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In this study, we investigated the human capacity to generate randomness in decision-making processes using the rock-paper-scissors (RPS) game. The randomness of the time series was evaluated using the time-series data of RPS moves made by 500 subjects who played 50 consecutive RPS games. The indices used for evaluation were the Lempel–Ziv complexity and a determinism index obtained from a recurrence plot, and these indicators represent the complexity and determinism of the time series, respectively. The acquired human RPS time-series data were compared to a pseudorandom RPS sequence generated by the Mersenne Twister and the RPS time series generated by the RPS game’s strategy learned using the human RPS time series acquired via genetic programming. The results exhibited clear differences in randomness among the pseudorandom number series, the human-generated series, and the AI-generated series.

We show that the integration by parts formula based on Malliavin-Skorohod calculus techniques for additive processes helps us to compute quantities like $\mathbb{E}\left(L_{T} h\left(L_{T}\right)\right)$, or more generally $\mathbb{E}\left(H\left(L_{T}\right)\right)$, for different suitable functions $h$ or $H$ and different models for the cumulative loss process $L .$ These quantities are important in Insurance and Finance. For example they appear in computing expected shortfall risk measures or prices of stop-loss contracts. The formulas given in the present paper generalize the formulas given in a recent paper by Hillairet, Jiao and Réveillac (HJR). In the HJR paper, despite the use of advanced models, including the Cox process, the treatment of the formulas is based only on Malliavin calculus techniques for the standard Poisson process, a particular case of additive process. In the present paper, Malliavin calculus techniques for additive processes are used, more general results are obtained and proofs appears to be shorter.

Background: Recent scientific research explains corporate social responsibility as an economic activity. This paper interprets social responsibility as a means of power to increase firms’ market share in a duopoly. Objectives: This paper analyses the duopoly model in which firms decide on optimal social investments and production in two phases. The basic research question is how the significance of the conflict affects social investments, market shares, production quantities, profits, and social welfare. Methods / Approach: Conflict technology is described by contest success functions determining market shares. Game theory, optimization, and comparative statics are used in the analysis. Results: The conditions of equilibrium existence and its characteristics are described. Conflict adversely affects the profit of the inefficient firm while it favourably affects social welfare. Conflict’s impact on an efficient firm’s profit depends on the marginal cost difference. Conclusions: If there is no significant cost difference, it is more favourable for firms not to invest in socially responsible activities by agreement, which hurts social welfare. When marginal cost difference is significant, corporate social responsibility increases an efficient firm’s profit, positively impacting social welfare.

gracias a la teoría de los juegos tenemos una mejor comprensión del comportamiento humano en la economía. Sin embargo, como esta teoría excluye el aspecto psicológico de la conducta, una revisión del supuesto de racionalidad completa la información perdida en algunos juegos. Como consecuencia, han surgido algunos enfoques que incluyen aspectos conductuales y psicológicos en los juegos. Esto ha generado una gran cantidad de literatura distribuida en líneas de investigación aparentemente independientes, hecho que puede generar confusión. Para aclarar si la teoría de juegos conductual y psicológica son enfoques independientes, se realizó una revisión sistemática utilizando las directrices PRISMA para identificar todos los estudios empíricos publicados bajo ambas denominaciones. Se recogieron trabajos que (1) tuvieran variables psicológicas, (2) estuvieran revisados por pares y (3) tuvieran algún diseño experimental. De los 492 trabajos buscados, 67 se incluyeron en esta revisión sistemática. Se organizarwon y estudiaron para determinar qué tipo de variables psicológicas incluían y si realmente existen dos enfoques diferentes o no. El término más utilizado es la teoría del juego conductual, en la que se utilizan ampliamente variables como la culpa, la confianza, la motivación y la reciprocidad. La principal conclusión es que los dos enfoques son realmente el mismo y son los seguidores de los principales autores de cada corriente los que publican bajo uno u otro nombre.//thanks to the game theory we have a better understanding of human behavior in the economy. However, since this theory excludes the psychological aspect from conduct, a revision of the rationality assumption completes the missed information in some games. As a consequence, some approaches have emerged including behavioral and psychological aspects in games. This has generated a large amount of literature distributed in apparently independent lines of research, a fact that could generate confusion. To clarify whether behavioral game theory and psychological game theory are independent approaches, a systematic review was conducted using the PRISMA guidelines to identify all empirical studies published under both names. Papers that (1) had psychological variables, (2) were peer-reviewed, and (3) had any experimental design were collected. From 492 papers searched, 67 were included in this systematic review. They were organized and studied to determine what type of psychological variables they included and whether there are really two different approaches. The most common term used is behavioral game theory in which variables like guilt, trust, motivation, and reciprocity are widely used. The main conclusion is that the two approaches are really the same and it is the followers of the main authors of each current who publish under one or the other name.

Competition to gain more market share leads to the development of a dynamic environment and the fulfillment of an improvement cycle in supply networks. In this paper, as the first attempt, two strategic periods based on the introduction and growth phases of supply networks are considered. Each of these periods has its own characteristics and develops different competitive structures. The allocation of market share between competing supply networks is carried out on the extent of their relationships with green orientations in the network. The ownership of supply sources in these networks is replaced by that of relationships (partnership supply) developed by some parent brands. The efforts of each parent brand towards establishing green supply relationships lead to a greater market share for it. Further to competition between the supply networks, co-petition within each network also results in the emergence of different virtual alliance structures. The performance history of each brand and its subsidiary partners in the first period is included in the second period's assessments and leads to the development or decline of the network. Another dimension of the paper lies in the development of multi-agent transportation arising from the green requirements. Two gaming-based heuristic algorithms are proposed for handling the decentralized decision-making structures in the multi-period multi-stage platform under discussion. To evaluate the taken approach, a real-world inspired case is taken into account. The numerical results prove the better performance of the proposed scheme towards green coverage of the network and, thereupon, increased market share.

Under this background, this paper tries to find countermeasures and ways for carbon reduction by observing and analyzing the influencing factors of carbon emissions, designing ways to minimize carbon emissions and maximize resources and energy. In view of the above problems, the carbon emission prediction research is closely combined with the research of deep neural network, the carbon emission prediction models based on deep neural network are established, respectively, and the game theory is introduced to maximize the resource economy. Based on the analysis of the cost of energy resources, this paper puts forward a model based on game theory and makes an overall planning of the bidding online auxiliary decision-making system in combination with the actual market demand. Build a big data analysis platform based on the Internet of things, collect the data related to carbon emission for normalization, analyze the influencing factors related to carbon emission by using the principal component analysis method, select the data with higher connection value, and take the time series data as the input of the deep neural network for simulation verification. The simulation results show that the game model of carbon emission minimization and energy resource economic maximization based on deep neural network can effectively improve the economic maximization of energy resources and reduce carbon emissions.

As an effective paradigm to address uncertainty in payoffs and transition probabilities, robust stochastic games have been formulated in the literature. This paper is concerned with the computation of subgame perfect equilibria in stationary strategies (SSPEs) in robust stochastic games. To tackle this problem, we develop in this paper a globally convergent differentiable path-following method by exploiting the structures of the games. Incorporating a logarithmic-barrier term into each player’s payoff function with an extra variable between zero and one, we constitute a logarithmic-barrier robust stochastic game in which each player solves in each state a convex optimization problem. An application of the optimality conditions to the barrier game together with a fixed-point argument yields a polynomial equilibrium system for the barrier game. As a result of this system, we establish the existence of a smooth path that starts from an arbitrary mixed strategy profile and ends at an SSPE as the extra variable descends from one to zero. As an alternative scheme, we make up a convex-quadratic-penalty robust stochastic game and attain a globally convergent convex-quadratic-penalty differentiable path-following method for SSPEs in robust stochastic games. Numerical comparisons show that the logarithmic-barrier path-following method significantly outperforms the convex-quadratic-penalty path-following method. To further evince the value of the proposed methods, we apply the logarithmic-barrier path-following method to solve a supply chain configuration problem and a market entry problem from medical waste recycling.

