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A Mediterranean-style eating pattern (MED-EP) may include moderate red meat intake. However, it is unknown if the pro-atherogenic metabolite trimethylamine N-oxide (TMAO) is affected by the amount of red meat consumed with a MED-EP. The results presented are from a secondary, retrospective objective of an investigator-blinded, randomized, crossover, controlled feeding trial (two 5-wk interventions separated by a 4-wk washout) to determine if a MED-EP with 200g unprocessed lean red meat/wk (MED-CONTROL) reduces circulating TMAO concentrations compared to a MED-EP with 500g unprocessed lean red meat/wk (MED-RED). Participants were 27 women and 12 men (n=39 total) who were either overweight or obese (BMI: 30.5 ± 0.3 kg/m2 mean ± SEM). Serum samples were obtained following an overnight fast both before (pre) and after (post) each intervention. Fasting serum TMAO, choline, carnitine, and betaine concentrations were measured using a targeted Liquid chromatography-mass spectrometry. Data were analyzed to assess if (a) TMAO and related metabolites differed by intervention, and (b) if changes in TMAO were associated with changes in Framingham 10-year risk score. Serum TMAO was lower post-intervention following MED-CONTROL compared to MED-RED intervention (post-MED-CONTROL 3.1 ± 0.2 µM vs. post-MED-RED 5.0 ± 0.5 µM, p<0.001), and decreased following MED-CONTROL (pre- vs post-MED-CONTROL, p = 0.025). Exploratory analysis using mixed model analysis of covariance identified a positive association between changes in TMAO and changes in HOMA-IR (p = 0.036). These results suggest that lower amounts of red meat intake leads to lower TMAO concentrations in the context of a MED-EP.
South-east Asia's diverse coastal wetlands, which span natural mudflats and mangroves to man-made salt pans, offer critical habitat for many migratory waterbird species in the East Asian–Australasian Flyway. Species dependent on these wetlands include nearly the entire population of the Critically Endangered spoon-billed sandpiper Calidris pygmaea and the Endangered spotted greenshank Tringa guttifer, and significant populations of several other globally threatened and declining species. Presently, more than 50 coastal Important Bird and Biodiversity Areas (IBAs) in the region (7.4% of all South-east Asian IBAs) support at least one threatened migratory species. However, recent studies continue to reveal major knowledge gaps on the distribution of migratory waterbirds and important wetland sites along South-east Asia's vast coastline, including undiscovered and potential IBAs. Alongside this, there are critical gaps in the representation of coastal wetlands across the protected area networks of many countries in this region (e.g. Viet Nam, Indonesia, Malaysia), hindering effective conservation. Although a better understanding of the value of coastal wetlands to people and their importance to migratory species is necessary, governments and other stakeholders need to do more to strengthen the conservation of these ecosystems by improving protected area coverage, habitat restoration, and coastal governance and management. This must be underpinned by the judicious use of evidence-based approaches, including satellite-tracking of migratory birds, ecological research and ground surveys.
Users in a social network are usually confronted with decision-making under uncertain network states. While there are some works in the social learning literature on how to construct belief in an uncertain network state, few studies have focused on integrating learning with decision-making for the scenario in which users are uncertain about the network state and their decisions influence each other. Moreover, the population in a social network can be dynamic since users may arrive at or leave the network at any time, which makes the problem even more challenging. In this chapter, we introduce a dynamic Chinese restaurant game to study how a user in a dynamic social network learns about the uncertain network state and makes optimal decisions by taking into account not only the immediate utility, but also subsequent users’ influence. We introduce a Bayesian learning-based method for users to learn the network state and discuss a multidimensional Markov decision process-based approach for users to make optimal decisions. Finally, we apply the dynamic Chinese restaurant game to cognitive radio networks and use simulations to verify the effectiveness of the scheme.
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.
In cognitive networks, how to stimulate cooperation among nodes is very important. However, most 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, such as in the well-known Prisoner’s dilemma and the backward induction principle, the unique Nash equilibrium is to always play noncooperatively. In this chapter, we discuss a cooperation stimulation scheme for the scenario in which the number of interactions is finite. This scheme is based on indirect reciprocity game modeling where the key concept is “I help you not because you have helped me but because you have helped others.” The problem of finding the optimal action rule is formulated as a Markov decision process, and a modified value-iteration algorithm is utilized to find the optimal action rule. Using the packet forwarding game as an example, it is shown 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.
In the third part of this book, the third branch of modern game theory – sequential decision-making – is presented. The important components in sequential decision-making, such as network externality, information asymmetry, and user rationality, are presented and defined. The limitations of the existing approaches, such as social learning and multiarm bandit problems, are also presented.
The viability of cooperative communications depends on the willingness of users to help. Therefore, it is important to study incentive issues when designing such systems. In this chapter, we discuss a cooperation stimulation scheme for multiuser cooperative communications using an indirect reciprocity game. By introducing the notion of reputation and social norms, rational users who care about their future utility are incentivized to cooperate with others. Differently from existing works on reputation-based schemes that mainly rely on experimental verification, the effectiveness of the scheme is demonstrated in two steps. First, we conduct a steady-state analysis of the game and show that cooperating with users who have a 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 the possible cheating behaviors of users and study its impact on the indirect reciprocity game.
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.
Network service acquisition in a wireless environment requires the selection of a wireless access network. A key problem in wireless access network selection is studying rational strategies that consider negative network externality. In this chapter, we formulate the wireless network selection problem as a stochastic game with negative network externality and show that finding the optimal decision rule can be modeled as a multidimensional Markov decision process. A modified value-iteration algorithm is utilized to efficiently obtain the optimal decision rule with a simple threshold structure. We further investigate the mechanism design problem with incentive compatibility constraints, which force the networks to reveal truthful state information. The formulated problem is a mixed-integer programming problem that, in general, lacks an efficient solution. Exploiting the optimality of substructures, we introduce a dynamic programming algorithm that can optimally solve the problem in the two-network scenario. For the multinetwork scenario, the dynamic programming algorithm can outperform the heuristic greedy approach in polynomial-time complexity.
In a social network, agents are intelligent and have the capacity to make decisions so as to maximize their utility. They can either make wise decisions by taking advantages of other agents’ experiences through learning or make decisions earlier to avoid competition from huge crowds. Both of these effects – social learning and negative network externality – play important roles in the decision-making process of an agent. In this chapter, a new game called the Chinese restaurant game is introduced to formulate the social learning problem with negative network externality. Through analyzing the Chinese restaurant game, we derive the optimal strategy of each agent and provide a recursive method to achieve the optimal strategy. How social learning and negative network externality influence each other under various settings is studied through simulations. We also illustrate the spectrum access problem in cognitive radio networks as one application of the Chinese restaurant game. We find that the Chinese restaurant game-theoretic approach indeed helps users make better decisions and improves overall system performance.
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.
Cooperation is a promising approach to simultaneously achieving efficient spectrum resource use and improving the quality of service of primary users in dynamic spectrum access networks. However, due to their selfish nature, how to stimulate secondary users to play cooperatively is an important issue. In this chapter, we discuss a reputation-based spectrum access framework where the cooperation stimulation problem is modeled as an indirect reciprocity game. In this game, secondary users choose how to help primary users relay information and gain reputation, 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, the optimal action rule can be obtained, according to which the secondary user will use maximal power to help the primary user relay data and thus greatly improve the primary user's quality of service as well as the spectrum utilization efficiency. Moreover, the uniqueness of the stationary reputation distribution is proved, and the conditions under which the optimal action rule is evolutionarily stable are theoretically derived.
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.