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Summary: With the advent and popularity of electric vehicles (EVs), the intelligent transportation system has adopted them as an alternative to fossil fuel or gasoline vehicles owing to their benefits of reduced greenhouse gas emissions. However, it becomes critical to schedule the vast EVs at the charging station (CS) efficiently while maintaining security, privacy, and optimality during the energy trading. Therefore, we propose a blockchain and zero‐sum game theory‐based energy trading scheme, that is, DEVS, for optimal and secure EV charging at the CS. We incorporate the Interplanetary File System with the blockchain to ameliorate the cost‐efficiency and reliability of EV energy trading. Furthermore, we implement the zero‐sum game theory between players EV and CS to yield an optimal payoff based on pure and mixed strategies during the energy trading procedure. Moreover, we deploy and execute the smart contract (SC) of the blockchain‐based proposed DEVS scheme in the Remix integrated development environment to secure EV energy trading transactions. Additionally, the security of the proposed DEVS scheme is checked and analyzed using a fuzzing‐based Echidna tool for protected energy trading. Finally, the performance evaluation of the proposed DEVS scheme is analyzed using various standard metrics such as profit comparison based on strategies, energy price comparison, CS profit comparison for pure strategy (based on arrival time), convergence comparison, and profit (EV and CS) at saddle point and mixed strategy to avail preserved and optimal energy trading for EVs. [ABSTRACT FROM AUTHOR]

In this article, an H∞$$ {H}_{\infty } $$ optimal preview repetitive control (OPRC) scheme is proposed to deal with the disturbance attenuation problem for continuous‐time linear systems with external unknown disturbance and input delay. A general bounded L2$$ {L}_2 $$ gain is applied to the H∞$$ {H}_{\infty } $$ OPRC tracking control problem by introducing a function with discounted performance. First, an augmented system containing system state equation, tracking error dynamics, and modified repetitive control output equation is constructed, which is then transformed into a non‐delayed one by state transformation. Next, the OPRC controller is given and the game algebraic Riccati equation (GARE) is derived by transforming the H∞$$ {H}_{\infty } $$ tracking problem into a 2‐player zero‐sum game problem to give a Nash equilibrium solution of the associated min–max optimization problem. Besides, a value iteration (VI) algorithm is introduced to optimize the solution of continuous time GARE and ensure its convergence. Furthermore, the bounded‐input bounded‐output stability of the closed‐loop system is obtained by giving an upper bound on the discount factor. Finally, the numerical simulation example is provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This study focuses on the analysis of a linear quadratic zero‐sum game (LQZSG) for time‐delayed uncertain stochastic systems. To begin, we introduce a general time‐delayed zero‐sum game. Employing an algebraic transformation method, we transform the time‐delayed zero‐sum game into an equivalent uncertain random zero‐sum game without time delay. Subsequently, we present equilibrium equations that streamline the transformation of the uncertain random zero‐sum game into problems solvable as deterministic difference equations. We then investigate the LQZSG involving a linear time‐delayed uncertain stochastic system with a quadratic objective function. Within this framework, we provide a unified framework for solving this type of game and obtaining the analytic expression for the saddle‐point equilibrium of the LQZSG. Additionally, we present a numerical example and a counter‐terrorism economic game to illustrate the applicability of our findings. [ABSTRACT FROM AUTHOR]

We investigate the causal nexus between pandemic distress and anti‐immigration sentiments. We exploit the disruption brought about by the Covid‐19 outbreak to randomly provide survey respondents with information on the economic or health consequences of the pandemic. Overall, we find that pessimistic information about the economic outlook reinforces overall adversity to immigration and the wish to exclude immigrants from access to healthcare. This effect is less pronounced in areas with larger immigrant populations. Our theoretical model pins down two possible mechanisms explaining these results: a zero‐sum game to split scarce public resources between residents and immigrants on the one hand, and on the other, fear of contagion. [ABSTRACT FROM AUTHOR]

This paper presents a differential equation including both random matrices and a Liu process. Then we demonstrate that the solution to this equation exists and is unique. Under the framework of chance theory, problems of optimal control and two‐person zero‐sum game subject to differential equations are considered. An equation of optimality is provided for solving a problem of optimal control. Then equilibrium equations are proposed to identify the saddle‐point of a two‐person zero‐sum game problem. As an extension, we generalize the obtained results to the problems subject to differential equations including both random matrices and multiple Liu processes. Finally, we utilize the acquired theoretical results to analyze a portfolio selection game problem. [ABSTRACT FROM AUTHOR]

