Indirect Reciprocity Game Research Articles

An Indirect-Reciprocity-Based Incentive Framework for Multimedia Service Through Device-to-Device Multicast

Instead of delivered directly by the base station (BS), in a device-to-device multicast (D2MD) system, the multimedia content is broadcasted to the request nodes (RNs) by the head node (HN) that has cached the content, and thus the system's performances can be greatly improved. However, the content delivery consumes energy. If the cost cannot get compensated from the cooperation, the HN may refuse to serve the RNs due to the selfish and rational nature. In such a case, how to stimulate the HN to cooperate is an essential issue. In this paper, we propose an indirect reciprocity game framework to investigate the D2MD stimulation problem, where the service level of the content and the location distribution of the RNs are taken into account. In the game, the HN will be assigned a reputation value after its delivery (i.e., action), based on which the HN can get some rewards in the future. Due to the channel fading gain, the HN's action and reputation value will be distorted. Taking such a distortion into consideration, we propose a reputation updating policy to update the HN's reputation, where the deviation of the HN's reputation is captured by the concept of reputation distributions. We prove theoretically that even under the influence of channel fading, there exists a stationary reputation distribution of the whole system. To find the optimal action rule of the HN in the game, we propose a modified value iteration algorithm. We prove that within an appropriate cost-to-gain ratio, the optimal action for the HN is to serve all the RNs. Such an optimal strategy is also proved to be an evolutionarily stable strategy (ESS) under the perturbation effect of the HN's action selection. The simulation results show that the proposed scheme is superior to the existing schemes in terms of portion of the served RNs, system throughput and energy efficiency.

The Evolution of Morals Under Indirect Reciprocity

We study the coexistence of strategies in the indirect reciprocity game where agents have access to second-order information. We fully characterize the evolutionary stable equilibria and analyze their comparative statics with respect to the cost-benefit ratio (CBR). There are indeed only two stable sets of equilibria enabling cooperation, one for low CBRs involving two strategies and one for higher CBR’s which involves two additional strategies. We thereby offer an explanation for the coexistence of different moral judgments among humans. Both equilibria require the presence of second-order discriminators which highlights the necessity for higher-order information to sustain cooperation through indirect reciprocity. In a laboratory experiment, we find that more than 75% of subjects play strategies that belong to the predicted equilibrium set. Furthermore, varying the CBR across treatments leads to changes in the distribution of strategies that are in line with theoretical predictions.

Indirect-Reciprocity Data Fusion Game and Application to Cooperative Spectrum Sensing

Data sharing is one critical step to implement data fusion, and how to encourage sensors to share their data is an important issue. In this paper, we propose a reputation-based incentive framework, where the data sharing stimulation problem is modeled as an indirect reciprocity game. In the proposed game, sensors choose how to report their results to the fusion center and gain reputations, based on which they can obtain certain benefits in the future. Taking the sensing and fusion accuracy into account, reputation distribution is introduced in the proposed game, where we prove theoretically the Nash equilibrium of the game and its uniqueness. Furthermore, we apply the proposed scheme to the cooperative spectrum sensing. We show that within an appropriate cost-to-gain ration, the optimal strategy for the secondary users is to report when the average received energy is above a given threshold and keep silence otherwise. Such an optimal strategy is also proved to be a desirable evolutionarily stable strategy. Finally, simulation results are shown to verify the theoretical results and demonstrate that compared with the existing schemes, our proposed scheme achieves better operating characteristic curve and higher system throughput with convincing performance on fairness.

Multiple Criteria Relay Selection Scheme in Cooperative Communication Networks

This paper considers a wireless cooperative network with a base station (central unit) and many subscribers (users) in which the subscribers have the ability to relay information for each other to improve the overall network performance. In future wireless communication networks where subscribers are intellectual and pursue various objectives, they will not help relay information for others. This means that the viability of cooperative communication networks largely depends on the willingness of users to help. The reputation based schemes solved this problem by stimulating the users to cooperate with others. Besides, the average achievable rate at the destination node in a relay based network highly depends on the relay node location. Therefore, for a relay selection scheme, the choices of relay location, and the reputation of each user as well as signal-to-noise ratio, which is the main criterion for relay selection schemes, are important selection parameters. This paper proposes a novel multiple criteria relay selection scheme with a low implementation complexity. The proposed scheme incorporates reputation, SNR and location of the users in a highest-level design that not only selects the best relay node for each source-destination pair, but also assigns weight efficiently for each criterion. A simple and efficient optimization tool based on multiple criteria decision making method is employed to solve our formulated problem. In addition, a novel information theoretic measure, which effectively measures dissimilarity between candidate relay nodes and ideal solutions, is also investigated. Since the proposed scheme determines the potential relay nodes in order of their superiority based on the predetermined criteria, it can be utilized in multiple relay selection schemes in wireless relay networks and also in the indirect reciprocity game methods. Finally, the numerical results are provided to illustrate the comparisons between different typical experiments. To best of our knowledge, no prior work has studied the process of relay selection from this point of view.