This paper incorporates the players’ risk attitudes into a green supply chain (GSC) consisting of a supplier and a retailer. The supplier conducts production and determines the green level and wholesale price as a game leader, the retailer sells green products to consumers and determines the retail price as a follower. Equilibrium solutions are derived, and the influence of risk aversion on the GSC is examined. Our results show that, for the centralized GSC, risk aversion lowers the green level and the retail price; while for the decentralized GSC, risk aversion lowers the wholesale price and the retail price, but it may induce the supplier to increase the green level given a large risk tolerance of the supplier. Meanwhile, the risk-averse decentralized GSC may obtain more expected profit than the risk-neutral decentralized GSC. Furthermore, this paper designs a revenue-and-cost-sharing joint contract to coordinate the risk-neutral GSC, and such a contract can improve the risk-averse GSC under specific conditions.

The emergence of intelligent applications produces the demand for computing. How to reduce the computation pressure in mobile edge computing (MEC) under massive computation demand is an urgent problem to solve. Specifically, the allocation of heterogeneous resources including communication resources and computing resources needs to be optimized simultaneously. From the perspective of joint optimization of channel allocation, device-to-device (D2D) pairing, and offloading mode, this paper studies the multi-user computing task offloading problem in device-enhanced MEC. The objective is maximizing the aggregate offloading benefits, i.e., the tradeoff between delay and energy consumption, of all compute-intensive users in the network. By introducing game theory, the problem is modeled as a multi-user computation task offloading game, which is proved to be an exact potential game (EPG) with at least one pure-strategy Nash equilibrium (NE) solution. In order to find a desirable solution, this paper proposes a better reply based distributed multi-user computation task offloading algorithm (BR-DMCTO). Simulation results show that the proposed offloading mechanism can improve the benefit of users, and verify the effectiveness and convergence of the proposed algorithm.

Problem definition: Few papers have explored the impact of price matching negotiation (PM), in which a channel matches its price with the resulting wholesale price bargained by another channel, on firms’ performances, consumer welfare, and social welfare, with and without supply chain coordination. Academic/practical relevance: Negotiation has been widely seen in determining both uniform and discriminatory wholesale prices, which affect outcomes of competitive supply chain practices. Methodology: To characterize the PM mechanism, we use game theory and Nash bargaining theory to compare PM with simultaneous negotiation (SN) through a common-seller two-buyer differentiated Bertrand competition model. Results: Our analysis reveals that PM can benefit the seller but hurt all buyers, which is at odds with some fair wholesale pricing clauses intending to protect buyers. Under coordination with side payments, however, all firms can conditionally benefit more from PM than from SN. Despite firms’ gains, PM leads to less consumer utility and social welfare compared with SN, unless the second buyer in PM is considerably less powerful than the first buyer. Coordination further worsens PM’s negative impact on consumer utility and social welfare. Moreover, the existence of a spot market can increase the wholesale price in PM, hurting buyers, consumers, and society. Furthermore, the qualitative results about PM remain robust under an alternative disagreement point for PM, multiple buyers, and other extensions. Managerial implications: This paper delivers insights on when price matching in supply chain wholesale price negotiation can benefit a seller, buyers, consumers, and society in a variety of scenarios. It advocates how managers can use PM to their own advantages and provides rationale to decision makers for policy regulations regarding wholesale pricing.

Developing a low-carbon economy is the only way for countries to achieve sustainable development. Carbon emission reduction policies and low-carbon technology (LCT) innovation play key roles in developing low-carbon economy. Under government reward and punishment regulations, based on the bilateral matching and evolutionary theories, this paper constructs an evolution model consisting of a manufacturer investing LCT and a supplier offering LCT to analyze multi-phase LCT investment strategies. Firstly, the profit optimization model of a green supply chain is constructed from the perspectives of centralized-matching (CM), decentralized-matching (DM), and mismatching (MM), and the spatial information internal evolution law of multi-phase LCT investment is described by the Markov chain. Then, a bilateral matching algorithm is proposed to solve the equilibrium solutions, and the evolution process of the three modes is analyzed by numerical simulation. Finally, based on the product green degree, we analyze the impact of subsidies and taxes on investment-production decisions. Analytical results show that the matching mechanism proposed in this paper can help supply chain firms to obtain stable matching and has a significant effect on the realization of "triple wins" of society, economy, and environment. The investment utility of CM is higher than that of DM and MM. Manufacturers are inclined to adopt LCT, and the investment level tends to be stable over time. Government reward and punishment regulations are helpful to motivate supply chain firms to invest in LCT, and the synergistic effect of subsidies and taxes is better than that of a single policy.

The user demands to delay-sensitive applications have put forward new requirements for the mobile networks. Edge caching as a promising way is proposed to enhance the QoS for end users at the network edge. Given the widely distributed edge nodes, the content providers usually prefer to integrate them with cooperative caching services, by forming a cache coalition. Although such a coalition could be beneficial as a whole, it neglects the profits of individual members, which is one major concern in forming the coalition itself. Specifically, they remain open problems that i) whether each member can benefit from the coalition and ii) how much (more) revenue each member can earn from the cooperation mode. Besides, due to the poor scalability, conventional cooperation scheme which only considers fixed edge nodes cannot adapt to the spatial and temporal imbalance of user requests. In this paper we tackle the problems of coalition establishment and profit allocation among the coalition members. Particularly, by adopting both fixed edge nodes and mobile vehicles as caching nodes, we propose a hybrid service provisioning framework and cooperative service caching and workload scheduling methods. To maximize the profits in managing the caching resources in the established coalition, we devise an optimization model with a mixed integer programming (MIP), in which the QoS requirements of end users and caching capacities of each coalition member are also considered as constraints. In addition, based on the Hedonic game theory, we propose a dynamic coalition algorithm to guide each member to join or leave the coalition at each time slot out of its own profits. Experimental results demonstrate that, compared to the cases only considering fixed caching nodes, our hybrid caching scheme can improve i) the overall profit of the coalition by 53% and ii) the average profit of individual participants by 42%, respectively. Experimental results demonstrate that our algorithm not only improves the overall profits of the coalition (average 53% compared to BS-cooperation case), but also ensures the benefit of each participant from the coalition. Mobile CIPs obtain attractive profits (more than 0) and fixed CIPs gain 42% more profits than that in BS-cooperation condition. Recently, emergence of user demand for delay-critical applications has put forward new requirements for mobile network. Edge caching acting as a promising way is proposed to supply better QoS close to user end. Usually, a network operator or content provider(CP) integrates various caching infrastructures owned by various caching infrastructures providers(CIPs) to provide cooperative caching service. Most previous literature prove forming coalition is beneficial to overall profits but neglects the profits of members. It remains open to figure out whether each member can benefit from the coalition and how much revenues it can receive from this cooperation mode. In this paper, we consider the establishment of coalition and the profits allocation among players. By adopting the caching vehicles which are more flexible and lowcost than fixed infrastructures when acting as caching nodes, We devise a BS-vehicle service provisioning framework to depict the process of cooperative service caching and workload scheduling. We then propose a mathematical optimization model which is formulated as a mixed integrated programming(MIP) for profit maximization to manage the caching resources among a set of CIPs, meanwhile, to guarantee the QoS requirement and caching capacity constraint. Based on the hedonic game theory, we propose a coalition formation algorithm that each CIP make a decision to cooperate with others or end cooperation with others to form a dynamic coalition out of its own profits at each time slot. Simulation results demonstrate that our algorithm improves the overall profits of coalition and ensures each participants benefit from the coalition, compared with the baseline case.

