Heterogeneous Coupling between Interdependent Lattices Promotes the Cooperation in the Prisoner's Dilemma Game

Crash dynamics of interdependent networks

The emergence and evolution of real-world systems have been extensively studied in the last few years. However, equally important phenomena are related to the dynamics of systems' collapse, which has been less explored, especially when they can be cast into interdependent systems. In this paper, we develop a dynamical model that allows scrutinizing the collapse of systems composed of two interdependent networks. Specifically, we explore the dynamics of the system's collapse under two scenarios: in the first one, the condition for failure should be satisfied for the focal node as well as for its corresponding node in the other network; while in the second one, it is enough that failure of one of the nodes occurs in either of the two networks. We report extensive numerical simulations of the dynamics performed in different setups of interdependent networks, and analyze how the system behavior depends on the previous scenarios as well as on the topology of the interdependent system. Our results can provide valuable insights into the crashing dynamics and evolutionary properties of interdependent complex systems.

Individual diversity between interdependent networks promotes the evolution of cooperation by means of mixed coupling

Along with the rapid development of network-based information technology, such as cloud computing, big data, the IoT, and so on, human society has stepped into a new era of complex networks. People's life and production activities depend more and more on various complex networks to ensure security and reliability. The complex interrelationships between human and nature establish a link to explain the cooperation of individual behaviour, especially for individual diversity. However, existing researches mostly ignore the influence of individual diversity on networks involved in individual behaviour to strategy selection. Therefore, it needs further research on how to consider both individual diversity and independent networks in the evolution of cooperative behaviour. To address this issue, we extend a simple game model into the interdependent networks through the mixed coupling (i.e., utility and probability) in this work. Also, we divide the kinds of strategic behaviour of a player in one layer concerning individual diversity. Moreover, there exists an optimal region of mixed coupling between networks such that cooperation can be promoted. Finally, experimental results can open the path to understanding the emergence and maintenance of cooperation within various interconnected and interrelated real-world systems newly.

Heterogeneous indirect reciprocity promotes the evolution of cooperation in structured populations

A fundamental aspect of all biological system is cooperation. Human society is based to a large extent on mechanisms that favor cooperation. Indirect reciprocity is a crucial factor for the emergence and maintenance of cooperation in evolutionary games. In this work, we introduce a mechanism of heterogeneous indirect reciprocity in the prisoner's dilemma game, where an altruistic attribute is considered. The so-called altruistic attribute refers to the trait that when an altruistic individual cooperates, its neighbors, regardless of their strategies, can gain additional benefits. Intuitively, altruistic cooperative agents seem to do only what is beneficial for others, but in fact, their neighbors tend to cooperate in order to maintain the cooperative strategies of altruistic individuals. In this way, the neighbors of cooperative altruistic individuals not only guarantee their own additional benefits, but also indirectly protect the income of altruistic individuals, which strengthens the links between cooperative individuals, thus promoting the evolution of cooperation. The robustness of the results is verified on homogeneous and heterogeneous networks. Moreover, the results of individual heterogeneity corroborate the existing evidence that heterogeneity, almost irrespective of its origin, promotes cooperative actions. Our conclusions might provide additional insights into understanding the roots of cooperation in social systems.

Environment-based preference selection promotes cooperation in spatial prisoner's dilemma game

The impact of environment on individuals is particularly critical. In evolutionary games, adopting the strategy of the neighbor who performs better is nontrivial for the survival and maintenance of cooperation, in that such an action may help the agents to obtain higher benefit and more obvious evolutionary advantages. Inspired by this idea, we investigate the effect of the environment-based preference selection on the evolution of cooperation in spatial prisoner’s dilemma. A simple rule, incorporating individual preference selection via an adjustable parameter α to explore how the selection of the potential strategy sources influences individual behavior traits, is considered. Because social interaction may not be the only way of generating payoffs, we assume that the individual’s income is also affected by the environment. Besides, taking into account individual differences, we introduce the heterogeneity of the environment. Through numerous computing simulations, we find that environment-based preference selection, which accelerates the microscopic organization of cooperator clusters to resist the aggression of defectors, can truly promote cooperation within a large range of parameters. Our study indicates that the combination of heterogeneity and preference selection may be key for the sustainability of cooperation in structured populations.

