Search

The presence of observation errors limits players' ability to detect deviations, and hence the possibility of efficient cooperation in repeated games. When players interact repeatedly in multiple games with imperfect observation in at least one game, however, we find a novel spillover effect that enhances players' ability to sustain cooperation. This finding suggests the presence of observation error can increase the likelihood of efficient cooperation when players interact in multiple repeated games simultaneously. Our result has important implications for industrial organizations, relational contracts, and international cooperation. [ABSTRACT FROM AUTHOR]

• We study the two-player repeated game where players can choose to be spite or not. • We investigate the case where players lack knowledge of their opponent's actions. • Evolution of spite is likely to occur when players exhibit persistence. Social behavior is divided into four types: altruism, spite, mutualism, and selfishness. The former two are costly to the actor; therefore, from the perspective of natural selection, their existence can be regarded as mysterious. One potential setup which encourages the evolution of altruism and spite is repeated interaction. Players can behave conditionally based on their opponent's previous actions in the repeated interaction. On the one hand, the retaliatory strategy (who behaves altruistically when their opponent behaved altruistically and behaves non-altruistically when the opponent player behaved non-altruistically) is likely to evolve when players choose altruistic or selfish behavior in each round. On the other hand, the anti-retaliatory strategy (who is spiteful when the opponent was not spiteful and is not spiteful when the opponent player was spiteful) is likely to evolve when players opt for spiteful or mutualistic behavior in each round. These successful conditional behaviors can be favored by natural selection. Here, we notice that information on opponent players' actions is not always available. When there is no such information, players cannot determine their behavior according to their opponent's action. By investigating the case of altruism, a previous study (Kurokawa, 2017, Mathematical Biosciences, 286, 94–103) found that persistent altruistic strategies, which choose the same action as the own previous action, are favored by natural selection. How, then, should a spiteful conditional strategy behave when the player does not know what their opponent did? By studying the repeated game, we find that persistent spiteful strategies, which choose the same action as the own previous action, are favored by natural selection. Altruism and spite differ concerning whether retaliatory or anti-retaliatory strategies are favored by natural selection; however, they are identical concerning whether persistent strategies are favored by natural selection. [ABSTRACT FROM AUTHOR]

To start with an infinitely repeated game of supply chains of public goods, a stout reciprocity mechanism is introduced into income games to build a matric dynamic equation. The conventional evolutionary game method is employed to propose a model called the evolutionary game for the cooperative strategy of both the manufacturer and the seller groups in the supply chain of public goods. Also, white Gaussian noise (WGN) is added to reflect random interference in the evolution process. Then, a stochastic dynamic system is established, and Ito’s differential equation is used to analyze both sides’ strategy evolution in a game, interpret changes in system stability when random disturbance is added, and finally test the influence of different situations on the system stability by running a numerical simulation. The research shows that the stronger the reciprocity coefficient is, and the system is subjected to random interference, the greater the strategy choice change in players’ decision-making procedures when the repeated game of public goods is conducted. [ABSTRACT FROM AUTHOR]

The traditional theory of bidder and regulatory agency of construction engineering does not take into account the repeated periodicity of the game between the regulator and regulated party, so that the mathematical point of game equilibrium deviates from actual behavioral expression. According to the intertemporal nature of bidder and regulatory agency, this paper analyzed the payoff matrix of the subject of bidder and regulatory agency, constructed the repeated game behavior model of bidder and regulatory agency, and explored the game conditions of the behavioral expression (steady state and unsteady state) between the two game parties of construction engineering. The results shows that: (1) The administrative triggers are adopted in the normalized regulation, which could make both parties between bidder and regulatory agency reach Pareto Optimality; (2) The intertemporal choice behavior of the bidder is related to the economic punishments, extraneous benefits and legitimate benefits. The increase of economic punishments and legitimate benefits could reduce the illegal behaviors; (3) The larger the discounted function, the easier it is for the bidder to choose long-term legal behavior. Our work indicated that the key to establishing a long-term market mechanism between bidder and regulatory agency is to increase the future impact on the present, and construct the administrative trigger measures of infinitely repeated game. [ABSTRACT FROM AUTHOR]