This paper proposes a game-theoretic approach to address the problem of optimal sensor placement for detecting cyber-attacks in networked control systems. The problem is formulated as a zero-sum game with two players, namely a malicious adversary and a detector. Given a protected target vertex, the detector places a sensor at a single vertex to monitor the system and detect the presence of the adversary. On the other hand, the adversary selects a single vertex through which to conduct a cyber-attack that maximally disrupts the target vertex while remaining undetected by the detector. As our first contribution, for a given pair of attack and monitor vertices and a known target vertex, the game payoff function is defined as the output-to-output gain of the respective system. Then, the paper characterizes the set of feasible actions by the detector that ensures bounded values of the game payoff. Finally, an algebraic sufficient condition is proposed to examine whether a given vertex belongs to the set of feasible monitor vertices. The optimal sensor placement is then determined by computing the mixed-strategy Nash equilibrium of the zero-sum game through linear programming. The approach is illustrated via a numerical example of a 10-vertex networked control system with a given target vertex., Comment: 6 pages, 6 figures, 1 table, accepted to the 9th IFAC Conference on Networked Systems, Zurich, July 2022

PurposeThis conceptual paper examines the need for viable theoretical models of international learning alliances in the light of cooperative games and complexity in two distinguished educational settings. Game theory is used to demonstrate the need for the top managerial executives to acquire a detailed understanding of decision makers' behaviours and trigger the deployment of complex analytical methods. The paper scrutinizes Russia's pursued aggressive approach using shock therapy, also suggest the critical need to reform policy.Design/methodology/approachCombining the critical analysis of (1) cooperative games and (2) adaptive systems structures, a dependable model is derived, which sets the baseline for determining the role of costs and gains. The analysis is supported using a real-world example of an alliance between British University (the X and Y alliance) and Russian University. It also emphasizes the importance of both internal and external pressure variables closely connected with the cooperative games, adaptive system and shock therapy.FindingsTwo features of alliances have been emphasized. The first is the importance of informal relationships in the evolution of partnerships and of cooperation. This is a well-known factor in the success of any relationship. Especially in international partnerships, empathy at the individual level is perhaps necessary to bypass the influence of historical and cultural differences that are barriers at the macro level, preconditions rather than consequences of successful policies and contractual arrangements at the level of organizations and governments. The second feature is interdependence at the cost–benefit level and in the domain of decisions. The cooperation of both partners is required to realize payoffs.Practical implicationsThe implication of this paper is a guideline for regulators and policymakers designing worldwide alliances in higher education. In addition, this paper covers an interesting domain that could be of interest to organizations involved in forming strategic alliances, developing and re-engineering policies for strategic coalitions and setting future profitable payoff relationships within the contextual limitations of X and Y.Originality/valueThis paper creates new knowledge by concisely examining the meaning of strategic alliances in the context of the global education industry.

PurposeThe paper aims to explore how the participants in the closed-loop supply chain (CLSC) select collecting strategies under the scenarios of information symmetry and asymmetry, and to investigate the value of corporate social responsibility (CSR) cost information for participants.Design/methodology/approachThis paper constructs a two-echelon CLSC Stackelberg game consisting of one manufacturer and one retailer, where the manufacturer undertakes CSR and is responsible for the remanufacturing of used products. First, the authors establish two collecting models under information symmetry: manufacturer-collecting and retailer-collecting. Second, the authors construct two collecting models under information asymmetry and propose a two-part tariff contract to coordinate the participants’ profits. Finally, the authors make a numerical analysis to verify the results.FindingsUnder information symmetry, the profit of the participant who does not undertake collecting is positively related to the collecting rate, and the other participant may not benefit from the increase in the collecting rate. Under information asymmetry, the manufacturer will still select the retailer-collecting channel. Asymmetric information only affects the retailer’s selection. In addition, the manufacturer’s private CSR cost information is always valuable to the retailer.Originality/valueThis paper first explores the influence of the CSR cost information value on the selection of collecting channel under information asymmetry in the CLSC. The results can help company managers choose optimal collecting channel under information symmetry or under information asymmetry.

Purpose This paper aims to contribute to the understanding of the mechanisms that evolve during reputational scandals and lead to changes in industry regulation. It explores the processes by which a demand for external industry regulation evolves, also addressing the consequences of firms’ competitive behaviors which lead to substantial misbehavior and the destruction of reputational capital. The authors are interested in whether and how regulatory activities – in the case analyzed here, changes in insurance regulation regarding sales commissions for insurance brokers – are used as a costly, external behavioral control mechanism (third-loop learning) to terminate a reputational scandal that cannot be stopped by internal controls at a firm level (first-loop and second-loop learning) anymore. Design/methodology/approach The paper explores a real-life case in the German insurance industry that peaked in 2012 and has been well documented by broad media coverage, complemented by interviews with leading industry representatives. Using causal process tracing as a methodology, the authors study the factors in the case that led to an industry scandal. The authors further analyze why the insurance firms involved were not able to limit the scandal’s impact by internally controlling their behaviors, but had to call for external regulation, thus imposing costly restrictions on sales and contract processes. To identify the mechanisms underlying this result, theories from the fields of economics (game theory) and sociology (vicious cycle of bureaucracies), as well as organizational learning theory, are used. Findings The authors find that individual rationality does not suffice to prevent insurance firms from scandalous business practices, e.g. via implementing appropriate internal behavioral control measures within their organizations. If, as a result, misbehavior leads to reputational scandals, and the destruction of reputational capital spills over to the whole industry, a vicious cycle is set in motion which can be terminated by regulation as an externally enforced control mechanism. Research limitations/implications This study is limited to the analysis of a single case study, combining published materials, e.g. broad media coverage, with interviews from representatives of the insurance industry. Nevertheless, the underlying mechanisms that have been identified can be used in other case studies as well. Practical implications The paper shows that if firms want to avoid increasing regulation, they must implement strong reputational risk management (RRM) to counteract short-term profit pressure and to avoid restrictive regulation imposed on the industry as a whole. Furthermore, it sheds light on the relevance of spillover effects for RRM, as not only employee behavior within an organization might lead to the destruction of reputational capital but also that from other firms, e.g. from elsewhere within an industry. Originality/value The paper contributes by emphasizing a direct causal link between corporate scandals, loss of reputation and regulatory change within the insurance industry. Furthermore, the paper contributes by combining economic theories with organizational theories to understand real-life phenomena.

It is possible to model trust as an investment game, where a player in order to receive a reward or a better outcome, accepts a certain risk of defection by another player. Despite having achieved interesting insights and conclusions, traditional game theory does not predict the existence of trust between players who are selfish and exhibit maximizing behavior. However, experiments with these games reveal the presence of trust in player decision making. The purpose of this paper is twofold. First, it aims to build an agent-based economic model to show that trust revealed in these experiments can emerge from a simple set of dynamics. Using the generative methodology proposed by Epstein, we introduce natural selection, learning and group formation to the model to verify their impact on the emergence of trust between agents. Second, since the experiments reveal that the participants present bounded rational behavior, the paper aims to show that in an agent-based model, bounded rationality can be modelled through an artificial intelligence algorithm, the learning classifier system (LCS). As a result, we have observed that natural selection favors more selfish behavior. In addition, learning and the forming of groups increased trust in our simulations and they were able to reverse selfish behavior when introduced along with natural selection. The level of trust that emerged from the model with these three dynamics was similar to that observed in these experiments. Finally, it is possible to verify that the LCS was able to model bounded rational behavior in agents.