Coevolution of teaching ability and cooperation in spatial evolutionary games

Individuals with higher reputation are able to spread their social strategies easily. At the same time, one’s reputation is changing according to his previous behaviors, which leads to completely different teaching abilities for players. To explore the effect of the teaching ability influenced by reputation, we consider a coevolutionary model in which the reputation score affects the updating rule in spatial evolutionary games. More precisely, the updating probability becomes bigger if his/her partner has a positive reputation. Otherwise, the updating probability becomes smaller. This simple design describes the influence of teaching ability on strategy adoption effectively. Numerical results focus on the proportion of cooperation under different levels of the amplitude of change of reputation and the range of reputation. For this dynamics, the fraction of cooperators presents a growth trend within a wide range of parameters. In addition, to validate the generality of this mechanism, we also employ the snowdrift game. Moreover, the evolution of cooperation on Erdős-Rényi random graph is studied for the prisoner’s dilemma game. Our results may be conducive to understanding the emergence and sustainability of cooperation during the strategy adoptions in reality.

Finding overlapping communities in multilayer networks

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks.

Evolution of Cooperation with Heterogeneous Conditional Cooperators

Conditional cooperation declines over time if heterogeneous ideal conditional agents are involved in repeated interactions. With strict assumptions of rationality and a population consisting of ideal conditional agents who strictly follow a decision rule, cooperation is not expected. However, cooperation is commonly observed in human societies. Hence, we propose a novel evolutionary agent-based model where agents rely on social information. Each agent interacts only once either as a donor or as a receiver. In our model, the population consists of either non-ideal or ideal heterogeneous conditional agents. Their donation decisions are stochastically based on the comparison between the number of donations in the group and their conditional cooperative criterion value. Non-ideal agents occasionally cooperate even if the conditional rule of the agent is not satisfied. The stochastic decision and selection rules are controlled with decision intensity and selection intensity, respectively. The simulations show that high levels of cooperation (more than 90%) are established in the population with non-ideal agents for a particular range of parameter values. The emergence of cooperation needs non-ideal agents and a heterogeneous population. The current model differs from existing models by relying on social information and not on individual agent's prior history of cooperation.

From rationality to cooperativeness: The totally mixed Nash equilibrium in Markov strategies in the iterated Prisoner's Dilemma

In this research, the social behavior of the participants in a Prisoner's Dilemma laboratory game is explained on the basis of the quantal response equilibrium concept and the representation of the game in Markov strategies. In previous research, we demonstrated that social interaction during the experiment has a positive influence on cooperation, trust, and gratefulness. This research shows that the quantal response equilibrium concept agrees only with the results of experiments on cooperation in Prisoner’s Dilemma prior to social interaction. However, quantal response equilibrium does not explain of participants’ behavior after social interaction. As an alternative theoretical approach, an examination was conducted of iterated Prisoner's Dilemma game in Markov strategies. We built a totally mixed Nash equilibrium in this game; the equilibrium agrees with the results of the experiments both before and after social interaction.

Intermediate Levels of Network Heterogeneity Provide the Best Evolutionary Outcomes

Complex networks impact the diffusion of ideas and innovations, the formation of opinions, and the evolution of cooperative behavior. In this context, heterogeneous structures have been shown to generate a coordination-like dynamics that drives a population towards a monomorphic state. In contrast, homogeneous networks tend to result in a stable co-existence of multiple traits in the population. These conclusions have been reached through the analysis of networks with either very high or very low levels of degree heterogeneity. In this paper, we use methods from Evolutionary Game Theory to explore how different levels of degree heterogeneity impact the fate of cooperation in structured populations whose individuals face the Prisoner’s Dilemma. Our results suggest that in large networks a minimum level of heterogeneity is necessary for a society to become evolutionary viable. Moreover, there is an optimal range of heterogeneity levels that maximize the resilience of the society facing an increasing number of social dilemmas. Finally, as the level of degree heterogeneity increases, the evolutionary dominance of either cooperators or defectors in a society increasingly depends on the initial state of a few influential individuals. Our findings imply that neither very unequal nor very equal societies offer the best evolutionary outcome.