I study an indefinitely repeated game where players can differ in size and investigate the implications for cooperative-looking behavior. A common approach to modeling such behavior is to appeal to trigger strategies, where deviation from the desired level of cooperation triggers a punishment phase. An alternative approach arises if players condition their actions on a stock of social goodwill, which is developed when players collectively choose more cooperative actions than the one-shot Nash (Benchekroun and Long in J Econ Behav Organ 67(1):239–252, 2008). Using data from two-person experimental games, I analyze an empirical model that combines these two approaches. I find nuanced support for each approach. For the social goodwill model, there is statistically important support in symmetric games and for larger players in asymmetric games, but not for smaller players. [ABSTRACT FROM AUTHOR]

The information sharing among internet-connected vehicles is close, but there may be the issue of vehicles providing false information. This paper proposes a new incentive mechanism scheme for VANET to provide a decentralized and trusted service management. We introduce a trust-based message evaluation system, incorporating reputation assessment and negative feedback functions to evaluate the credibility of messages and assessments. A reward and punishment mechanism based on repeated games is designed to incentivize vehicles to proactively share information, thereby preventing false information and defamation attacks. Additionally, a blockchain-based distributed trust management scheme is designed to ensure the consistency of trust management storage through the consensus mechanism of the blockchain, reducing reliance on centralized institutions. Simulation results demonstrate that the establishment of reputation and incentive mechanisms among vehicles enhances the enthusiasm for information sharing and improves the accuracy of identifying false messages.

We developed a repeated quantum game of public goods by using quantum entanglement and strong reciprocity mechanisms. Utilizing the framework of quantum game analysis, a comparative investigation incorporating both entangled and non-entangled states reveals that the player will choose a fully cooperative strategy when the expected cooperation strategy of the competitor exceeds a certain threshold. When the entanglement of states is not considered, the prisoner's dilemma still exists, and the cooperating party must bear the cost of defactoring the quantum strategy themselves; when considering the entanglement of states, the benefits of both parties in the game are closely related, forming a community of benefits. By signing a strong reciprocity contract, the degree of cooperation between the game parties can be considered using the strong reciprocity entanglement contract mechanism. The party striving to cooperate does not have to bear the risk of the other party's defector, and to some extent, it can solve the prisoner's dilemma problem. Finally, taking the public goods green planting industry project as an example, by jointly entrusting a third party to determine and sign a strong reciprocity entanglement contract, both parties can ensure a complete quantum strategy to maximize cooperation and achieve Pareto optimality, ultimately enabling the long-term and stable development of the public goods industry project. [ABSTRACT FROM AUTHOR]

Multiagent reinforcement learning (MARL) has been used extensively in the game environment. One of the main challenges in MARL is that the environment of the agent system is dynamic, and the other agents are also updating their strategies. Therefore, modeling the opponents' learning process and adopting specific strategies to shape learning is an effective way to obtain better training results. Previous studies such as DRON, LOLA and SOS approximated the opponent's learning process and gave effective applications. However, these studies modeled only transient changes in opponent strategies and lacked stability in the improvement of equilibrium efficiency. In this article, we design the MOL (modeling opponent learning) method based on the Stackelberg game. We use best response theory to approximate the opponents' preferences for different actions and explore stable equilibrium with higher rewards. We find that MOL achieves better results in several games with classical structures (the Prisoner's Dilemma, Stackelberg Leader game and Stag Hunt with 3 players), and in randomly generated bimatrix games. MOL performs well in competitive games played against different opponents and converges to stable points that score above the Nash equilibrium in repeated game environments. The results may provide a reference for the definition of equilibrium in multiagent reinforcement learning systems, and contribute to the design of learning objectives in MARL to avoid local disadvantageous equilibrium and improve general efficiency. [ABSTRACT FROM AUTHOR]