Aiming at the competition and cooperation decision-making problem between two ports in the same regional port group, this paper studies four kinds of dynamic game scenarios of two adjacent ports—namely, independent strategy–independent strategy (i.e., DD combinations), independent strategy–integrated strategy (i.e., DT combinations), integrated strategy–independent strategy (i.e., TD combinations), and integrated strategy–integrated strategy (i.e., TT combinations). By introducing port demand models and using the dynamic game method, the paper performs a comparative study of port service pricing, port demand, and port profit in different combinations of competition and cooperation. The results show that taking port profit as payment function, the equilibrium strategy of the leader port is the independent strategy, which is also the dominant strategy, while the independent strategy or integrated strategy of the follower port depends on the degree of service substitution provided by the two competing ports. When the degree of service substitution is low (0 < γ < 0.53), the equilibrium strategies of two competing ports are the DD combinations, but the equilibrium strategies can be improved by Pareto, and further analysis shows that TT combinations are the Pareto equilibrium strategies at this time. By contrast, when the degree of service substitution provided by the two competing ports is high (0.53 ≤γ < 1), the DT combinations are the equilibrium strategies of the two competing ports, which are also the Pareto equilibrium strategies at this time. The research shows that when the degree of service substitution of the two ports is low, to encourage the two ports to carry out differentiated development of service functions, it is conducive to promote the two ports to adopt the integrated cooperation strategies.

The digital content industry is rapidly growing, and many platforms host a vast amount of content that is produced by independent producers. A major source of revenue for these platforms is advertising. However, advertising revenue depends on the number of active customers who visit the platform and enjoy content created by content producers. Therefore, major platforms provide incentives for content producers to encourage high-quality content creation. A commonly used incentive is ad revenue sharing. In this paper, we study how such sharing incentives are affected by the nature of competition among various producers, the size of customer base, and the type of customers. We also examine the implications for profits and welfare and extend the analysis to consider the case when the platform allows consumers to directly donate to the producers. Our results show that increased producer competition can lead to higher compensation for the producers, higher content quality, and higher producers’ profits. We also show that allowing consumers to donate to producers can lead to a higher commission rate and better-quality content. However, increased consumer generosity may lead to lower profits for content producers. In addition, we find that asymmetry among producers can sometimes benefit the platform. This paper was accepted by Matthew Shum, marketing.

Game theory has recently drawn the attention of researchers from various research communities due to its diverse applications, which include modelling of real-world scenarios. The paper presented here introduces a bridge game for two players and analyses their interactions. The proposed work demonstrates that the payoffs of the players and the outcomes of the game are affected by the nature of the opponent. Furthermore, the presented work demonstrates that the outcome of the game is dependent on insufficient prior knowledge about the respective opponents. The paper emphasises and illustrates how the payoffs of the players change as the strategy mixing probabilities change. The paper also redefines the problem by concealing the identity of the opponent and analyses it using the Bayesian game model. The players’ behavioural changes over time are also mathematically analysed. Furthermore, the paper emphasises that strategies can be improved by learning and gaining information based on available data. The bridge game’s applications in real-world scenarios are also demonstrated in the paper. The paper focuses on the game’s applications in dealing with security issues in communication systems and race conditions in operating systems. To the best of our knowledge, no scientific study has ever examined bridge games by examining the behaviour of the players. There is no existing literature that shows how games can be used to model real-world scenarios. This presented work also establishes four theorems.

This paper proposes a radical alternative to discounting utility streams that allows each payoff in a stream to be treated equally. It is based on the psychology of how humans perceive time—not as the objective clicking of time on a clock—but in terms of the incidents to which attention needs to be paid. The paper’s conclusions receive some support from the discovery of the Peak-End Rule, in which laboratory subjects recall only the worst and the last experience during painful episodes.

PurposeThe objective of this paper is to investigate the impact of the information sharing of the dynamic demand on green technology innovation and profits in supply chain from a long-term perspective.Design/methodology/approachThe authors consider a supply chain consisting of a manufacturer and a retailer. The retailer has access to the information of dynamic demand of the green product, whereas the manufacturer invests in green technology innovation. Differential game theory is adopted to establish three models under three different scenarios, namely (1) decentralized decision without information sharing of dynamic demand (Model N-D), (2) decentralized decision with information sharing of dynamic demand (Model S-D) and (3) centralized decision with information sharing of dynamic demand (Model S-C).FindingsThe optimal equilibrium results show that information sharing of dynamic demand can improve the green technology innovation level and increase the green technology stocks only in centralized supply chain. In the long term, the information sharing of dynamic demand can make the retailer more profitable. If the influence of green technology innovation on green technology stocks is great enough or the cost coefficient of green technology innovation is small enough, the manufacturer and decentralized supply chain can benefit from information sharing. In centralized supply chain, the value of demand information sharing is greater than that of decentralized supply chain.Originality/valueThe authors used game theory to investigate demand information sharing and the green technology innovation in a supply chain. Specially, the demand information is dynamic, which is a variable that changes over time. Moreover, our research is based on a long-term perspective. Thus, differential game is adopted in this paper.

Rail transportation, especially, high-speed rails (HSR), is an important infrastructure for the development of national economy and the promotion of passenger experience. Due to the large bandwidth, millimeter wave (mmWave) communication is regarded as a promising technology to meet the demand of high data rates. However, since mmWave communication has the characteristic of high attenuation, mobile relay (MR) is considered in this paper. Also, full-duplex (FD) communications have been proposed to improve the spectral efficiency. However, because of the high speed, as well as the problem of penetration loss, passengers on the train have a poor quality of service. Consequently, an effective user association scheme for HSR in mmWave band is necessary. In this paper, we investigate the user association optimization problem in mmWave mobilerelay systems where the MRs operate in the FD mode. To maximize the system capacity, we propose a cooperative user association approach based on coalition formation game, and develop a coalition formation algorithm to solve the challenging NP-hard problem. We also prove the convergence and Nashstable property of the proposed algorithm. Extensive simulations are done to show the system performance of the proposed scheme under various network settings. It is demonstrated that the proposed distributed low complexity scheme achieves a nearoptimal performance and outperforms two baseline schemes in terms of average system throughput., Comment: 11 pages, 11 figures

Peer reviewed: True, Acknowledgements: The authors appreciate for the contributions of Ryman Hashem, Thomas George Thuruthel, and David Hardman by discussing the method of analyzing the collective behaviors of Bernoulli-balls presented in this paper., Collective behavior observed in nature has been actively employed in swarm robotics. In order to better respond to external cues, the agents in such systems organize themselves in an ordered structure based on simple local rules. The central assumption, in swarm robotics, is that all agents in the system collaborate to fulfill a common goal. In nature, however, many multi-agent systems exhibit a more complex collective behavior involving a certain level of competition. One representative example of complex collective behavior is a multi-ball Bernoulli-ball system. In this paper, by extracting local force among the Bernoulli balls, we approximated the state-transfer model mapping interaction forces to observed behaviors. The results show that the collective Bernoulli-ball system spent 41% of its time on competitive behaviors, in which up to 84% of the interaction state is unorganized. The rest 59% of the time is spent on collaborative behavior. We believe that the novel proposed model opens new avenues in swarm robotics research.