Data sharing as social dilemma: Influence of the researcher's personality

It is widely acknowledged that data sharing has great potential for scientific progress. However, so far making data available has little impact on a researcher’s reputation. Thus, data sharing can be conceptualized as a social dilemma. In the presented study we investigated the influence of the researcher's personality within the social dilemma of data sharing. The theoretical background was the appropriateness framework. We conducted a survey among 1564 researchers about data sharing, which also included standardized questions on selected personality factors, namely the so-called Big Five, Machiavellianism and social desirability. Using regression analysis, we investigated how these personality domains relate to four groups of dependent variables: attitudes towards data sharing, the importance of factors that might foster or hinder data sharing, the willingness to share data, and actual data sharing. Our analyses showed the predictive value of personality for all four groups of dependent variables. However, there was not a global consistent pattern of influence, but rather different compositions of effects. Our results indicate that the implications of data sharing are dependent on age, gender, and personality. In order to foster data sharing, it seems advantageous to provide more personal incentives and to address the researchers’ individual responsibility.

Impact of interaction style and degree on the evolution of cooperation on Barabási-Albert scale-free network

In this work, we study an evolutionary prisoner's dilemma game (PDG) on Barabási-Albert scale-free networks with limited player interactions, and explore the effect of interaction style and degree on cooperation. The results show that high-degree preference interaction, namely the most applicable interaction in the real world, is less beneficial for emergence of cooperation on scale-free networks than random interaction. Besides, cooperation on scale-free networks is enhanced with the increase of interaction degree regardless whether the interaction is high-degree preference or random. If the interaction degree is very low, the cooperation level on scale-free networks is much lower than that on regular ring networks, which is against the common belief that scale-free networks must be more beneficial for cooperation. Our analysis indicates that the interaction relations, the strategy and the game payoff of high-connectivity players play important roles in the evolution of cooperation on scale-free networks. A certain number of interactions are necessary for scale-free networks to exhibit strong capability of facilitating cooperation. Our work provides important insight for members on how to interact with others in a social organization.

The socialization effect on decision making in the Prisoner's Dilemma game: An eye-tracking study

We used a mobile eye-tracking system (in the form of glasses) to study the characteristics of visual perception in decision making in the Prisoner's Dilemma game. In each experiment, one of the 12 participants was equipped with eye-tracking glasses. The experiment was conducted in three stages: an anonymous Individual Game stage against a randomly chosen partner (one of the 12 other participants of the experiment); a Socialization stage, in which the participants were divided into two groups; and a Group Game stage, in which the participants played with partners in the groups. After each round, the respondent received information about his or her personal score in the last round and the overall winner of the game at the moment. The study proves that eye-tracking systems can be used for studying the process of decision making and forecasting. The total viewing time and the time of fixation on areas corresponding to noncooperative decisions is related to the participants’ overall level of cooperation. The increase in the total viewing time and the time of fixation on the areas of noncooperative choice is due to a preference for noncooperative decisions and a decrease in the overall level of cooperation. The number of fixations on the group attributes is associated with group identity, but does not necessarily lead to cooperative behavior.