Problem definition: We study the use of nonmonetary incentives based on reciprocity to facilitate capacity sharing between two service providers that have limited and substitutable service capacity. Academic/practical relevance: We propose a parsimonious game theory framework, in which two firms dynamically choose whether to accept each other's customers without the capability to perfectly monitor each other's capacity utilization state. Methodology: We solve the continuous-time imperfect-monitoring game by focusing on a class of public strategy, in which firms' real-time capacity-sharing decision depends on an intuitive and easy-to-implement accounting device, namely the current net number of transferred customers. We refer to such an equilibrium as a trading-favors equilibrium. We characterize the condition in which capacity sharing takes place in such an equilibrium. Results: We find that some degree of efficiency loss (as compared with a central planner's solution) is necessary to induce reciprocity. The efficiency loss is small when the two firms have similar traffic intensity even if they are different in service-capacity scale, whereas the efficiency loss can be considerably large when the two firms have significantly different traffic intensities. The trading-favors mechanism, surprisingly, can outperform the perfect-monitoring benchmark when the two firms exhibit high asymmetry in terms of service-capacity scale or traffic intensity because the smaller firm tends to deviate from collaboration. Managerial implications: Firms should consider engaging in nonmonetary reciprocal capacity sharing if regulations, transaction costs, or other market and operational frictions make it difficult to use a capacity-sharing contract based on monetary payments. The trading-favors collaboration can improve the firms' payoff close to the centralized upper bound when the firms have similar traffic intensities. However, when their traffic intensities are highly different, firms are better off with a monetary-payment contract to induce more capacity sharing and are worse off investing in increasing their visibility to each other's real-time available capacity, namely investing in perfect monitoring. Funding: X. Hu thanks Faculty of Business and Economics of the University of Hong Kong and R. Caldentey thanks the University of Chicago Booth School of Business for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1203. [ABSTRACT FROM AUTHOR]

China’s economic level and people’s living standards have developed rapidly in recent years, and the medical level and medical technology have made breakthroughs continuously.With the promotion and deepening of“Internet Plus” to business model innovation in various fields, the development of “Internet Plus” medical has been rapidly developed.Due to the continuous development of data processing technologies such as machine learning and data mining, the risk of users’ personal medical data disclosure in the process of online medical treatment has also attracted the attention of researchers.Considering the deductibility of information, the discount mechanism was adopted to describe the change of user’s private information value in different stages of the game.Combined with the current research status in the field of online medical privacy protection motivation, how to mobilize the enthusiasm of both players from the level of privacy protection motivation was explored with game analysis.In view of the game characteristics of users’ strong willingness to continually use the online medical platform and intermittently provide privacy, the repeated game method was adopted to better describe the game process between users and the online medical platform.The tendency change law of the players on both sides of the game was obtained.Moreover, the Nash equilibrium of the game model was analyzed under different model parameters and the change trend of the game strategy of both sides with the progress of the game stage.Finally,from the perspective of online medical platform and users, policy suggestions on how to realize privacy protection from the level of privacy protection motivation in the process of online medical treatment were given.

This paper examines the formation of multiple coalitions under the umbrella of an international environmental agreement. Using a repeated game, we demonstrate that in any coalition structure consisting of only two or three countries, cooperation can be sustained through punishments inflicted on the deviating country from agreement rules by fellow coalition members if the discount factor is greater than 1 / 2. Furthermore, our rules of agreement can be effective even in the case of heterogeneous countries if coalitions are formed among the same type of countries. With this method, a coalition consisting of two or three countries is sustained whenever the discount factor is greater than 1 / 2 , as is the case with symmetric countries. [ABSTRACT FROM AUTHOR]