In my previous work, “A behavioural model for the discussion of resilience, elasticity, and antifragility,” I discussed resilience as the emerging result of the dynamic interplay between the behaviors exercised by a system and those of the environment it is set to operate in. Then, in my latest work, “On ambients as systemic exoskeletons: Crosscutting optimizers and antifragility enablers,” I introduced a “horizontal” and a “vertical classification” of systems. The idea in this second paper is that any system may be considered at the same time a “whole”, a set of “parts”, and a nested compositional hierarchy of functional nodes. By applying the idea of the first paper to this second idea of a system’s horizontal and vertical classifications, I thought of resilience / antifragility as the emergent result of concurrent behavioral interplays taking place across the involved systems’ wholes / sets of parts / hierarchies of nodes.

This project is primarily inspired by three papers: Colin Camerer and Xiaomin Li’s (2019 working paper)—Using Visual Salience in Empirical Game Theory, Ryan Oprea’s (2020)—What Makes a Rule Complex?, and Caplin et. al.’s (2011)—Search and Satisficing. Over the summer, I worked towards constructing a model of choice for the visual coordination game that can model player behavior more accurately than traditional game theoretic predictions. It attempts to do so by incorporating a degree of bias towards salience into a cellular automaton search algorithm and utilizing it alongside a sequential search mechanism of satisficing. This presentation documents my research process, describes the challenges and breakthroughs I encountered, and gives an overview of the model I developed in its current form.

In this paper, a game theory based optimization algorithm is presented for optimal planning of hybrid energy systems. Considering wind turbine, photovoltaic, and battery energy storage systems as rational players, all feasible cooperative game models are defined to find the Nash equilibrium point and maximize the profit of the players, while the real data for solar irradiation, wind speed, and power consumption of local load are considered. As the main contribution of this paper, different time of use (ToU) tariffs for covering different time intervals of a day including off-peak, shoulder, and peak hours are utilized to determine the cost of purchasing power from the grid, and consequently, to enhance the accuracy of the developed optimal planning compared with the recently presented approaches. The effectiveness of proposed strategy is verified using MATLAB software and compared with the fixed tariff based solutions. The simulation results confirms impact of ToU tariffs on the planning's results so that the accuracy of the developed optimal planning is improved using the proposed strategy.

Sponge City Public Private Partnership (PPP) project is a significant step to promote the construction of resilient city and sustainable development. Private companies take advantage of information asymmetry and regulatory loopholes to take opportunistic behavior, which affects the project delivery quality and public interests. In order to reveal the decision-making mechanism of the main stakeholders, this paper constructs an evolutionary game model of private companies, citizens and the government from a fresh perspective of perceived value. First, the traditional payoff matrix is modified by combining Prospect Theory and Mental Accounting. Next, this paper analyzes the strategic evolution law and stability conditions of game players by replicated dynamic equation. Finally, Nanganqu project is used for empirical simulation to verify the effectiveness of this model. Results indicate that, (1) due to the complexity of the project and the bounded rationality of the participants, there is no evolutionary stable strategy in this game system. (2) The behavioral decision of participants is affected by perceived incomes and perceived costs. (3) Government punishment and reputation loss can effectively curb the opportunistic behavior. All above studies are expected to improve the management of Sponge City PPP projects, providing theoretical guidance for the government to make scientific decisions.

Retail supply chains are key on any market. Their significance has long been recognized in the literature and in practice. Various factors such as pandemics, economic crises, wars, and natural disasters have further increased interest in this area. However, the most recent research has focused more on resilience, sustainability, energy consumption, and a circular economy, while the efficiency of logistics processes has been almost completely overlooked. Logistics process efficiency in retail supply chain is a fundamental principle without which all mentioned performances cannot have desired values. This gap is precisely the main motivation of this research. In this paper, research models in literature are presented which can be used, with some modifications, to measure the efficiency of the retail supply chain. The models were based on the data envelopment analysis (DEA) approach. Four main groups were identified: standard DEA models, efficiency decomposition models, network models, and game-theory-based models. In the second part of the paper, various approaches were tested on a real example of a trading company operating in Serbia. Seven supply chains were observed, each consisting of a distribution center (DC) and retail store (RS). Variables used were the number of pallet places, logistics costs, number of deliveries, accuracy of deliveries, and turnover. The results showed the advantages and disadvantages of different approaches in real examples. The main contributions of this paper lie in unique approaches to measuring the efficiency of the retail supply chain. The paper creates an excellent foundation for future research and measurements on real systems, which is equally useful for researchers and industry experts.

Industrial Internet of Things (IIoT) systems are enhancing the delivery of services and boosting productivity in a wide array of industries, from manufacturing to healthcare. However, IIoT devices are susceptible to cyber-threats such as the leaking of important information, products becoming compromised, and damage to industrial controls. Recently, blockchain technology has been used to increase the trust between stakeholders collaborating in the supply chain in order to preserve privacy, ensure the provenance of material, provide machine-led maintenance, etc. In all cases, such industrial blockchains establish a novel foundation of trust for business transactions which could potentially streamline and expedite economic processes to a significant extent. This paper presents an examination of “Schloss”, an industrial blockchain system architecture designed for multi-factory environments. It proposes an innovative solution to increase trust in industrial networks by incorporating a fairness concept as a subsystem of an industrial blockchain. The proposed mechanism leverages the concept of taxes imposed on blockchain nodes to enforce ethical conduct and discipline among participants. In this paper, we propose a game theory-based mechanism to address security and trust difficulties in industrial networks. The mechanism, inspired by the ultimatum game, progressively punishes malicious actors to increase the cost of fraud, improve the compensation system, and utilise the reward reporting capabilities of blockchain technology to further discourage fraudulent activities. Furthermore, the blockchain’s incentive structure is utilised to reduce collusion and speed up the process of reaching equilibrium, thereby promoting a secure and trustworthy environment for industrial collaboration. The objective of this paper is to address lack of trust among industrial partners and introduce a solution that brings security and trust to the forefront of industrial blockchain applications.

The need for artificial intelligence (AI) and machine learning (ML) models to optimize data center (DC) operations increases as the volume of operations management data upsurges tremendously. These strategies can assist operators in better understanding their DC operations and help them make informed decisions upfront to maintain service reliability and availability. The strategies include developing models that optimize energy efficiency, identifying inefficient resource utilization and scheduling policies, and predicting outages. In addition to model hyperparameter tuning, feature subset selection (FSS) is critical for identifying relevant features for effectively modeling DC operations to provide insight into the data, optimize model performance, and reduce computational expenses. Hence, this paper introduces the Shapley Additive exPlanation (SHAP) values method, a class of additive feature attribution values for identifying relevant features that is rarely discussed in the literature. We compared its effectiveness with several commonly used, importance-based feature selection methods. The methods were tested on real DC operations data streams obtained from the ENEA CRESCO6 cluster with 20,832 cores. To demonstrate the effectiveness of SHAP compared to other methods, we selected the top ten most important features from each method, retrained the predictive models, and evaluated their performance using the MAE, RMSE, and MPAE evaluation criteria. The results presented in this paper demonstrate that the predictive models trained using features selected with the SHAP-assisted method performed well, with a lower error and a reasonable execution time compared to other methods.