This article develops distributed optimal control policies via Q‐learning for multi‐agent systems (MASs) by solving dual games. According to game theory, first, the distributed consensus problem is formulated as a multi‐player non‐zero‐sum game, where each agent is viewed as a player focusing only on its local performance and the whole MAS achieves Nash equilibrium. Second, for each agent, the anti‐disturbance problem is formulated as a two‐player zero‐sum game, in which the control input and external disturbance are a pair of opponents. Specifically, (1) an offline data‐driven off‐policy for distributed tracking algorithm based on momentum policy gradient (MPG) is developed, which can effectively achieve consensus of MASs with guaranteed l2$$ {l}_2 $$‐bounded synchronization error. (2) An actor‐critic‐disturbance neural network is employed to implement the MPG algorithm and obtain optimal policies. Finally, numerical and practical simulation results are conducted to verify the effectiveness of the developed tracking policies via MPG algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the robust H∞$$ {H}_{\infty } $$ optimal formation control for the position subsystem of quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances and collision constraints. To prevent collision with both members of the formation and external obstacles, a collision avoidance potential function is constructed using relative position and velocity information. The basic bounded control input can ensure the stable flight and collision avoidance of a quadrotor UAV system. Based on the approximate dynamic programming (ADP) framework and two‐player zero‐sum differential game theory, the H∞$$ {H}_{\infty } $$ optimal controller is designed to further enhance the control performance of the system. The optimal value function is approximated by a single layer neural network, which avoids solving complex nonlinear Hamilton‐JacobiIsaac (HJI) equation. The stability of the closed loop system is proved. The effectiveness of the robust H∞$$ {H}_{\infty } $$ optimal formation controller based on learning is validated through MATLAB simulation in two distinct scenarios. [ABSTRACT FROM AUTHOR]

This article, titled "Case Material: 'Patricia'", discusses the therapy process with a 38-year-old woman named Patricia who sought help to understand her anger towards others. Patricia had a difficult childhood, feeling unwanted by her mother and experiencing sexual molestation by two teenage boys. Throughout therapy, Patricia exhibited anger, sarcasm, and a tendency to sabotage positive interactions. The therapist reflects on their own experiences and challenges in working with Patricia, including a breast cancer diagnosis. Towards the end of therapy, Patricia began to show progress and acceptance of her traumatic past. The article highlights the complex dynamics and milestones in the therapeutic relationship. [Extracted from the article]

To attenuate the effect of disturbances on control performance, a multi‐step adaptive critic control (MsACC) framework is developed to solve zero‐sum games for discrete‐time nonlinear systems. The MsACC algorithm utilizes multi‐step policy evaluation to obtain the solution of the Hamilton–Jacobi–Isaac equation, which is faster than that of the one‐step policy evaluation. The convergence rate of the MsACC algorithm is adjustable by varying the step size of the policy evaluation. In addition, the stability and convergence of the MsACC algorithm are proved under certain conditions. In order to realize the MsACC algorithm, three neural networks are established to approximate the control input, the disturbance input, and the cost function, respectively. Finally, the effectiveness of the MsACC algorithm is verified by two simulation examples, including a linear system and a nonlinear plant. [ABSTRACT FROM AUTHOR]

In this article, we present an iterative algorithm based on differential dynamic programming (DDP) for finite‐horizon two‐person zero‐sum differential games. The technique of DDP is used to expand the Hamilton–Jacobi–Isaacs (HJI) partial differential equation into higher‐order differential equations. Using value function and saddle point approximations, the DDP expansion is transformed into algebraic matrix equation in integral form. Based on the algebraic matrix equation, a DDP iterative algorithm is developed to learn the solution to the differential games. Strict proof is proposed to guarantee the iterative convergences of the value function and saddle point. The new algorithm is fundamentally different from existing results, in the sense that it overcome the technical obstacle to address the time‐varying behavior of HJI partial differential equation. Simulation examples are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The article discusses a case study of a patient named Patricia and explores the emotions and challenges experienced by the analyst in their therapeutic relationship. The analyst reflects on the dynamics of jealousy and envy, the moments of relational satisfaction in analysis, and the difficulties of dealing with hateful transference. The article also touches on the analyst's feelings of disappointment and curiosity, as well as the importance of patience and self-care in the therapeutic process. The author concludes by expressing astonishment and admiration for the transformative potential of analysis. [Extracted from the article]

