Network Reciprocity Research Articles

Peer pressure induced punishment resolves social dilemma on interdependent networks

Despite the fruitful evidence to support the emergence of cooperation, irrational decisions are still an essential part of promoting cooperation. Among the many factors that affect human rational decision-making, peer pressure is unique to social organisms and directly affects individual cooperative behaviors in the process of social interaction. This kind of pressure psychologically forces individuals to behave consistently with their partners, and partners with inconsistent behaviors may suffer psychological blows. As feedback, this psychological harm may in turn affect individual cooperative decisions. There is evidence that when peer pressure exists, partnerships can reduce free-riding in enterprise. Based on interdependent networks, this paper studies the impact of peer pressure on cooperation dynamics when the strategies of corresponding partners from different layers of the networks are inconsistent. We assume that when individuals are under peer pressure, their payoffs will be compromised. The simulation results show that the punishment effect will force the expulsion of partners with different strategies, which will further reduce the proportion of partners with inconsistent strategies in the system. However, in most cases, only moderate fines are most conductive to the evolution of cooperation, and the punishment mechanisms can effectively promote the interdependent network reciprocity. The results on the small world and random network prove the robustness of the result. In addition, under this mechanism, the greater the payoff dependence between partners, the better the effect of interdependent network reciprocity.

Structure and Reciprocity in Technology-Centered Q&A Communities

In this paper we examine the network structure of the MythTV mailing list, an online technology Q&A user community, and we use time-series analysis techniques to study users’ reciprocity behavior in this community. We find that the amount of help users provide is strongly correlated to the amount of help they receive. Further, by conducting the Granger Causality test on the time series data of active users’ activity, we find that the amount of help given is actually the reason why one gets a lot of help. This finding corresponds to the concept of directed reciprocity in social networks and provides insights into social dynamics in technology-centered online communities.

Effects of emotion on the evolution of cooperation in a spatial prisoner’s dilemma game

Emotion emerges along with individuals’ interactions, and numerous experimental studies have shown that emotion plays an important role in individual decision making. However, how emotion affects the evolution of cooperation in structured population is largely unknown. Here we introduce emotion into network reciprocity, where emotion is considered quantifiable and cumulative, and then divide individuals into two types, namely non-competitive individuals whose social value orientation is mutually beneficial and competitive individuals whose social value orientation is maximizing outcomes for the self. Here, we explore the effects of the proportion of non-competitive individuals and emotional cumulative length on the evolution of cooperation. Simulation results show that the existence of non-competitive individuals promotes cooperation in the system. Furthermore, we also find that emotional cumulative length plays a key role in promoting overall cooperation, but surprisingly, the cooperation rate of competitive individuals peaks at an intermediate value of emotional cumulative length.

Social capital and income of cocoa farmers in peta village, Sendana District, Palopo City, South Sulawesi Province

Social capital has been tested by history as an important mechanism in achieving economic growth and equity in society. In terms of farmers’ income, one that can be used as an alternative in changing the farmer’s standard of living is through social capital. There are three elements of social capital that can be examined in this study, namely the element of trust, the element of network and the element of reciprocity. Research on the relationship between social capital and income of cocoa farmers aims to determine how strong the relationship between the three elements of social capital is to the income of cocoa farmers. This research was conducted from December 2018 to March 2019 in Peta Village, Sendana District, Palopo City, South Sulawesi Province. This location was determined by purposive sampling. The results of the study found that the recapitulation of the elements of social capital, trust, network and reciprocity of cocoa farmers were in the high category, with the income of cocoa farmers in the high category. For the relationship between social capital and income of cocoa farmers, the trust element has a sufficient coefficient value, while the network and reciprocity elements have a weak coefficient value. The relationship between social capital and income of cocoa farmers for the element of trust is in the quite significant category, while for the network elements and reciprocity on the income of cocoa farmers, it is in the weak category.

Quantifying Vote Trading Through Network Reciprocity

Building on the concept of reciprocity in directed weighted networks, we propose a framework to study legislative vote trading. We first discuss the conditions to quantify vote trading empirically. We then illustrate how a simple empirical framework--complementary to existing approaches--can facilitate the discovery and measurement of vote trading in roll-call data. The application of the suggested procedure preserves the micro-structure of trades between individual legislators, shedding light on, so far, unstudied aspects of vote trading. Validation is provided via Monte Carlo simulation of the legislative process (with and without vote trading). Applications to two major studies in the field provide richer, yet consistent evidence on vote trading in US politics.