Social contagions on interdependent lattice networks

Although an increasing amount of research is being done on the dynamical processes on interdependent spatial networks, knowledge of how interdependent spatial networks influence the dynamics of social contagion in them is sparse. Here we present a novel non-Markovian social contagion model on interdependent spatial networks composed of two identical two-dimensional lattices. We compare the dynamics of social contagion on networks with different fractions of dependency links and find that the density of final recovered nodes increases as the number of dependency links is increased. We use a finite-size analysis method to identify the type of phase transition in the giant connected components (GCC) of the final adopted nodes and find that as we increase the fraction of dependency links, the phase transition switches from second-order to first-order. In strong interdependent spatial networks with abundant dependency links, increasing the fraction of initial adopted nodes can induce the switch from a first-order to second-order phase transition associated with social contagion dynamics. In networks with a small number of dependency links, the phase transition remains second-order. In addition, both the second-order and first-order phase transition points can be decreased by increasing the fraction of dependency links or the number of initially-adopted nodes.

Defending Against Advanced Persistent Threats Using Game-Theory

Advanced persistent threats (APT) combine a variety of different attack forms ranging from social engineering to technical exploits. The diversity and usual stealthiness of APT turns them into a central problem of contemporary practical system security, since information on attacks, the current system status or the attacker’s incentives is often vague, uncertain and in many cases even unavailable. Game theory is a natural approach to model the conflict between the attacker and the defender, and this work investigates a generalized class of matrix games as a risk mitigation tool for an advanced persistent threat (APT) defense. Unlike standard game and decision theory, our model is tailored to capture and handle the full uncertainty that is immanent to APTs, such as disagreement among qualitative expert risk assessments, unknown adversarial incentives and uncertainty about the current system state (in terms of how deeply the attacker may have penetrated into the system’s protective shells already). Practically, game-theoretic APT models can be derived straightforwardly from topological vulnerability analysis, together with risk assessments as they are done in common risk management standards like the ISO 31000 family. Theoretically, these models come with different properties than classical game theoretic models, whose technical solution presented in this work may be of independent interest.

Leading from the Centre: A Comprehensive Examination of the Relationship between Central Playing Positions and Leadership in Sport

RESEARCH AIMS

The present article provides a comprehensive examination of the relationship between playing position and leadership in sport. More particularly, it explores links between leadership and a player's interactional centrality-defined as the degree to which their playing position provides opportunities for interaction with other team members. This article examines this relationship across different leadership roles, team sex, and performance levels.

RESULTS

Study 1 (N = 4443) shows that athlete leaders (and the task and motivational leader in particular) are more likely than other team members to occupy interactionally central positions in a team. Players with high interactional centrality were also perceived to be better leaders than those with low interactional centrality. Study 2 (N = 308) established this link for leadership in general, while Study 3 (N = 267) and Study 4 (N = 776) revealed that the same was true for task, motivational, and external leadership. This relationship is attenuated in sports where an interactionally central position confers limited interactional advantages. In other words, the observed patterns were strongest in sports that are played on a large field with relatively fixed positions (e.g., soccer), while being weaker in sports that are played on a smaller field where players switch positions dynamically (e.g., basketball, ice hockey). Beyond this, the pattern is broadly consistent across different sports, different sexes, and different levels of skill.

CONCLUSIONS

The observed patterns are consistent with the idea that positions that are interactionally central afford players greater opportunities to do leadership-either through communication or through action. Significantly too, they also provide a basis for them to be seen to do leadership by others on their team. Thus while it is often stated that "leadership is an action, not a position," it is nevertheless the case that, when it comes to performing that action, some positions are more advantageous than others.

Optimization of a Multi-Product Intra-Supply Chain System with Failure in Rework

Globalization has created tremendous opportunities, but also made business environment highly competitive and turbulent. To gain competitive advantage, management of present-day transnational firms always seeks options to trim down various transaction and coordination costs, especially in the area of controllable intra-supply chain system. This study investigates a multi–product intra-supply chain system with failure in rework. To achieve maximum machine utilization, multiple products are fabricated in succession on a single machine. During the process, production of some defective items is inevitable. Reworking of nonconforming items is used to reduce the quality cost in production and achieving the goal of lower overall production cost. Because reworks are sometimes unsuccessful, failures in rework are also considered in this study. Finished goods for each product are transported to the sales offices when the entire production lot is quality assured after rework. A multi-delivery policy is used, wherein fixed quantity n installments of the finished lot are transported at fixed intervals during delivery time. The objective is to jointly determine the common production cycle time and the number of deliveries needed to minimize the long–term expected production–inventory–delivery costs for the problem. With the help of a mathematical model along with optimization technique, the optimal production–shipment policy is obtained. We have used a numerical example to demonstrate applicability of the result of our research.