Summary: In this article, a novel event‐triggered zero‐sum differential game through value iteration (VI)‐based adaptive fuzzy control is proposed for modular robot manipulators (MRMs) with uncertain disturbance. The subsystem dynamic of MRMs is established by joint torque feedback (JTF) technology. An identifier based on fuzzy logic model (FLM) is established to identify unknown system dynamics. Then, the trajectory tracking control issue of MRMs with uncertain disturbance is transformed into a two‐player zero‐sum differential game. On the basis of event‐triggered mechanism and VI algorithm, the event‐triggered Hamilton–Jacobi–Issacs (HJI) equation is approximately solved through establishment of actor‐critic structure. Different from the traditional adaptive dynamic programming (ADP) algorithm, this scheme is based on adaptive fuzzy optimal control through an actor‐critic structure, which refers to adaptation of both optimal cost function and optimal control policy. Moreover, tracking errors of closed‐loop MRM system are demonstrated to be uniformly ultimately bounded (UUB) under Lyapunov stability theorem. At last, experimental examples and comparisons show the reliability and effectiveness of this method. [ABSTRACT FROM AUTHOR]

In this article, the zero‐sum game problem of control and disturbance is studied for discrete affine nonlinear switched systems with unknown dynamical models. A two‐level heuristic dynamic programming iterative algorithm for solving the zero‐sum game problem is proposed, which can be used to solve the Hamilton‐Jacobi‐Isaacs equation associated with the optimal regulation control problem. The convergence analysis based on the value function and control strategy is given. For algorithm implementation, neural networks are used to approximate the control strategy, disturbance strategy, and value function of the game dynamic programming, respectively. A model network is used to approximate the system states with an unknown dynamical model. The iterative algorithm steps are given. Finally, the validity of the method is verified by simulation examples. [ABSTRACT FROM AUTHOR]

This article provides sufficient conditions for the existence of solutions for two‐person zero‐sum games with inf/sup‐compact payoff functions and with possibly noncompact decision sets for both players. Payoff functions may be unbounded, and we do not assume any convexity/concavity‐type conditions. For such games expected payoff may not exist for some pairs of strategies. The results of this article imply several classic facts. The article also provides sufficient conditions for the existence of a value and solutions for each player. The results of this article are illustrated with the number guessing game. [ABSTRACT FROM AUTHOR]

This paper deals with a robust H∞$$ {H}_{\infty } $$ tracking problem with a discounted factor. A new auxiliary system is established in terms of norm‐bounded time‐varying uncertainties. It is shown that the robust discounted H∞$$ {H}_{\infty } $$ tracking problem for the auxiliary system solves the original problem. Then, the new robust discounted H∞$$ {H}_{\infty } $$ tracking problem is represented as a well‐known zero‐sum game problem. Moreover, the robust tracking Bellman equation and the robust tracking Algebraic Riccati equation (RTARE) are inferred. A lower bound of a discounted factor for stability is obtained to assure the stability of the closed‐loop system. Based on the auxiliary system, the system is reshaped in a new structure that is applicable to Reinforcement Learning methods. Finally, an online Q‐learning algorithm without the knowledge of system matrices is proposed to solve the algebraic Riccati equation associated with the robust discounted H∞$$ {H}_{\infty } $$ tracking problem for the auxiliary system. Simulation results are given to verify the effectiveness and merits of the proposed method. [ABSTRACT FROM AUTHOR]

This research aims to study the applicability of game theory to system safety and reliability decision‐making problems and corresponding objective conflicts using non‐cooperative games. The non‐cooperative games would solve the games considering non‐cooperative cognitive decision‐makers behaviors, which are commonly ignored by other system safety and reliability analysis (SSRA) techniques, assuming that there would be perfect cooperation between the players (decision‐makers). Game theory can also recognize and understand the decision‐makers' behaviors and provide a "win‐win" situation for all players and the best broader system outcomes. The paper also shows the use of dynamic game theory in system safety and reliability decision‐making problems over time. The results indicate the effectiveness and efficiency of game theory and show how this can better reflect decision‐makers' opinions in system safety and reliability decision‐making problems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimal control strategies for model‐free zero‐sum games involving the H∞$$ {H}_{\infty } $$ control. The key contribution is the development of a Q‐learning algorithm for linear quadratic games without knowing the system dynamics. The finite‐horizon setting is more practical than the infinite‐horizon setting, but it is difficult to solve the time‐varying Riccati equation associated with the finite‐horizon setting directly. The proposed algorithm is shown to solve the time‐varying Riccati equation iteratively without the use of models, and numerical experiments on aircraft dynamics demonstrate the algorithm's efficiency. [ABSTRACT FROM AUTHOR]