Generative model for reciprocity and community detection in networks

We present a probabilistic generative model and efficient algorithm to model reciprocity in directed networks. Unlike other methods that address this problem such as exponential random graphs, it assigns latent variables as community memberships to nodes and a reciprocity parameter to the whole network rather than fitting order statistics. It formalizes the assumption that a directed interaction is more likely to occur if an individual has already observed an interaction towards her. It provides a natural framework for relaxing the common assumption in network generative models of conditional independence between edges, and it can be used to perform inference tasks such as predicting the existence of an edge given the observation of an edge in the reverse direction. Inference is performed using an efficient expectation-maximization algorithm that exploits the sparsity of the network, leading to an efficient and scalable implementation. We illustrate these findings by analyzing synthetic and real data, including social networks, academic citations and the Erasmus student exchange program. Our method outperforms others in both predicting edges and generating networks that reflect the reciprocity values observed in real data, while at the same time inferring an underlying community structure. We provide an open-source implementation of the code online.

Fitness of others’ evaluation effect promotes cooperation in spatial public goods game* *Project supported by the National Natural Science Foundation of China (Grant Nos. 61673096 and 62076057), the Social Science Project of the Ministry of Education of China (Grant No. 16YJC630118), and the Project of Promoting Talents in Liaoning Province, China (Grant No. XLYC1807033).

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 scalefree network.

Evolutionary game on a growing multilayer network

Numerous complex systems display the multilayer feature rather than the single or isolated one. It is thus significant to study how these structures emerge and the mechanisms behind them. In this paper we propose a novel growing model to explain the formation of multilayer networks, where the network growth and strategy evolution in prisoner’s dilemma game interact and coevolve. We introduce the growing rate to control the ratio of strategy evolution to network growth, and the coupling strength to adjust the connecting density between layers. The simulation results show that besides the scale-free property observed in the resulting networks, the coupling strength is significant to cooperative behaviors. Under the weak coupling strength, the role of network reciprocity works and the resulting networks can preserve a relatively high cooperation level; however, with the increase of coupling strength, the role of network reciprocity fades away or disappears. The strong coupling strength, denoting individuals can obtain more extra payoff from their external circumstance, induces a low cooperation level. In addition, the growing rate also influences cooperative behaviors and a fast growing rate is beneficial to cooperation.

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis.

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

Social network stability is impacted by removing a dominant male in replicate dominance hierarchies of a cichlid fish

Individuals with high social rank within a dominance hierarchy often have priority access to resources relative to subordinate individuals, but these rank-dependent fitness effects may depend on the stability of the social hierarchy. Here we studied temporal changes in network structure and social relationships in experimentally perturbed social hierarchies of the cichlid Astatotilapia burtoni . By removing a dominant male in replicate groups, we triggered more status changes compared to control, subordinate male removal groups. At the individual level, we found that dominant male removal resulted in dominant males significantly increasing the rate of chases (‘chase strength’), but there was no significant increase in the rate of display (‘display strength’). Dominant male removal also led to several changes at the group level: network reciprocity and network stability decreased in response to dominant male removal, while network density was not affected by our treatment. However, in contrast to the individual level network measures, these group level effects were present in display networks but not in chase networks. Display networks mostly connect dominant males within the network, making display networks more sensitive to changes in the hierarchy. Together, our results provide novel insights into complex social dynamics in experimentally altered social dominance networks. • Cichlid fish form dominance hierarchies in which males fight for territories. • In groups of cichlid fish, we vacated a territory by removing a territorial male. • This created social instability and increased chase behaviour. • However, the instability was linked to changes in the display network only. • This is because display networks strongly connect territorial males in the network.

Effects of defensive cooperation strategy on the evolution of cooperation in social dilemma

Punishment and reward are effective ways to sustain cooperation among selfish groups. Researches show that the ultimate result of selflessness is self-interest. This paper considers that individuals take defensive measures to reduce losses in the face of unknown risks. Defensive cooperators need to pay the defensive cost. When they interact with unconditional defectors, the defensive cooperators gain benefit due to defensive behavior, while the unconditional defectors lose some benefit due to the defense of defensive cooperators. Results show that in the well-mixed population, reducing defensive cost and improving defensive benefit can promote cooperation, and maintain a higher cooperation level unchanged within a larger range of temptation. In the structured population, the three strategies perform the phenomenon of cyclic dominance, that is unconditional cooperators dominate defensive cooperators who dominate unconditional defectors who dominate unconditional cooperators. It is worth noting that both in the well-mixed population and structured population, three strategies have obvious phased advantages with regard to defensive benefit. Lower defensive benefit leads to an advantage for unconditional defectors. If defensive benefit is moderate, the weakened competitiveness of unconditional defectors leads defensive cooperators dominate. And excessive defensive benefit results in the second-order free-rider phenomenon of the unconditional cooperation strategy. In addition, structured population do not always support cooperation better than well-mixed population. When the defensive cost is small and defensive benefit is high, the spatial effect can enhance network reciprocity. More generally, our results prove that defensive self-interested behavior leads to the maintenance of cooperation, which provides a new perspective for understanding the cooperation evolutionary.