Utility Evaluation Based on One-To-N Mapping in the Prisoner's Dilemma Game for Interdependent Networks

In the field of evolutionary game theory, network reciprocity has become an important means to promote the level of promotion within the population system. Recently, the interdependency provides a novel perspective to understand the widespread cooperation behavior in many real-world systems. In previous works, interdependency is often built from the direct or indirect connections between two networks through the one-to-one mapping mode. However, under many realistic scenarios, players may need much more information from many neighboring agents so as to make a more rational decision. Thus, beyond the one-to-one mapping mode, we investigate the cooperation behavior on two interdependent lattices, in which the utility evaluation of a focal player on one lattice may not only concern himself, but also integrate the payoff information of several corresponding players on the other lattice. Large quantities of simulations indicate that the cooperation can be substantially promoted when compared to the traditionally spatial lattices. The cluster formation and phase transition are also analyzed in order to explore the role of interdependent utility coupling in the collective cooperation. Current results are beneficial to deeply understand various mechanisms to foster the cooperation exhibited inside natural, social and engineering systems.

Women Tend to Defect in a Social Dilemma Game in Southwest China

Cooperation theories assume that interacting individuals can change their strategies under different expected payoffs, depending on their social status or social situations. When looking at sex differences in cooperation, the existing studies have found that the genders cooperate at similar frequencies. However, the majority of the data originate within Western human societies. In this paper, we explore whether there are gender differences in cooperation in China. An Iterated Prisoner’s Dilemma game with a punishment option was used to gather data about Southwest Chinese subjects in a culture in which men have a hierarchical advantage over women. Results indicate that men invested into partners significantly more than women did (34% ♂ vs. 24% ♀) while women, in turn, were more likely to defect (65% ♀ vs. 50% ♂). In this region, women have customarily held less economic power and they are used to obtain a payoff typically lower than men. We suggest that the women’s willingness to invest in cooperation has decreased throughout evolutionary time, providing us with an illustration of a culturally-driven shift towards a disparity in gender cooperation interests.

Optimum Design of PIλDμ Controller for an Automatic Voltage Regulator System Using Combinatorial Test Design

Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly.

Collaboration Networks in Applied Conservation Projects across Europe

The main funding instrument for implementing EU policies on nature conservation and supporting environmental and climate action is the LIFE Nature programme, established by the European Commission in 1992. LIFE Nature projects (>1400 awarded) are applied conservation projects in which partnerships between institutions are critical for successful conservation outcomes, yet little is known about the structure of collaborative networks within and between EU countries. The aim of our study is to understand the nature of collaboration in LIFE Nature projects using a novel application of social network theory at two levels: (1) collaboration between countries, and (2) collaboration within countries using six case studies: Western Europe (United Kingdom and Netherlands), Eastern Europe (Romania and Latvia) and Southern Europe (Greece and Portugal). Using data on 1261 projects financed between 1996 and 2013, we found that Italy was the most successful country not only in terms of awarded number of projects, but also in terms of overall influence being by far the most influent country in the European LIFE Nature network, having the highest eigenvector (0.989) and degree centrality (0.177). Another key player in the network is Netherlands, which ensures a fast communication flow with other network members (closeness-0.318) by staying connected with the most active countries. Although Western European countries have higher centrality scores than most of the Eastern European countries, our results showed that overall there is a lower tendency to create partnerships between different organization categories. Also, the comparisons of the six case studies indicates significant differences in regards to the pattern of creating partnerships, providing valuable information on collaboration on EU nature conservation. This study represents a starting point in predicting the formation of future partnerships within LIFE Nature programme, suggesting ways to improve transnational cooperation and communication.