We developed a repeated quantum game of public goods by using quantum entanglement and strong reciprocity mechanisms. Utilizing the framework of quantum game analysis, a comparative investigation incorporating both entangled and non-entangled states reveals that the player will choose a fully cooperative strategy when the expected cooperation strategy of the competitor exceeds a certain threshold. When the entanglement of states is not considered, the prisoner’s dilemma still exists, and the cooperating party must bear the cost of defactoring the quantum strategy themselves; when considering the entanglement of states, the benefits of both parties in the game are closely related, forming a community of benefits. By signing a strong reciprocity contract, the degree of cooperation between the game parties can be considered using the strong reciprocity entanglement contract mechanism. The party striving to cooperate does not have to bear the risk of the other party’s defector, and to some extent, it can solve the prisoner’s dilemma problem. Finally, taking the public goods green planting industry project as an example, by jointly entrusting a third party to determine and sign a strong reciprocity entanglement contract, both parties can ensure a complete quantum strategy to maximize cooperation and achieve Pareto optimality, ultimately enabling the long-term and stable development of the public goods industry project.

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors. Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy. However, this assumption may not be true in reality, particularly when a power market is newly launched. To help power suppliers bid with the limited information, a modified continuous action reinforcement learning automata algorithm is proposed. This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game. Simulation results verify the effectiveness of the proposed learning algorithm.

I study a two-player repeated game where payoffs are asymmetric. Attempts to form cooperative agreements in such an environment, for example by pro-rating actions relative to the one-shot Nash equilibrium, have generally struggled in practice. I show that cooperative arrangements when payoff functions are asymmetric tend to disproportionately favor the larger player. Incorporating social concerns, either via envious feelings on the part of the smaller player or altruistic feelings on the part of the larger player, shift quasi-cooperative incentives toward outcomes that are more favorable to the smaller player. [ABSTRACT FROM AUTHOR]

In the Internet of things (IoTs), data transmission via network coding is highly vulnerable to intra-generation and inter-generation pollution attacks. To mitigate such attacks, some resource-intensive privacy-preserving schemes have been adopted in the previous literature. In order to balance resource consumption and data-privacy-preserving issues, a novel fuzzy-based privacy-preserving scheme is proposed. Our scheme is constructed on a T-S fuzzy trust theory, and network coding data streams are routed in optimal clusters formulated by a designed repeated game model to defend against pollution attacks. In particular, the security of our scheme relies on the hardness of the discrete logarithm. Then, we prove that the designed repeated game model has a subgame-perfect Nash equilibrium, and the model can improve resource utilization efficiency under the condition of data security. Simulation results show that the running time of the proposed privacy-preserving scheme is less than 1 s and the remaining energy is higher than 4 J when the length of packets is greater than 400 and the number of iterations is 100. Therefore, our scheme has higher time and energy efficiency than those of previous studies. In addition, the effective trust cluster formulation scheme (ETCFS) can formulate an optimal cluster more quickly under a kind of camouflage attack. [ABSTRACT FROM AUTHOR]

We examine whether foreign chief executive officers (FCEOs) and foreign independent board chairpersons (FIBCs) improve on the corporate governance (CG) practices of emerging market multinational corporations (EMMNCs) through governance spill over. We use hand‐collected data for 80 listed Nigerian multinational corporations for the period 2011–2016 (480 firm‐years) and apply a three‐stage least squares regression to address endogeneity issues. Our findings show international exposure of EMMNCs motivate appointment of FIBCs and FCEOs who positively affect their CG quality. In addition, international board interlocks positively moderate the likelihood of FCEOs to export and enhance EMMNCs' CG quality, but negatively moderate FIBCs impact on CG practices of EMMNCs. Finally, we develop a framework to show how EMMNCs' CG practices are exemplary to local firms in the home country who may mimic these governance practices. We contend the repeated game of governance spill‐over and mimetic isomorphism drives the evolution of CG institutions and, potentially, will generate institutional change in CG practices in emerging markets. [ABSTRACT FROM AUTHOR]

IOTA is widely used in the field of Internet of things, but it also produces a series of security problems which brings great security risks to IOTA. Starting from the perspective of parasite chain attack, we put forward the cost function of nodes successfully launching parasite chain attack. On this basis, the game relationship between nodes in IOTA is studied through repeated game theory and the strategy selection of nodes is analyzed, so as to provide a new method to further reduce parasite chain attacks. [ABSTRACT FROM AUTHOR]

This paper proposes a blockchain based trust management method for agents in a multi-agent system (MAS). In this work, three objectives are achieved: trust, cooperation and privacy. The trust of agents depends on the credibility of trust evaluators, which is verified using the proposed methods of trust distortion, consistency and reliability. To enhance the cooperation between agents, a tit-3-for-tat (T3FT) repeated game strategy is developed. The strategy is more forgiving than the existing tit-for-tat (TFT) strategy. It encourages cheating agents to re-establish their trust by cooperating for three consecutive rounds of play. Also, a proof-of-cooperation consensus protocol is proposed to improve agents’ cooperation while creating and validating blocks. The privacy of agents is preserved in this work using the publicly verifiable secret sharing mechanism. The proposed methods are implemented using MATLAB R2018a while the MAS is simulated using Java Agent DEvelopment framework (JADE). Simulation results validate the effectiveness of the proposed work. From the simulation results, the proposed trust method outperforms an existing fuzzy logic trust method in terms of detecting the cheating behavior of agents in the system. Besides, the proposed T3FT strategy is effective as compared to the existing tit-for-2-tat and TFT strategies in the literature. Moreover, the security analysis of the proposed method is performed. The analysis shows that the proposed work is safe from bad-mouthing and on-off trust related attacks.

Exogeneous disruptions in labor demand have become more frequent in recent times. The COVID-19 pandemic has resulted in millions of workers being repeatedly laid off and rehired according to local public health conditions. This may be bad news for market efficiency. Typical employment relations—which resemble non-enforceable (implicit) contracts—rely on reciprocity (Brown et al. in Econometrica 72:747–780, 2004), and hence could be harmed when workers' efforts no longer guarantee reemployment in the next period. In this paper we extend the BFF paradigm to include a per-period probability (0%, 10%, 50%) of publicly observable "shutdown", where a specific firm cannot contract with any workers for several periods. A Perfect Bayesian Equilibrium exists in which these shutdowns destabilize relationships, but do not harm efficiency. Our experiment shows that, remarkably, market efficiency can be maintained even with very frequent stochastic shutdowns. However, the dynamic of relational contracts changes from one where a worker finds stable employment to one where she juggles multiple employers, laying the burden of maintaining productivity upon workers and worsening worker-side inequality. [ABSTRACT FROM AUTHOR]

Few want to do business with a partner who has a bad reputation. Consequently, once a bad reputation is established, it can be difficult to get rid of. This leads on the one hand to the intuitive idea that a good reputation is easy to lose and hard to gain. On the other hand, it can lead to a strong form of history dependence in which a single beneficial or adverse event can cast a shadow over a very long period of time. It gives rise to a reputational trap where an agent rationally chooses not to invest in a good reputation because the chances others will find out is too low. Nevertheless, the same agent with a good reputation will make every effort to maintain it. Here, a simple reputational model is constructed and the conditions for there to be a unique equilibrium that constitutes a reputation trap are characterized. [ABSTRACT FROM AUTHOR]

Honeypots have been widely used in the security community to understand the cyber threat landscape, for example to study unauthorized penetration attempts targeting industrial cyber-physical systems (ICPS) and observing the behaviors in such activities. However, some better-resourced cyber attackers may attempt to identify honeypots and develop strategies to compromise them, aka anti-honeypot. In this paper, we present an anti-honeypot enabled optimal attack strategy for ICPS, by employing a novel game-theoretical approach. Specifically, the interactions between the attacker and ICPS defender are captured with a proposed hybrid signaling and repeated game, i.e., a non-cooperative two-player one-shot game with incomplete information. By taking into account both various possible defenses of an ICPS and diverse offensive acts of attackers, a Nash equilibrium is derived, which exhibits an optimal attack strategy for attackers with varying technical sophistication. Extensive simulation experiments on multiple test cases demonstrate that, the derived strategy offers the attackers an optimal tactic to compromise the target ICPS protected by honeypots, while having only incomplete knowledge of the defensive mechanisms.