This paper investigates how women leaders in the UK negotiate claims of being competitive by internalizing competition. Competition is a critical component in neoliberal contexts; yet its gendered implications are under research. Through analysis of 18 women leaders' narratives who directly characterize themselves as 'competitive with myself,' we theorize how and why competition is directed at the self. We understand articulations of 'I'm competitive with myself as a discursive strategy, which functions in the narratives in three interconnected ways. 'Competitive with myself' versus 'competitive with others' explains how women leaders internalize competition by rejecting competition with others and distancing from the conventional notion of zero‐sum game competition. 'Competing with myself for perfection' and 'Competitive with myself as a protective shield' explain why women leaders internalize competition—to perfect the self and navigate the double standards of a gendered neoliberal workplace. We argue that 'competitive with myself' as a discursive strategy enables women leaders to openly claim competitiveness, (an undesirable performance for women) and simultaneously distance themselves from it. The study contributes understandings of competition as gendered under neoliberalism and in patriarchal men‐dominated leadership roles and workplaces. Through a nuanced discussion of women leaders' narratives, we identify both an obligation to compete and a possible flexing of gender norms in relation to competition. [ABSTRACT FROM AUTHOR]

This paper proposes a zero‐sum dynamic game (ZSDG) design to mitigate frequency deviations in the secondary control layer of islanded AC microgrids. By defining a min‐max optimization problem, where the control input minimizes the frequency deviations while the external disturbances maximizes the cost function, a robust control law is designed. The outcome leads to setting the frequency to its desired value and at the same time effects of disturbances are attenuated by applying the H∞$H_{\infty }$ optimal controller in the secondary control level of AC microgrids. Since the basic ZSDG controller cannot eliminate adverse effects of external disturbances on the system frequency, an extended‐ZSDG (EZSDG) method is introduced to design an effective H∞$H_{\infty }$ controller. Due to the lack of access to the state variables of the microgrids, a full‐order observer is designed to estimate these variables based on the measured output. In simulation results, the validity of the proposed EZSDG is investigated by considering uncertainties in both the communication and physical layers. [ABSTRACT FROM AUTHOR]

A complex system exhibiting uncertainty as well as randomness may be portrayed by an uncertain random singular difference equation. This paper investigates two‐person nonzero‐sum and zero‐sum games based on uncertain random singular difference equations. First, an approach is proposed to translate the two‐person nonzero‐sum game into an equivalent game for a standard uncertain random dynamic system. The relevant recursive equations are developed to search the Nash equilibrium for the converted game. Solving the related recursive equations yields the solution to such a game. Following that, a Max‐Min Theorem is provided for finding the saddle‐point equilibrium of an uncertain random two‐person zero‐sum game. Finally, a numerical example is offered to demonstrate the validity of the findings. [ABSTRACT FROM AUTHOR]

In this article, the H∞$$ {H}_{\infty } $$ safe control problem of continuous‐time affine nonlinear safety‐critical systems is studied based on the barrier function (BF) and adaptive dynamic programming (ADP). We show that the safety constraints in this article occur under the condition that the system initial state is unsafe and have not been adequately addressed in the existing work. First, the H∞$$ {H}_{\infty } $$ control problem is transformed into a zero‐sum game problem, and a new safe Hamilton–Jacobi–Isaacs equation is proposed by combining with the BF, which makes the unsafe behavior be punished in the learning process. In addition, a damping coefficient is introduced into the BF to punish the unsafe behavior more flexibly. Aiming at the requirement that the initial system state must be strictly constrained in the safe set, a new weight updating method based on ADP is proposed, which can reasonably avoid the influence of the BF on neural network (NN) parameters when the initial system state is unsafe. Furthermore, based on the Lyapunov stability theory, it is proved that the system states of safety‐critical systems and the NN parameters are uniformly ultimately bounded under the safety constraints and disturbances effect. Finally, the effectiveness of the proposed method is verified by two simulation examples. [ABSTRACT FROM AUTHOR]

Solving non‐cooperative zero‐sum multi‐objective Games (zsMOGs), under undecided objective preferences results, for each of the players, in a Set of Rationalizable Strategies (SRS) to choose from. First, this paper deals with finding for each of the players a preferred subset of such rationalizable strategies based on a‐priori incorporation of partial preferences of the decision‐makers using secondary criteria. The obtained subset is termed the Set of Preferred Strategies (SPS). Here, a novel archive‐based co‐evolutionary algorithm is suggested to search for the SPS for each of the players. An academic example is suggested to demonstrate and validate the algorithm. It concerns a zsMOG that involves two adversarial planar manipulators. