharing game is proposed in a heterogeneous population which is divided into two kinds of groups, such as weak-power investors and strong-power investors. Economically, the degree of monopoly in the market is introduced in the game. Measuring the strategy-assortativity, the probability p decides what kind of investors’ partner in the game. We show that there are three kinds of equilibrium states in the process. In equilibrium states, all of weak-power investors cooperate while the density of strong-power cooperators depends on p. Social welfare is highest if every one cooperates. In order to promote cooperation in strong-power groups, higher strategy-assortativity is needed in a more monopolized market. Interestingly, given the degree of monopoly, when the ratio of strong-power investor’s amount to weak-power investor’s amount rises, the emergence of cooperative behaviors in strong-power groups only requires a lower p.

Environmental mismatch results in emergence of cooperative behavior in a passerine bird

A major problem in the evolution of maternal effects is explaining the origin and persistence of maternally induced phenotypes that lower offspring fitness. Recent work focuses on the relative importance of maternal and offspring selective environments and the mismatch between them. However, an alternative approach is to directly study the origin and performance of offspring phenotypes resulting from mismatch. Here, we capitalize on a detailed understanding of the ecological contexts that provide both the cue and the functional context for expression of maternally induced offspring phenotypes to investigate the consequences of environmental mismatch. In western bluebirds, adaptive integration of offspring dispersal and aggression is induced by maternal competition over nest cavities. When nest cavities are locally abundant, mothers produce nonaggressive offspring that remain in their natal population, and when nest cavities are scarce, mothers produce aggressive dispersers. However, a few offspring neither disperse nor breed locally, instead helping at their parent’s nest, and as a result these offspring have unusually low fitness. Here, we investigate whether females produce helpers to increase their own fitness, or whether helpers result from a mismatch between the cues mothers experience during offspring production and the breeding environment that helpers later encounter. We found that producing helpers does not enhance maternal fitness. Instead, we show that helpers, which were the least aggressive of all returning sons in the population, were most common when population density increased from the time sons were produced to the time of their reproductive maturity, suggesting that the helper phenotype emerges when cues of resource competition during offspring development do not match the actual level of competition that offspring experience. Thus, environmental mismatch might explain the puzzling persistence of maternally induced phenotypes that decrease offspring fitness.

Swarm Robotic Systems Based on Collective Behavior of Chloroplasts

[abstFig src='/00290003/15.jpg' width='300' text='Swarming chloroplastic robots around light source' ] In this paper, distributed autonomous robots are used to perform area coverage tasks. In order for robots to cover the ground surface of environments, the coordination of a team of robots is a challenge. For this challenge, we present bio-inspired swarm robotic systems. We focus on the collective behavior of chloroplasts toward a light source. On the basis of the mechanism of the chloroplast, we propose robot behavior models that do not use local communication. The emergence of cooperative behavior through the interaction among the swarming robots is a main contribution of this paper. Based on simulation results, the effectiveness of the chloroplastic robots for the coverage task is discussed in terms of flexibility and scalability. Furthermore, the behavioral models are applied to actual mobile robots. Based on the results of experiments, the applicability of the chloroplastic robots to real environments is discussed. As an application of the swarm robotic system, a specific task, sweeping, is given to actual chloroplastic robots.

群ロボットにおける場の役割

群ロボットにおける場の役割

Coevolution of Cooperation and Layer Selection Strategy in Multiplex Networks

Recently, the emergent dynamics in multiplex networks, composed of layers of multiple networks, has been discussed extensively in network sciences. However, little is still known about whether and how the evolution of strategy for selecting a layer to participate in can contribute to the emergence of cooperative behaviors in multiplex networks of social interactions. To investigate these issues, we constructed a coevolutionary model of cooperation and layer selection strategies in which each an individual selects one layer from multiple layers of social networks and plays the Prisoner’s Dilemma with neighbors in the selected layer. We found that the proportion of cooperative strategies increased with increasing the number of layers regardless of the degree of dilemma, and this increase occurred due to a cyclic coevolution process of game strategies and layer selection strategies. We also showed that the heterogeneity of links among layers is a key factor for multiplex networks to facilitate the evolution of cooperation, and such positive effects on cooperation were observed regardless of the difference in the stochastic properties of network topologies.

Network Modularity is essential for evolution of cooperation under uncertainty.

Cooperative behavior, which pervades nature, can be significantly enhanced when agents interact in a structured rather than random way; however, the key structural factors that affect cooperation are not well understood. Moreover, the role structure plays with cooperation has largely been studied through observing overall cooperation rather than the underlying components that together shape cooperative behavior. In this paper we address these two problems by first applying evolutionary games to a wide range of networks, where agents play the Prisoner's Dilemma with a three-component stochastic strategy, and then analyzing agent-based simulation results using principal component analysis. With these methods we study the evolution of trust, reciprocity and forgiveness as a function of several structural parameters. This work demonstrates that community structure, represented by network modularity, among all the tested structural parameters, has the most significant impact on the emergence of cooperative behavior, with forgiveness showing the largest sensitivity to community structure. We also show that increased community structure reduces the dispersion of trust and forgiveness, thereby reducing the network-level uncertainties for these two components; graph transitivity and degree also significantly influence the evolutionary dynamics of the population and the diversity of strategies at equilibrium.

An Evolutionary Model of Price Competition Among Spatially Distributed Firms

Various studies have shown the emergence of cooperative behavior in evolutionary models with repeated local interaction among spatially distributed agents. We investigate to what extent these findings generalize to evolutionary models of price competition among spatially distributed firms. We consider both one- and two-dimensional models, and we vary the amount of information firms have about competitors in their neighborhood. Our computer simulations show that the emergence of cooperative behavior depends strongly on the amount of information available to firms. Firms tend to behave most cooperatively if they have only a very limited amount of information about their competitors. We provide an intuitive explanation for this phenomenon. Our simulations further indicate that three other factors in our models, namely the accuracy of firms' information, the probability of experimentation, and the spatial distribution of consumers, have little effect on the emergence of cooperative behavior.

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.

Effects of heterogeneous wealth distribution on public cooperation with collective risk

The distribution of wealth among individuals in real society can be well described by the Pareto principle or "80-20 rule." How does such heterogeneity in initial wealth distribution affect the emergence of public cooperation, when individuals, the rich and the poor, engage in a collective-risk enterprise, not to gain a profit but to avoid a potential loss? Here we address this issue by studying a simple but effective model based on threshold public goods games. We analyze the evolutionary dynamics for two distinct scenarios, respectively: one with fair sharers versus defectors and the other with altruists versus defectors. For both scenarios, particularly, we in detail study the dynamics of the population with dichotomic initial wealth-the rich versus the poor. Moreover, we demonstrate the possible steady compositions of the population and provide the conditions for stability of these steady states. We prove that in a population with heterogeneous wealth distribution, richer individuals are more likely to cooperate than poorer ones. Participants with lower initial wealth may choose to cooperate only if all players richer than them are cooperators. The emergence of pubic cooperation largely relies on rich individuals. Furthermore, whenever the wealth gap between the rich and the poor is sufficiently large, cooperation of a few rich individuals can substantially elevate the overall level of social cooperation, which is in line with the well-known Pareto principle. Our work may offer an insight into the emergence of cooperative behavior in real social situations where heterogeneous distribution of wealth among individual is omnipresent.