Multi-agent reinforcement learning (MARL) for cooperative tasks has been extensively studied in recent years. The balance of exploration and exploitation is crucial to MARL algorithms' performance in terms of the learning speed and the quality of the obtained strategy. To this end, we propose an algorithm known as the weighted relative frequency of obtaining the maximal reward (WRFMR), which uses a weight parameter and the action probability to balance exploration and exploitation and accelerate convergence to the optimal joint action. For the WRFMR algorithm, each agent needs to share the state and the immediate reward and does not need to observe the actions of the other agents. Theoretical analysis on the model of WRFMR in cooperative repeated games shows that each optimal joint action is an asymptotically stable critical point if the component action of every optimal joint action is unique. The box-pushing task, the distributed sensor network (DSN) task, and a strategy game known as blood battlefield are used for empirical studies. Both the DSN task and the box-pushing task involve full cooperation, while blood battle comprises both cooperation and competition. The simulation results show that the WRFMR algorithm outperforms the other algorithms regarding the success rate and the learning speed.

To start with an infinitely repeated game of supply chains of public goods, a stout reciprocity mechanism is introduced into income games to build a matric dynamic equation. The conventional evolutionary game method is employed to propose a model called the evolutionary game for the cooperative strategy of both the manufacturer and the seller groups in the supply chain of public goods. Also, white Gaussian noise (WGN) is added to reflect random interference in the evolution process. Then, a stochastic dynamic system is established, and Ito's differential equation is used to analyze both sides' strategy evolution in a game, interpret changes in system stability when random disturbance is added, and finally test the influence of different situations on the system stability by running a numerical simulation. The research shows that the stronger the reciprocity coefficient is, and the system is subjected to random interference, the greater the strategy choice change in players' decision-making procedures when the repeated game of public goods is conducted., Competing Interests: Yi Lei is employed by Guizhou Leading Network Technology Services Co. Ltd. Guizhou Leading Network Technology has no issues to publish this research., (©2024 Sun et al.)

We investigate the coexistence of an age optimizing network (AON) and a throughput optimizing network (TON) that share a common spectrum band. We consider two modes of long run coexistence: (a) networks compete with each other for spectrum access, causing them to interfere and (b) networks cooperate to achieve non-interfering access. To model competition, we define a non-cooperative stage game parameterized by the average age of the AON at the beginning of the stage, derive its mixed strategy Nash equilibrium (MSNE), and analyze the evolution of age and throughput over an infinitely repeated game in which each network plays the MSNE at every stage. Cooperation uses a coordination device that performs a coin toss during each stage to select the network that must access the medium. Networks use the grim trigger punishment strategy, reverting to playing the MSNE every stage forever if the other disobeys the device. We determine if there exists a subgame perfect equilibrium, i.e., the networks obey the device forever as they find cooperation beneficial. We show that networks choose to cooperate only when they consist of a sufficiently small number of nodes, otherwise they prefer to disobey the device and compete. [ABSTRACT FROM AUTHOR]

Based on the continuous observation of the ongoing China–India border conflict in recent years, the author intends to answer why China has not yielded prospective policy returns from the Indian side, even if it has been pursuing a cooperative strategy toward India after the Doklam standoff. Inspired by several doctrines of game theory under the dynamic game scenario and the application of relevant gaming tactics, this essay argues that after the Doklam standoff, China has been consistently pursuing an India policy that is risk-averse in nature, represented by its fundamental goal of persevering stability in the secondary direction of China–India border area. As a supporter of this argumentation, a diachronic investigation in terms of the evolution of China–India Relations between 2017 and 2020 was conducted, in which both countries were presumed as state actors involving in repeated gaming process with observable actions and asymmetric information sources. The investigation covers the respective actions adopted by both China and India since the Doklam standoff in 2017, along with the strategic interactions between the two sides from 2018 to 2019, till the most recent standoff in the Galwan Valley and the standoff along the Panggong Tso in 2020. The major finding of this essay is that there exists a causal-effect relationship between the expected payment structures of both sides in a gaming process and the outcome of the implementation of a certain cooperative strategy. Besides, as opponents in a gaming process, either side's self-cognition and its evaluation on the bilateral relations will pose critical impact on its policy-making. Therefore, in the specific case of China–India border conflict, it is highly advised that China should make practical efforts to avert cognition risks of all kinds while managing its relation with India; otherwise, negative consequences may occur due to the mismatch of its strategic goals and its policy devices. [ABSTRACT FROM AUTHOR]

Workers generally commute on a daily basis, so we model commuting as a repeated game. The folk theorem implies that for sufficiently large discount factors, the repeated commuting game has as a Nash equilibrium any feasible strategy that is uniformly better than the minimax strategy payoff for a commuter in the one shot game, repeated over the infinite horizon. This includes the efficient equilibria. An example where the efficient payoffs strictly dominate the one shot Nash equilibrium payoffs is provided. Our conclusions pose a challenge to congestion pricing in that equilibrium selection could be at least as effective in improving welfare. We examine evidence from St. Louis to determine what equilibrium strategies are actually played in the repeated commuting game. [ABSTRACT FROM AUTHOR]

In the Internet of things (IoTs), data transmission via network coding is highly vulnerable to intra-generation and inter-generation pollution attacks. To mitigate such attacks, some resource-intensive privacy-preserving schemes have been adopted in the previous literature. In order to balance resource consumption and data-privacy-preserving issues, a novel fuzzy-based privacy-preserving scheme is proposed. Our scheme is constructed on a T-S fuzzy trust theory, and network coding data streams are routed in optimal clusters formulated by a designed repeated game model to defend against pollution attacks. In particular, the security of our scheme relies on the hardness of the discrete logarithm. Then, we prove that the designed repeated game model has a subgame-perfect Nash equilibrium, and the model can improve resource utilization efficiency under the condition of data security. Simulation results show that the running time of the proposed privacy-preserving scheme is less than 1 s and the remaining energy is higher than 4 J when the length of packets is greater than 400 and the number of iterations is 100. Therefore, our scheme has higher time and energy efficiency than those of previous studies. In addition, the effective trust cluster formulation scheme (ETCFS) can formulate an optimal cluster more quickly under a kind of camouflage attack.

The paper studies a noncooperative repeated game describing the behavior of mobile agents with directional antennas or a wireless sensor network. Periodically, agents must direct antennas towards each other to exchange information. However, the actions required to turn the antenna are energy consuming, and a balance must be maintained between the information relevance and the energy consumed to maintain this relevance. Agents receive smaller payoff at the stage in which they decide to exchange information, but win in subsequent stage by increasing the relevance of information. [ABSTRACT FROM AUTHOR]

In this paper, I study the ability of a group of citizens to cooperate against a hegemon in a repeated contest game and where group members and the hegemon have different valuations of the prize. I first consider that group members use grim trigger strategies (GTSs) to support cooperative behavior and show that full cooperation within the group is more easily sustained as a stationary subgame perfect (Nash) equilibrium (SSPE) as either group size or the heterogeneity in the valuation of the prize increases. In turn, I show that full cooperation within the group can also be sustained as a weakly renegotiation-proof equilibrium (WRPE). However, an increase in group size makes it more difficult to sustain within-group cooperation, but an increase in the relative valuation of the prize by group members still facilitates group cooperation. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we introduce the concept of infinitely split Nash equilibrium in repeated games in which the profile sets are chain-complete posets. Then by using a fixed point theorem on posets in (J. Math. Anal. Appl. 409:1084–1092, 2014), we prove an existence theorem. As an application, we study the repeated extended Bertrant duopoly model of price competition.