5G wireless networks have as one of the main characteristics the large-scale deployment of small cells (microcells, picocells, etc.), which is expected to bring several advantages in what concerns the high speed and low latency connectivity of the users. This large-scale deployment of small cells also raises several technical challenges, provisioning the backhaul connectivity being one of them. The paper considers the situations when small cells are deployed temporarily or are deployed in a vehicle transporting many passengers, situations when the traditional wired or wireless backhaul solutions could be too costly to be used. The paper proposes, as an alternative solution, the use as backhaul connections of the wireless links set up in the macro cells which cover the location of the small cell. The paper proposes several Game Theory (GT)-based Load-Balancing (LB) algorithms for distributing the traffic of the small cell users over the macro cell links. The proposed LB algorithms are evaluated by computer simulations and are compared with “classical” LB algorithms considered as references. The performed computer simulations show that the auction-based algorithms have the best performance in terms of delay suffered by the transmitted data packets, while the selfish routing type algorithm has weaker performance, even behaving poorly than some of the reference non-GT-based algorithms. The paper also considers the situation when several small cell APs are deployed in a limited area or a vehicle and the user groups that attach to different APs should be identified. The paper proposes two GT-based user clustering algorithms, and the performance of these algorithms are evaluated by computer simulations. These simulations show that even simple clustering algorithms could improve the distribution of the traffic over the neighbor small cell APs and reduce the delay experienced by the data packets in the transmission system.

In recent years, with the continuous development of information technology and the advent of the Internet + era, the sharing economy has not only changed people’s life and consumption patterns but also reconstructed the value cocreation process between enterprises and customers. Unlike the traditional economic model in which the enterprise is the dominant player, in the sharing economy, the enterprise withdraws from the dominant position in the value creation process and becomes a platform to support and serve the value creation of users (customers in the traditional economic model), and users become the dominant player in value creation. Users are not only the core subject of value cocreation in the sharing economy model but also the key factor determining the profitability of enterprises and the image of the platform. Bilateral users create rich value for individual users, platform enterprises, and society through value creation behaviors such as trading of the right to use idle resources and online and offline communication and interaction. Therefore, it is necessary to study the value cocreation behaviors of bilateral users of shared service platforms. This paper studies the value cocreation of bilateral users of shared service platforms, hoping to help platform enterprises further understand user behavior and needs, better fulfill the role of support and service in the process of value cocreation, provide a good value cocreation environment for bilateral users, enhance the reputation and influence of platform enterprises through user value cocreation behaviors, expand market share, and form competitive advantages. This paper stands in the perspective of intellectual enterprises and studies the problem of knowledge sharing. From the perspective of individuals, it mainly analyzes the willingness and ability of individual knowledge sharing, focusing on the “prisoner’s dilemma” in game theory to study the willingness of individuals to share. From the perspective of knowledge, we mainly analyze the interference brought by the implicit and exclusive nature of knowledge to knowledge sharing, focusing on the implicit nature of knowledge. In addition, this paper only studies the influence of value cocreation of consumer users on value cocreation of resource users and does not deeply explore whether the value cocreation of resource users influences the value cocreation of consumer users.

Volleyball is a sport of teamwork competition and tactical coordination. It tests the ability of team members to cooperate with each other and personal resilience. With the development of wireless communication networks, how to use wireless communication networks in the analysis of volleyball tactics is the subject of this article. To explore how to design the volleyball tactics analysis model of the wireless communication network, this paper proposes the method of LTE system and MIMO technology and designs the tactics analysis model based on the characteristics of the wireless network, then combined volleyball tactics and game theory to design a wireless communication network volleyball tactics analysis model. Then, this paper designs the simulation experiment of scene allocation problem, the algorithm simulation experiment, and the comparative investigation and analysis of volleyball game spiking technical ability. The results of the experiment are optimized for the tactical analysis model, and finally, the improvement of the training of the volleyball tactical analysis model based on the wireless communication network is verified through comparative experiments. The experimental results show that the team fit of the volleyball tactical analysis model training based on the wireless communication network has increased by 22.12% compared with the traditional volleyball tactical training. Compared with the traditional volleyball tactical training, the personal on-the-spot adaptability of the volleyball tactical analysis model training based on the wireless communication network has increased by 9.05%.

This study aims to effectively reduce carbon emissions by selecting the low-carbon technology service emission reduction modes. This paper constructs a revenue-sharing contract model based on game theory. The purpose of this paper is to analyze the choice of carbon emission reduction strategies for carbon emission-dependent manufacturers and low-carbon technology service providers with advance funding risk aversion. This study analyzes the effects on the optimal decision-making and profits of both parties of the degree of risk aversion and the investment cost coefficient of carbon emissions reduction for service providers, and initial carbon emissions of manufacturers. The optimal carbon emissions reduction strategy of manufacturers and service providers with numerical analysis is obtained, and the revenue-sharing contract coordination is realized. Finally, the validity of the contract is verified by simulation analysis. The results show that manufacturers and service providers can only achieve optimal cooperation for emission reduction within a certain range of parameters. With different investment cost coefficients of carbon emissions reduction and with different initial carbon emissions, manufacturers will adjust the fixed emissions reduction fee and the revenue-sharing coefficient to encourage service providers to offer the optimal carbon emissions reduction rate. This study enriches theoretical research on low-carbon service chains. It also provides important practical evidence to help manufacturers and service providers choose optimal strategies for coordinating revenue-sharing contracts.

In this paper, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The need for a modeling framework that can address multiple human-human and human-automation interactions, where all the agents can be modeled as decision makers simultaneously, is the main motivation behind this work. Such a modeling framework may be utilized for the validation and verification of autonomous vehicles: It is estimated that for an autonomous vehicle to reach the same safety level of cars with drivers, millions of miles of driving tests are required. The modeling framework presented in this paper may be used in a high-fidelity traffic simulator consisting of multiple human decision makers to reduce the time and effort spent for testing by allowing safe and quick assessment of self-driving algorithms. To demonstrate the fidelity of the proposed modeling framework, game theoretical driver models are compared with real human driver behavior patterns extracted from traffic data., Comment: 22 pages, 19 figures

International audience; This paper proposes a new decision-making framework in the context of Human-Robot Collaboration (HRC). State-of-the-art techniques consider the HRC as an optimization problem in which the utility function, also called reward function, is defined to accomplish the task regardless of how well the interaction is performed. When the performance metrics are considered, they cannot be easily changed within the same framework. In contrast, our decision-making framework can easily handle the change of the performance metrics from one case scenario to another. Our method treats HRC as a constrained optimization problem where the utility function is split into two main parts. Firstly, a constraint defines how to accomplish the task. Secondly, a reward evaluates the performance of the collaboration, which is the only part that is modified when changing the performance metrics. It gives control over the way the interaction unfolds, and it also guarantees the adaptation of the robot actions to the human ones in real-time. In this paper, the decision-making process is based on Nash Equilibrium and perfect-information extensive form from game theory. It can deal with collaborative interactions considering different performance metrics such as optimizing the time to complete the task, considering the probability of human errors, etc. Simulations and a real experimental study on "an assembly task"-i.e., a game based on a construction kitillustrate the effectiveness of the proposed framework.