Dynamic aspiration based on Win-Stay-Lose-Learn rule in spatial prisoner's dilemma game.

Prisoner's dilemma game is the most commonly used model of spatial evolutionary game which is considered as a paradigm to portray competition among selfish individuals. In recent years, Win-Stay-Lose-Learn, a strategy updating rule base on aspiration, has been proved to be an effective model to promote cooperation in spatial prisoner's dilemma game, which leads aspiration to receive lots of attention. In this paper, according to Expected Value Theory and Achievement Motivation Theory, we propose a dynamic aspiration model based on Win-Stay-Lose-Learn rule in which individual's aspiration is inspired by its payoff. It is found that dynamic aspiration has a significant impact on the evolution process, and different initial aspirations lead to different results, which are called Stable Coexistence under Low Aspiration, Dependent Coexistence under Moderate aspiration and Defection Explosion under High Aspiration respectively. Furthermore, a deep analysis is performed on the local structures which cause defectors' re-expansion, the concept of END- and EXP-periods are used to justify the mechanism of network reciprocity in view of time-evolution, typical feature nodes for defectors' re-expansion called Infectors, Infected nodes and High-risk cooperators respectively are found. Compared to fixed aspiration model, dynamic aspiration introduces a more satisfactory explanation on population evolution laws and can promote deeper comprehension for the principle of prisoner's dilemma.

Trust, reciprocity and reputation in informal networks in post-Soviet Russia

Trust, reciprocity and reputation in informal networks in post-Soviet Russia

Reputation preferences resolve social dilemmas in spatial multigames

Heterogeneity and difference in the dynamics of individual reputation may strongly affect learning behavior, and hence also the evolution of cooperation within a population. Motivated by this, we propose here an evolutionary spatial multigames model, wherein the reputation of an individual increases if they cooperate and decreases if they defect. After the payoffs are determined, individuals with a higher reputation will be more likely to act as strategy sources for other individuals. We perform systematic Monte Carlo simulations to determine the transitions between cooperation and defection, as well as the parameter regions of strategic coexistence. We show that preferential learning, based on dynamic reputation changes, strongly promotes cooperation regardless of the interaction network’s structure. The mechanism responsible for more favorable evolutionary outcomes is enhanced network reciprocity, which leads to more compact cooperator clusters and thus to more robust spatiotemporal dynamics that are resilient to invading defectors. Our research may improve the understanding of selection patterns that favor the emergence and persistence of cooperative behavior.

Evolution of cooperative study

Associate Professor Isamu Okada is based at the Department of Business Administration, Faculty of Business Administration, Soka University in Japan, as well as a visiting professor of Department of Information Systems and Operations, Vienna University of Economics and Business in Austria. Okada has dedicated his career to understanding more about the evolution of cooperation which is a strand of thought that falls under evolutionary biology. Academics around the world have long considered the issues relating to the evolution of cooperation. In these studies, cooperation is taken to mean providing benefits to others by paying some kind of cost, whether that be money, time, effort, etc. One of the most fascinating aspects of the theory is that rational thought holds a person has no incentive to cooperate. Indeed, despite decades of research and huge numbers of studies, a rational reason for cooperating has still not been cultivated properly. One of the mechanisms that lie behind cooperation are known as reciprocity and there are many different types. Three specific types have been studied in great detail; direct reciprocity, indirect reciprocity and network reciprocity. Okada's team has conducted investigations that shine new light on indirect reciprocity which could open up new directions for the field of evolutionary biology.

Evolution of cooperation in the spatial prisoner’s dilemma game with extortion strategy under win-stay-lose-move rule

Abstract Extortion strategy and mobility have both been studied separately, and their roles for the evolution of cooperation are well known. In this paper, we combine these two mechanisms and explore the effects of one mechanism on another, especially for how spatial exclusion influences the dynamics. The model incorporates migration into the prisoner’s dilemma game with extortion and allows agents to change their spatial position governed by win-stay-lose-move rule. By means of Monte Carlo simulations, we show that when the population density is intermediate (neither too high or too low), empty sites weaken the cooperation-extortion alliance and allow cooperators to form compact clusters through migration, which then enhance network reciprocity in the populations. In this way, cooperation can be maintained in the structured populations with mobility, we thus provide a deeper understanding for the evolution of cooperation.

