Reciprocity is a mechanism for the evolution of cooperation. This mechanism explains cooperation among unrelated individuals by an assortment in which individuals restrict their interaction partners to avoid being exploited by defectors. Three types of reciprocal mechanisms have been proposed: direct reciprocity, indirect reciprocity, and network (spatial) reciprocity. Direct reciprocity is a behavior whereby an individual acts in a manner that temporarily reduces his or her fitness while increasing another individual’s fitness with the expectation that the other individual will behave similarly at a later time. If individuals who use direct reciprocity both regard each other as cooperative, they will establish reciprocal relationships. Indirect reciprocity is a mechanism that enables individuals to establish cooperative relationships, e.g., by evaluating others’ behavior, without having direct interactions, which are required by direct reciprocity. If an individual cooperates with individuals with good reputations and that individual establishes a good reputation, cooperation via indirect reciprocity can occur. Network reciprocity explains reciprocal relationships via restriction of interaction, e.g., network and spatial restrictions, namely, individuals interact with fixed individuals in a network. If cooperative clusters arise, cooperators realize high fitness by cooperating with each other. Theoretical studies have explored the evolutionary stability of the cooperation that facilitates these reciprocities. Experimental studies have examined the evidence for these reciprocities according to the theoretical studies. These theoretical and experimental studies assume a simple interaction among individuals for cooperative and reciprocal behavior, such as in evolutionary game theory. By examining such theoretical and experimental evidence in a less restrictive environment, these pieces of evidence can be generalized to natural environments and real life. Human behavior logs on the Internet, e.g., online games, can be utilized in such studies. In this chapter we review the theory and evidence for the three types of reciprocities. First, we introduce evolutionary game theory as a framework for the evolution of cooperation, such as reciprocal altruism. Second, we explain the three types of reciprocity by reviewing results of theoretical studies. Third, we present experimental evidence for human reciprocity, which supports these evolutionary theories of reciprocity. Finally, we present empirical evidence for human reciprocity in practice using an online game dataset, which represents a less restrictive environment.

While peer-to-peer (P2P) video streaming systems have achieved promising results, they introduce a large number of unnecessary traverse links, leading to substantial network inefficiency. To address this problem, we discuss how to enable cooperation among“group peers,” which are geographically neighboring peers with large intragroup upload and download bandwidths. Considering the peers’ selfish nature, we formulate the cooperative streaming problem as an evolutionary game and introduce, for every peer, the evolutionarily stable strategy (ESS). Moreover, we discuss a simple and distributed learning algorithm for the peers to converge to the ESSs. With the discussed algorithm, each peer decides whether to be an agent who downloads data from the peers outside the group or a free-rider who downloads data from the agents by simply tossing a coin, where the probability of the coin showing a head is learned from the peer’s own past payoff history. Simulation results show that compared to the traditional noncooperative P2P schemes, the discussed cooperative scheme achieves much better performance in terms of social welfare, probability of real-time streaming, and video quality.

A huge amount of information, created and forwarded by millions of people with various characteristics, propagates through online social networks every day. Understanding the mechanisms of information diffusion over social networks is critical to various applications, including online advertisements and website management. Differently from most existing works in this area, we investigate information diffusion from an evolutionary game-theoretic perspective and try to reveal the underlying principles dominating the complex information diffusion process over heterogeneous social networks. Modeling the interactions among the heterogeneous users as a graphical evolutionary game, we derive the evolutionary dynamics and the evolutionarily stable states (ESSs) of the diffusion. The different payoffs of the heterogeneous users lead to different diffusion dynamics and ESSs among them, in accordance with the heterogeneity observed in real-world data sets. The theoretical results are confirmed by simulations. We also test the theory on the Twitter hashtag data set. We observe that the evolutionary dynamics fit the data well and can predict future diffusion data.

Many spectrum sensing methods and dynamic access algorithms have been proposed to improve secondary users’ access opportunities. However, few of them have considered integrating the design of spectrum sensing and access algorithms together by taking into account the mutual influence between them. In this chapter, we focus on jointly analyzing the spectrum sensing and access problem. Due to their selfish nature, secondary users tend to act selfishly to access the channel without contributing to spectrum sensing. Moreover, they may employ out-of-equilibrium strategies because of the uncertainty of others’ strategies. To model the complicated interactions among secondary users, the joint spectrum sensing and access problem is formulated as an evolutionary game and the evolutionarily stable strategy (ESS) that no one will deviate from is studied. Furthermore, a distributed learning algorithm for the secondary users to converge to the ESS is introduced. Simulation results shows that the system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of the selfish secondary users.

The motivation of this book and necessary background knowledge of this book are provided. First, a brief introduction to competition and cooperation in wireless and social networks is provided, along with examples and a literature review. Then, the limitations of traditional game theory in this area are presented. Finally, the three branches of modern game theory – indirect reciprocity, evolutionary games, and sequential decision-making – will be briefly mentioned to illustrate their strengths for overcoming the highlighted limitations.

How information diffuses over social networks has attracted much attention from both industry and academics. Most of the existing works in this area are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the network users’ decisions, actions, and socioeconomic interactions are generally ignored in most existing works. In this chapter, we discuss an evolutionary game-theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks and uniform-degree and nonuniform-degree networks. We find that the dynamics of information diffusion over these three kinds of networks are scale-free and the same as each other when the network scale is sufficiently large. To verify the theoretical analysis, we perform simulations of the information diffusion over synthetic networks and real-world Facebook networks. Moreover, we conduct an experiment on the Twitter hashtag data set, which shows that the game-theoretic model well fits and predicts information diffusion over real social networks.

Distributed adaptive filtering has been considered to be an effective approach for data processing and estimation over distributed networks. Most existing algorithms focus on designing different information diffusion rules, regardless of the evolutionary characteristics of a distributed network. In this chapter, we study the adaptive network from the game-theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game. With this formulation, the nodes in the network are regarded as players and the local combiner of estimated information from different neighbors is regarded as a form of diverse strategy selection. We show that this graphical evolutionary game framework is very general and can unify the existing adaptive network algorithms. Based on this framework, as examples, two error-aware adaptive filtering algorithms are discussed. Moreover, we use graphical evolutionary game theory to analyze the information diffusion process over the adaptive networks and the evolutionarily stable strategy of the system. Finally, simulation results are shown to verify the effectiveness of the method discussed in this chapter.