Fifth Generation (5G) systems are envisaged to support a wide range of applications scenarios with varying requirements. 5G architecture includes network slicing abilities which facilitate the partitioning of a single network infrastructure on to multiple logical networks, each tailored to a given use case, providing appropriate isolation and Quality of Service (QoS) characteristics. Radio Access Network (RAN) slicing is key to ensuring appropriate QoS over multiple domains; achieved via the configuration of multiple RAN behaviours over a common pool of radio resources. This Paper proposes a novel solution for efficient resource allocation and assignment among a variety of heterogeneous services, to utilize the resources while ensuring maximum QoS for network services. First, this paper evaluates the effectiveness of different wireless data bearers. Secondly, the paper proposes a novel dynamic resource allocation algorithm for RAN slicing within 5G New Radio (NR) networks utilising cooperative game theory combined with priority-based bargaining. The impact of this work to industry is to provide a new technique for resource allocation that utilizes cooperative bargaining to ensure all network services achieve minimum QoS requirements – while using application priority to reduce data transfer time for key services to facilitate decreased turnaround time at the gate.

To alleviate farmer poverty, this paper investigates the effect of a retailer’s different socially responsible practices on a two-echelon supply chain consisting of one rural (poor) farmer, one suburban farmer, and one common retailer. Different from a commercial supply chain (whose members’ objectives are to maximize their profits) and a humanitarian supply chain (whose objective is to save more people, rather than to prioritize profits), the paper aims to study a development supply chain where the CSR-conscious retailer aims to lift the poor farmer out of poverty through cost sharing, altruistic practices, or fairness practices. Can the CSR-conscious retailer (and the development supply chain) do well by doing good? To answer the above question, four models of potential CSR investment are established and analyzed. Considering the different influences of the retailer’s CSR practices, this paper uses a Stackelberg game to analyze the decisions and profits of the farmers and the retailer in these four models. Our study finds that, first, the retailer’s CSR practices can improve the whole supply chain’s performance, which means that the supply chain has the potential to achieve the Pareto improvement for both the farmers and the retailer. Second, the retailer’s CSR practices yield benefits while implementing cost-sharing or fairness practices. Third, the rural farmer always benefits from the retailer’s CSR practices and may prefer the altruistic practice from which they can benefit the most. In addition, to benefit their profit more, the rural farmer should grow high- or low-value-added crops rather than medium-value-added ones. Fourth, from the suburban farmer’s perspective, the retailer’s CSR practices are not beneficial for their performance. However, the extent to which the suburban farmer’s performance decreases is much lower than the extent to which the rural farmer’s performance improves. The results of this paper might be used by stakeholders to alleviate poverty.

Body Area Network (BAN) is one of the most important techniques for observing patient health in real time and identifying and analyzing diseases. For effective implementation of this technology in practice and to benefit from it, there are some key issues which are to be addressed, and among those issues, security is highly critical. WBAN will have to operate in a cooperative networking model of multiple networks such as those of homogeneous networks, for the purpose of performance and reliability, or those of heterogeneous networks, for the purpose of data transfer and processing from application point of view, with the other networks such as the networks of hospitals, clinics, medical experts, etc. and the patient himself/herself, who may be moving from one network to another. This paper brings out the issues related to security in WBAN in separate networks as well as in multiple networks. For WBAN working in a separate network, the IEEE 802.15.6 standard is considered. For WBANs working in multiple networks, especially heterogeneous networks, the security issues are considered. Considering the advancements of artificial intelligence (AI), the paper describes how AI is addressing some challenges faced by WBAN. The paper describes possible approaches which can be taken to address these issues by modeling a security mechanism using various artificial intelligence techniques. The paper proposes game theory with Stackelberg security equilibrium (GTSSE) for modeling security in heterogeneous networks in WBAN and describes the experiments conducted by the authors and the results proving the suitability of the modeling using GTSSE.

Increasing urbanization and exacerbation of sustainability goals threaten the operational efficiency of current transportation systems and confront cities with complex choices with huge impact on future generations. At the same time, the rise of private, profit-maximizing Mobility Service Providers leveraging public resources, such as ride-hailing companies, entangles current regulation schemes. This calls for tools to study such complex socio-technical problems. In this paper, we provide a game-theoretic framework to study interactions between stakeholders of the mobility ecosystem, modeling regulatory aspects such as taxes and public transport prices, as well as operational matters for Mobility Service Providers such as pricing strategy, fleet sizing, and vehicle design. Our framework is modular and can readily accommodate different types of Mobility Service Providers, actions of municipalities, and low-level models of customers choices in the mobility system. Through both an analytical and a numerical case study for the city of Berlin, Germany, we showcase the ability of our framework to compute equilibria of the problem, to study fundamental tradeoffs, and to inform stakeholders and policy makers on the effects of interventions. Among others, we show tradeoffs between customers satisfaction, environmental impact, and public revenue, as well as the impact of strategic decisions on these metrics., Comment: 8 pages, 6 figures, Published in the Proceedings of the 2021 IEEE International Conference on Intelligent Transportation Systems (Awarded the Best Paper Award - First Place)

With the ever-increasing demand for air traffic over the years, safety risk assessment has become significant in maintaining the operational safety of the air transport system for long-term development towards sustainability. This paper conducts a safety risk assessment of the air traffic control (ATC) system based on game theory and cloud matter element analysis. The safety risk of the ATC system is evaluated from four aspects, including human, machine, environment, and management. The Nash equilibrium is introduced from game theory to weigh the indicators. The cloud matter element assessment adopts the cloud model from fuzzy sets and probability theory to replace the certain value in conventional matter element theory, which takes the randomness, ambiguity, and incompatibility of the indicators into consideration. In this sense, the safety risk level of the ATC system can be evaluated by calculating the correlation degree of the standard cloud matter element between the indicators and the risks. This paper expands the research scope by introducing and combing game theory and cloud matter element analysis. Furthermore, the applicability and the robustness of the method are examined with a case study of the ATC system, which enriches the existing literature and points out the direction for future work.

Mixed traffic composed of human-driven vehicles (HDVs) and CAVs will exist for an extended period before connected and autonomous vehicles (CAVs) are fully employed on the road. There is a consensus that dense fog can cause serious traffic accidents and reduce traffic efficiency. In order to enhance the safety, mobility, and efficiency of highway networks in adverse weather conditions, it is necessary to explore the characteristics of mixed traffic. Therefore, we develop a novel cellular automata model for mixed traffic considering the limited visual distance and exploring the influence of visibility levels and CAV market penetration on traffic efficiency. We design acceleration, deceleration, and randomization rules for different car-following scenes. For lane-changing, considering the interaction of CAVs and surrounding vehicles, we introduce game theory (GT) to lane-changing policies for CAVs. This paper presents the following main findings. In reduced visibility conditions, the introduction of CAVs is beneficial to improve mixed traffic efficiency on metrics such as free-flow speed and traffic capacity (e.g., 100% CAVs could increase the traffic capacity up to around 182% in environments of dense fog). In addition, the critical density increases as the proportion of CAVs increases, which is more pronounced in conditions of dense fog according to the simulation results. In addition, we compared the proposed GT-based lane-changing strategy to the traditional STCA lane-changing strategy. The results showed that the average speed is significantly improved under the proposed lane-changing strategy. The model presented in this paper can evaluate the overall performance and provide a reference for future management and control of mixed traffic flow in fog conditions.

The advance selling (AS) has been widely applied in fresh industry for it can elevating the customer experience and increase flexibility thus profit for a retailer. However, the introduction of the AS will have an impact on spot market in pricing strategy, market share and the profit of the retailer. Hence, to coordinate the supply chain and improve the efficiency of the agricultural supply chain, a two-stage game theory model is constructed to analyze the effects of AS on three classic contracts: wholesale price, quantity discount and revenue-sharing contract. This paper also discusses the boundary conditions of whether a retailer should sell in advance. The conclusions of this paper are as follows: First, revenue-sharing contracts are superior to wholesale price and quantity discount contracts when retailers sell in advance, the wholesale price contract can perform better than the quantity discount contract in the presence of AS if the contract parameter is properly set. Second, a revenue-sharing contract that normally coordinates the supply chain can performs poorly when the retailer sells in advance that the social welfare would be higher if using a quantity discount contract instead. These conclusions have important implications for suppliers when retailers sell in advance. Such suppliers need to design appropriate contracts to distribute FAP that carefully take into consideration the AS activities in the market.