Device-to-device (D2D) communication is an emerging IoT technology for improved performance of cellular networks in terms of aspects like spectral efficiency, battery lifetime, coverage range. The 5G cellular network is supposed to provide a perception of 99.999% network availability. One of the approaches to obtain it is by intercell D2D communication that eliminates the issue of unavailability of bandwidth between two users connected with different base stations (BSs). The interference mitigation is a key challenge while using licensed cellular spectrum for D2D links. It is seen that the resource allocation to the D2D links is a complex problem when D2D pairs are connected with different base stations. This paper presents a dynamic Resource-Block (RB) sharing scheme between the D2D users and cellular users in a multi-cell cellular network. Here a D2D user pair plays a Repeated Game with the nearby base stations in such a way that the players share a subset of their initial allotted resource to maximize their utility. The game is played by every intercell D2D player with the base stations simultaneously. We show that the punishment period of a deviating player may be reduced by invoking an additional penalty factor for its fast adherence. In addition, we also compute the optimal number of initial orthogonal RBs that is allocated to the D2D and the cellular users. Evaluation of our proposed scheme in comparison to few well known existing techniques shows a marked improvement in throughput of the overall system. [ABSTRACT FROM AUTHOR]

We study repeated games in which players learn the unknown state of the world by observing a sequence of noisy private signals. We find that for generic signal distributions, the folk theorem obtains using ex post equilibria. In our equilibria, players commonly learn the state, that is, the state becomes asymptotic common knowledge. [ABSTRACT FROM AUTHOR]

In view of Internet of vehicles within the false information attack, and inefficiency of malicious vehicle nodes detection mechanism caused by fast node dynamic change and intensive different, this paper proposed a malicious nodes vehicle detection mechanism based on repeated game . Firstly, according to the behaviors of the vehicle in the information interaction it established the repeated game mode, and used the generated node income to calculate the trust value and the dynamic threshold. By comparison, it screened out the suspicious malicious vehicle nodes . Secondly, it identified the malicious nodes vehicles by weights of voting algorithm from suspected malicious nodes vehicles. Finally, it selected the next-hop vehicle node with the highest trust value from the neighbor list to cooperate. Simulation and analysis show that compared with the existing mechanism, this mechanism improves the detection rate of false information attack, reduces the error detection rate . [ABSTRACT FROM AUTHOR]

This study investigates the effect of voluntary disclosures on lending decisions in the repeated game. Using a unique dataset from a peer-to-peer lending platform, "ppdai" (paipaidai), we document that voluntary disclosures in the repeated game play a stronger role in promoting funding success than those in the one-shot game. We argue that voluntary disclosures improve the bidding activity in the repeated game through which they increase funding success. In addition, the greater impact of voluntary disclosures on funding success in the repeated game only holds for loans without a personal guarantee attribution. Our extended results suggest that the subjective voluntary disclosures in the repeated game have greater information content only when borrowers have a successful borrowing experience. We also point out that voluntary disclosures in the repeated game are associated with a lower probability of default. Our results are robust to the Heckman two-step estimation that addresses the self-selection effect and a specification designed to rule out the alternative explanation from reputation in the repeated game. Our study provides new insights into the real effects of costless, voluntary and unverifiable disclosures on lending decisions. [ABSTRACT FROM AUTHOR]

In obtaining the celebrated folk theorem, not only everyone must value his future sufficiently high, but also everyone must be perceived so by the others. This common perception of players' time preferences must be maintained even after someone deviates. This paper explores the implications of myopic perception in repeated games with perfect monitoring. Under myopic perception, a deviator will not be perceived as a long-run player in the continuation game, which in turn affects the equilibria in the repeated game before deviation. We develop an algorithm to compute a set that characterizes almost all equilibrium payoffs when the discount factor is sufficiently high. When the stage game has a unique Nash equilibrium and it strictly dominates all other stage-game payoffs, then under myopic perception, the corresponding repeated game has a unique subgame perfect equilibrium. [ABSTRACT FROM AUTHOR]

Hindriks describes institutions as norm-governed social practices, and argue that his theory help bring together and complete earlier theories of institutions. In this comment on his paper, I argue that his argument would be even better if he clarified certain parts of his argument with regards to the nature of institutions and the relationship between institutions and social norms. I also argue that he should reconsider his claim that institutions (and social norms) exist in order to solve cooperation and coordination problems. [ABSTRACT FROM AUTHOR]