Reputation-Based Investment Helps to Optimize Group Behaviors in Spatial Lattice Networks

Encouraging cooperation among selfish individuals is crucial in many real-world systems, where individuals’ collective behaviors can be analyzed using evolutionary public goods game. Along this line, extensive studies have shown that reputation is an effective mechanism to investigate the evolution of cooperation. In most existing studies, participating individuals in a public goods game are assumed to contribute unconditionally into the public pool, or they can choose partners based on a common reputation standard (e.g., preferences or characters). However, to assign one reputation standard for all individuals is impractical in many real-world deployment. In this paper, we introduce a reputation tolerance mechanism that allows an individual to select its potential partners and decide whether or not to contribute an investment to the public pool based on its tolerance to other individuals’ reputation. Specifically, an individual takes part in a public goods game only if the number of participants with higher reputation exceeds the value of its tolerance. Moreover, in this paper, an individual’s reputation can increase or decrease in a bounded interval based on its historical behaviors. We explore the principle that how the reputation tolerance and conditional investment mechanisms can affect the evolution of cooperation in spatial lattice networks. Our simulation results demonstrate that a larger tolerance value can achieve an environment that promote the cooperation of participants.

Gene-culture interaction and the evolution of the human sense of fairness

How Darwinian evolution would produce creatures with the proclivity of Darwinian generosity, most of them voluntarily giving up the immediate benefit for themselves or their genes, remains a puzzle. This study targets a problem, the origin of human sense of fairness, and uses fairness-related genes and the social manipulation of Darwinian generosity as the key variables underlying the human sense of fairness, inequity aversion, as well as their relationships within cooperation, and the anticipation foresight of the way relationships are affected by resource division, given the assumption of randomly matched partners. Here we suggest a model in which phenotype will gradually converge towards the perfect sense of fairness along with the prospect of cooperation. Later, the sense of fairness will decrease but it is never extinct. Where social manipulation of Darwinian generosity overshadows genetics, the sense of fairness could be acute to the degree of social manipulation. Above all, there still exists a threshold in the degree of social manipulation, beyond which altruism dominates selfishness in human cooperation. Finally, we propose three new directions toward more realistic scenarios stimulated by recent development of the synergy between statistical physics, network science and evolutionary game theory.

Secret Forwarding of Events over Distributed Publish/Subscribe Overlay Network

Publish/subscribe is a communication paradigm where loosely-coupled clients communicate in an asynchronous fashion. Publish/subscribe supports the flexible development of large-scale, event-driven and ubiquitous systems. Publish/subscribe is prevalent in a number of application domains such as social networking, distributed business processes and real-time mission-critical systems. Many publish/subscribe applications are sensitive to message loss and violation of privacy. To overcome such issues, we propose a novel method of using secret sharing and replication techniques. This is to reliably and confidentially deliver decryption keys along with encrypted publications even under the presence of several Byzantine brokers across publish/subscribe overlay networks. We also propose a framework for dynamically and strategically allocating broker replicas based on flexibly definable criteria for reliability and performance. Moreover, a thorough evaluation is done through a case study on social networks using the real trace of interactions among Facebook users.

Predicting Human Cooperation

The Prisoner's Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit. A strictly dominant strategy in a Prisoner's Dilemma (defection), when played by both players, is mutually harmful. Repetition of the Prisoner's Dilemma can give rise to cooperation as an equilibrium, but defection is as well, and this ambiguity is difficult to resolve. The numerous behavioral experiments investigating the Prisoner's Dilemma highlight that players often cooperate, but the level of cooperation varies significantly with the specifics of the experimental predicament. We present the first computational model of human behavior in repeated Prisoner's Dilemma games that unifies the diversity of experimental observations in a systematic and quantitatively reliable manner. Our model relies on data we integrated from many experiments, comprising 168,386 individual decisions. The model is composed of two pieces: the first predicts the first-period action using solely the structural game parameters, while the second predicts dynamic actions using both game parameters and history of play. Our model is successful not merely at fitting the data, but in predicting behavior at multiple scales in experimental designs not used for calibration, using only information about the game structure. We demonstrate the power of our approach through a simulation analysis revealing how to best promote human cooperation.

Exacerbating the Tragedy of the Commons: Private Inefficient Outcomes and Peer Effect in Experimental Games with Fishing Communities

Economic Experimental Games have shown that individuals make decisions that deviate down from the suboptimal Nash equilibrium. However, few studies have analyzed the case when deviation is above the Nash equilibrium. Extracting from above the Nash equilibrium is inefficient not only socially but also privately and it would exacerbate the tragedy of the commons. That would be the case of a race to the fish when stocks are becoming depleted or driver behavior on a highly congested road. The objective of this study is to analyze private inefficient extraction behavior in experimental games and to associate the type of player and the type of player group with such inefficient outcomes. To do this, we carried out economic experimental games with local coastal fishermen in Colombia, using a setting where the scarcity of the resource allows for an interior Nash equilibrium and inefficient over-extraction is possible. The state of the resource, the type of player and the composition of the group explain, in part, this inefficient behavior.

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions

In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our "worst-case weighted multi-objective game" model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call "robust-weighted Nash equilibrium". We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.

An Extended N-Player Network Game and Simulation of Four Investment Strategies on a Complex Innovation Network

As computer science and complex network theory develop, non-cooperative games and their formation and application on complex networks have been important research topics. In the inter-firm innovation network, it is a typical game behavior for firms to invest in their alliance partners. Accounting for the possibility that firms can be resource constrained, this paper analyzes a coordination game using the Nash bargaining solution as allocation rules between firms in an inter-firm innovation network. We build an extended inter-firm n-player game based on nonidealized conditions, describe four investment strategies and simulate the strategies on an inter-firm innovation network in order to compare their performance. By analyzing the results of our experiments, we find that our proposed greedy strategy is the best-performing in most situations. We hope this study provides a theoretical insight into how firms make investment decisions.

The Emergence of Relationship-based Cooperation

This paper investigates the emergence of relationship-based cooperation by coupling two simple mechanisms into the model: tie strength based investment preference and homophily assumption. We construct the model by categorizing game participants into four types: prosocialists (players who prefers to invest in their intimate friends), antisocialists (players who prefer to invest in strangers), egoists (players who never cooperate) and altruists (players who cooperate indifferently with anyone). We show that the relationship-based cooperation (prosocialists) is favored throughout the evolution if we assume players of the same type have stronger ties than different ones. Moreover, we discover that strengthening the internal bonds within the strategic clusters further promotes the competitiveness of prosocialists and therefore facilitates the emergence of relationship-based cooperation in our proposed scenarios. The robustness of the model is also tested under different strategy updating rules and network structures. The results show that this argument is robust against the variations of initial conditions and therefore can be considered as a fundamental theoretical framework to study relationship-based cooperation in reality.

Quantifying the Role of Homophily in Human Cooperation Using Multiplex Evolutionary Game Theory

Nature shows as human beings live and grow inside social structures. This assumption allows us to explain and explore how it may shape most of our behaviours and choices, and why we are not just blindly driven by instincts: our decisions are based on more complex cognitive reasons, based on our connectedness on different spaces. Thus, human cooperation emerges from this complex nature of social network. Our paper, focusing on the evolutionary dynamics, is intended to explore how and why it happens, and what kind of impact is caused by homophily among people. We investigate the evolution of human cooperation using evolutionary game theory on multiplex. Multiplexity, as an extra dimension of analysis, allows us to unveil the hidden dynamics and observe non-trivial patterns within a population across network layers. More importantly, we find a striking role of homophily, as the higher the homophily between individuals, the quicker is the convergence towards cooperation in the social dilemma. The simulation results, conducted both macroscopically and microscopically across the network layers in the multiplex, show quantitatively the role of homophily in human cooperation.