Transient nature of cooperation by pay-it-forward reciprocity.

Humans often forward kindness received from others to strangers, a phenomenon called the upstream or pay-it-forward indirect reciprocity. Some field observations and laboratory experiments found evidence of pay-it-forward reciprocity in which chains of cooperative acts persist in social dilemma situations. Theoretically, however, cooperation based on pay-it-forward reciprocity is not sustainable. We carried out laboratory experiments of a pay-it-forward indirect reciprocity game (i.e., chained gift-giving game) on a large scale in terms of group size and time. We found that cooperation consistent with pay-it-forward reciprocity occurred only in a first few decisions per participant and that cooperation originated from inherent pro-sociality of individuals. In contrast, the same groups of participants showed persisting chains of cooperation in a different indirect reciprocity game in which participants earned reputation by cooperating. Our experimental results suggest that pay-it-forward reciprocity is transient and disappears when a person makes decisions repeatedly, whereas the reputation-based reciprocity is stable in the same situation.

Incentive analysis for cooperative interactive multiview video streaming

In interactive multiview video streaming (IMVS), users can periodically select one out of many captured views available for observation. In single-view video streaming, cooperative strategies where peers share received packets of the same video have proven to be effective in reducing server׳s upload burden, and incentive mechanisms are designed to stimulate user cooperation. However, exploiting user cooperation in higher dimensional IMVS is difficult, since users watching different views makes it difficult to establish partnership, and users switching views frequently and independently makes it difficult to maintain partnership over time. In this paper, we use a multiview video frame structure for IMVS to support cooperative view-switching, where peers may help each other even if they are observing different views. We then model peers׳ interaction as an indirect reciprocity game, where each user is assigned a reputation level. To gain a higher reputation level, users help others, which in turn leads to a higher likelihood to receive others׳ help later. In this work, we focus on how view switching, the key feature of IMVS, affects user cooperation. By modeling users׳ decision making as a Markov decision process, our analysis shows that users tend to cooperate at some views but not others: given peers can predict their future view navigation paths probabilistically, for a peer who is likely to enter a view-switching path not requiring others׳ help, he also has less incentive to cooperate. Furthermore, we observe that the game may have multiple Nash Equilibria corresponding to different cooperation levels, e.g., users cooperate at all views in the full cooperation equilibrium, while users only cooperate at certain views in the partial cooperation equilibrium. The particular equilibrium the game will converge to depends on the initial cooperation level of the game. To stimulate user cooperation at all views, we propose a Pay-for-Cooperation (PfC) scheme at the beginning of the game to drive the game to the full cooperation equilibrium to improve system efficiency. Our simulation results show the effectiveness of PfC.

Cooperation Stimulation for Multiuser Cooperative Communications Using Indirect Reciprocity Game

The viability of cooperative communications largely depends on the willingness of users to help. However, in future wireless networks where users are rational and pursue different objectives, they will not help relay information for others unless this can improve their own utilities. Therefore, it is very important to study the incentive issues when designing cooperative communication systems. In this paper, we propose a cooperation stimulation scheme for multiuser cooperative communications using indirect reciprocity game. By introducing the notion of reputation and social norm, rational users who care about their future utilities get the incentive to cooperate with others. Different from existing works on reputation based schemes that mainly rely on experimental verifications, we theoretically demonstrate the effectiveness of the proposed scheme in two steps. First, we conduct steady state analysis of the game and show that cooperating with users having good reputation can be sustained as an equilibrium when the cost-to-gain ratio is below a certain threshold. Then, by modeling the action spreading at transient states as an evolutionary game, we show that the equilibria we found in the steady state analysis are stable and can be reached with proper initial conditions. Moreover, we introduce energy detection to handle possible cheating behaviors of users and study its impact to the proposed indirect reciprocity game. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