Diversity of interaction intensity enhances the cooperation of spatial multi-games on interdependent lattices

We put forward a novel multi-games model based on the diversity of interaction intensity under the framework of interdependent lattices, where an adjustable variable ω is accustomed to regulate the interaction intensity between players. In detail, when the payoff of the focal player x gaining from his neighbor y is not less than the average payoff of the focal player x from four nearest neighbors, the interaction intensity between them is strengthened; otherwise, this intensity is weakened. In addition, the utility of players is mainly determined by their own payoff and also related to the payoff of that player occupying the same position on another lattice. Through extensive simulations, the results indicate that the diversity of interaction intensity between players can immensely favor the evolution of cooperation. The smaller ω means the stronger diversity of interaction intensity, which leads to the higher level of collective cooperation. Particularly, when the interaction intensity and the sucker's payoff are changed continuously, the cooperative behavior among individuals tends to become more complicated than that only driven by the network reciprocity. We also find that when the diversity is weak (ω>0.5), the utility coupling factor α does not seem to be a decisive element in facilitating the development of cooperation. However, when the diversity is strong (ω<0.5), the promotion of cooperation becomes more remarkable. Our work also illustrates the pivotal role of diversity of interaction intensity in solving complex social dilemmas, which will inspire us to comprehend the cooperation behavior in numerous real-world systems.

A limited mobility of minorities facilitates cooperation in social dilemmas

Individuals often move to distance themselves from defectors, or to seek better chances for higher payoffs, for example moving from rural to urban areas. Regardless of the reason, however, moving frequently also means alienation, which in turn means bearing costs for seeking new opportunities. With this motivation, we study a prisoner’s dilemma game, where individuals with defectors in their communities either move or update their strategy. We find that the alienation from defectors reinforces larger and more compact cooperative clusters. However, the number of cooperative clusters depends on the viscosity of the interaction network, where network reciprocity still works well. And it is the fine-tuned interplay between the mobility to alienate from defectors and a still functioning network reciprocity that works best in promoting cooperation. Our results suggest that a limited mobility of minorities could spare public resources in social dilemma situations more effectively than reward and punishment.

Memory-based prisoner's dilemma game with history optimal strategy learning promotes cooperation on interdependent networks

Cooperation is regarded as the architect of the evolution process. The research on the influence of memory mechanism on human cooperative behavior has been a hot issue in recent years. However, most previous researchers usually concentrated their efforts on the single-layer networks and the impact of memory length on cooperative behavior, but paid less attention to the interdependent networks and other factors affecting the decision-making. In this paper, we presented a two-layer interdependent Holme-Kim network model with scale-free, high clustering and interconnection characteristics and a history optimal strategy learning mechanism fully considering the historical strategy, payoffs information and memory length, and combined them to discuss the emergence and maintenance of cooperation behavior. Through modeling simulation, we studied the parameter r for network construction, memory length M and defection to temptation T. Game participants, equipped with the same memory length, played Prisoner's dilemma games with their neighbors located on home or adjacent layer (if any), and selected their following strategies for the next game round through the history optimal strategy learning mechanism. The results show that density of cooperation is inversely related to parameter r and positively related to memory length M. The newly proposed network model can improve network reciprocity and the history optimal strategy learning mechanism can find out the strategy that can bring the maximum revenue in a certain period of time. The smaller r is, the larger M is, the more conductive to cooperation.

Mixing protocols in the public goods game.

If interaction partners in social dilemma games are not selected randomly from the population but are instead determined by a network of contacts, it has far reaching consequences for the evolutionary dynamics. Selecting partners randomly leads to a well-mixed population, where pattern formation is essentially impossible. This rules out important mechanisms that can facilitate cooperation, most notably network reciprocity. In contrast, if interactions are determined by a lattice or a network, then the population is said to be structured, where cooperators can form compact clusters that protect them from invading defectors. Between these two extremes, however, there is ample middle ground that can be brought about by the consideration of temporal networks, mobility, or other coevolutionary processes. The question that we here seek to answer is, when does mixing on a lattice actually lead to well-mixed conditions? To that effect, we use the public goods game on a square lattice, and we consider nearest-neighbor and random mixing with different frequencies, as well as a mix of both mixing protocols. Not surprisingly, we find that nearest-neighbor mixing requires a higher frequency than random mixing to arrive at the well-mixed limit. The differences between the two mixing protocols are most expressed at intermediate mixing frequencies, whilst at very low and very high mixing frequencies the two almost converge. We also find a near universal exponential growth of the average size of cooperator clusters as their fraction increases from zero to one, regardless of whether this increase is due to increasing the multiplication factor of the public goods, decreasing the frequency of mixing, or gradually shifting the mixing from random to nearest neighbors.