This paper focuses on robustness and sensitivity analysis for sensor fault diagnosis of a voltage source converter based microgrid model. It uses robust control parameters such as minimum sensitivity parameter (H−), maximum robustness parameter (H∞), and compromised both (H−/H∞), being incorporated in the sliding mode observer theory using the game theoretic saddle point estimation achieved through convex optimization of constrained LMIs. The approach used works in a way that the mentioned robust control parameters are embedded in Hamilton–Jacobi–Isaacs-Equation (HJIE) and are also used to determine the inequality version of HJIE, which is, in terms of the Lyapunov function, faults/disturbances and augmented state/output estimation error as its variables. The stability analysis is also presented by negative definiteness of the same inequality version of HJIE, and additionally, it also gives linear matrix inequalities (LMIs), which are optimized using iterative convex optimization algorithms to give optimal sliding mode observer gains enhanced with robustness to maximal preset values of disturbances and sensitivity to minimal preset values of faults. The enhanced sliding mode observer is used to estimate states, faults, and disturbances using sliding mode observer theory. The optimality of sliding mode observer gains for sensitivity of the observer to minimal faults and robustness to maximal disturbance is a game theoretic saddle point estimation achieved through convex optimization of LMIs. The paper includes results for state estimation errors, faults’ estimation/reconstruction, fault estimation errors, and fault-tolerant-control performance for current and potential transformer faults. The considered faults and disturbances in current and potential transformers are sinusoidal nature composite of magnitude/phase/harmonics at the same time.

Reconstruction of network is to infer the relationship among nodes using observation data, which is helpful to reveal properties and functions of complex systems. In view of the low reconstruction accuracy based on small data and the subjectivity of threshold to infer adjacency matrix, the paper proposes two models: the quadratic compressive sensing (QCS) and integer compressive sensing (ICS). Then a combined method (CCS) is given based on QCS and ICS, which can be used on binary-state and multi-state dynamics. It is found that CCS is usually a superior method comparing with compressive sensing, LASSO on several networks with different structures and scales. And it can infer larger node correctly than the other two methods. The paper is conducive to reveal the hidden relationship with small data so that to understand, predicate and control a vast intricate system.

This paper presents a succinct review of attempts in the literature to use game theory to model decision making scenarios relevant to defence applications. Game theory has been proven as a very effective tool in modelling decision making processes of intelligent agents, entities, and players. It has been used to model scenarios from diverse fields such as economics, evolutionary biology, and computer science. In defence applications, there is often a need to model and predict actions of hostile actors, and players who try to evade or out-smart each other. Modelling how the actions of competitive players shape the decision making of each other is the forte of game theory. In past decades, there have been several studies which applied different branches of game theory to model a range of defence-related scenarios. This paper provides a structured review of such attempts, and classifies existing literature in terms of the kind of warfare modelled, the types of game used, and the players involved. The presented analysis provides a concise summary about the state-of-the-art with regards to the use of game theory in defence applications, and highlights the benefits and limitations of game theory in the considered scenarios., Comment: 37 pages

Dynamic pricing strategy will play an increasingly important role in solving the contradiction between power supply and demand under the current electricity market environment. The performance of different users varies from the same pricing strategy, which further to exacerbate the difficulty of the optimal power dispatching. Therefore, a non-cooperative Stackelberg model based game theory is developed in this paper, which considers both the impact on load fluctuations in power grid and users’ dissatisfaction with electricity consumption. Firstly, users are divided into different types by induction and classification in this model to realize the comprehensive consideration of different types of electricity users. Secondly, two utility functions are set up in this model. The utility function of power supply side is set to represent the benefit obtained in the process of power supply by the power company. The utility function of the power demand side is set to express the dissatisfaction degree of electricity users. The developed model is solved by using the NSGA-Ⅱ algorithm in this paper. Finally, the developed model is applied to the actual case and the sensitivity analysis of relevant parameters is carried out to verify its effectiveness.

Free riding refers to that in a multi-channel market, consumers enjoy the presale service of a product at one channel but purchase the product at another channel. In this paper, we study the optimal pricing and service strategies for a dual-channel retailer, who sells a product through both a traditional retail channel and an online channel. We assume that the offline channel provides the presale service but the online channel does not. We investigate how the changes of the degree of free riding affect the pricing/service strategies and profits of the two channels under three different scenarios: Stackelberg competition, Bertrand competition and channel integration. Our analysis shows that when the dual-channel retailer operates the two channels separately, no matter under which competitive scenario, free riding has a negative effect on both channels. And it is much more beneficial for the dual-channel retailer to let one channel work as a leader and another channel as a follower than to let the two channels make their decision simultaneous. In contrast, when the dual-channel retailer runs the two channels jointly, i.e., employs the channel-integration scenario, free riding may be beneficial to the retailer. Finally, this paper proposes and analyzes a revenue-sharing contract to coordinate a decentralized dual-channel retailer to achieve beneficial outcomes for both channels.

Aggregator-activated demand response (DR) is widely recognised as a viable means for increasing the flexibility of renewable and sustainable energy systems (RSESs) necessary to accommodate a high penetration of variable renewables. To this end, by acting as DR aggregators and offering energy trading capabilities to smaller customers, energy retailers unlock additional sources of demand-side flexibility to ensure the cost-optimal operation of RSESs. Accordingly, a growing body of literature has highlighted the ways in which non-cooperative game theory could be used to reduce the gaps between modelled and real-world results for aggregator-mediated DR schemes. This paper aims to contribute to the trends of giving a realistic grounding to research on distributed DR-integrated energy scheduling by using insights from non-cooperative game theory to determine: (1) the optimal trade-off between importing electricity and utilising DR capacity in grid-tied RSESs, (2) the impact of the price elasticity of DR supply of different customer classes – especially, new sources of electricity demand, such as e-mobility – on the system-level dispatch of DR resources, and (3) the financial implications of harnessing the flexibility potential of a large number of end-consumers across different sectors. Accordingly, the principal goal of the paper is to develop an operational planning optimisation model that can be directly applied to real-world aggregator-mediated, market-based demand-side flexibility provisioning domains. To this end, this paper presents the first DR elasticity-aware, non-cooperative game-theoretic DR scheduling model that: (1) yields the best compromise solution between imported power and dispatched DR resources from the utility's perspective, (2) characterises the utility-aggregator-customer interactions during the market-based DR trade process with several customer categories involved, and (3) disaggregates the total sectoral load on the system to individual end-consumers, which has potential implications for pre-feasibility and business case assessments. The application of the model to a conceptual micro-grid for the town of Ohakune, in New Zealand, demonstrates its effectiveness in reducing the daily system operational cost (over the critical peak hours) by ~66% and ~47% on a representative summer and winter day, respectively. Importantly, the paper provides statistically significant evidence supporting that activating the flexibility potential of small- to medium-scale end-consumers through specifically defined third-party aggregators in a market-based approach – that is aware of strategic interactions among instrumentally rational economic agents involved in the dispatch and delivery of DR resources – plays a significant role in the cost-optimal transition to 100%-renewable electricity generation systems within the smart grid paradigm.