An Indirect-Reciprocity Reputation Game for Cooperation in Dynamic Spectrum Access Networks

Cooperation is a promising approach to simultaneously achieve efficient utilization of spectrum resource and improve the quality of service of primary users in dynamic spectrum access networks. However, due to the selfish nature, secondary users may not act as cooperatively as primary users have expected. Therefore, how to stimulate the secondary users to play cooperatively is an important issue. In this paper, we propose a reputation-based spectrum access framework, where the cooperation stimulation problem is modeled as an indirect reciprocity game. In the proposed game, secondary users choose how to help primary users relay information and gain reputations, based on which they can access a certain amount of vacant licensed channels in the future. By formulating a secondary user's decision making as a Markov decision process, we obtain the optimal action rule, according to which the secondary user will use maximal power to help primary user relay data if the channel is not in an outage, and thus greatly improve the primary user's quality of service as well as the spectrum utilization efficiency. Moreover, we prove the uniqueness of stationary reputation distribution and theoretically derive the condition under which the optimal action rule is evolutionarily stable. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

Indirect Reciprocity Game Modelling for Cooperation Stimulation in Cognitive Networks

In cognitive networks, since nodes generally belong to different authorities and pursue different goals, they will not cooperate with others unless cooperation can improve their own performance. Thus, how to stimulate cooperation among nodes in cognitive networks is very important. However, most of existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, according to the well-known Prisoner's Dilemma and the backward induction principle, the unique Nash equilibrium (NE) is to always play non-cooperatively. In this paper, we propose a cooperation stimulation scheme for the scenario where the number of interactions between any pair of players are finite. The proposed algorithm is based on indirect reciprocity game modelling where the key concept is I help you not because you have helped me but because you have helped others. We formulate the problem of finding the optimal action rule as a Markov Decision Process (MDP) and propose a modified value iteration algorithm to find the optimal action rule. Using the packet forwarding game as an example, we show that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy (ESS). Finally, simulations are shown to verify the efficiency and effectiveness of the proposed algorithm.

Coevolution of honest signaling and cooperative norms by cultural group selection

Evolution of cooperative norms is studied in a population where individual and group level selection are both in operation. Individuals play indirect reciprocity game within their group and follow second order norms. Individuals are norm-followers, and imitate their successful group mates. Aside from direct observation individuals can be informed about the previous actions and reputations by information transferred by others. A potential donor estimates the reputation of a potential receiver either by her own observation or by the opinion of the majority of others (indirect observation). Following a previous study ( Scheuring, 2009) we assume that norms determine only the probabilities of actions, and mutants can differ in these probabilities. Similarly, we assume that individuals follow a stochastic information transfer strategy. The central question is whether cooperative norm and honest social information transfer can emerge in a population where initially only non-cooperative norms were present, and the transferred information was not sufficiently honest. It is shown that evolution can lead to a cooperative state where information transferred in a reliable manner, where generous cooperative strategies are dominant. This cooperative state emerges along a sharp transition of norms. We studied the characteristics of actions and strategies in this transition by classifying the stochastic norms, and found that a series of more and more judging strategies invade each other before the stabilization of the so-called generous judging strategy. Numerical experiments on the coevolution of social parameters (e.g. probability of direct observation and the number of indirect observers) reveal that it is advantageous to lean on indirect observation even if information transfer is much noisier than for direct observation, which is because to follow the majorities’ opinion suppresses information noise meaningfully.

Evolution of generous cooperative norms by cultural group selection

Evolution of cooperative norms is studied in a population where individual- and group-level selection are both in operation. Individuals play indirect reciprocity game within their group. Individuals are well informed about the previous actions and reputations, and follow second-order norms. Individuals are norm-followers, and imitate their successful group mates. In contrast to previous models where norms classify actions deterministically, we assume that norms determine only the probabilities of actions, and mutants can differ in these probabilities. The central question is how a selective cooperative norm can emerge in a population where initially only non-cooperative norms were present. It is shown that evolution leads to a cooperative state if generous cooperative strategies are dominant, although the “always defecting” and the “always cooperating”-like strategies remain stably present. The characteristics of these generous cooperative strategies and the presence of always defecting and always cooperating strategies are in concordance with experimental observations.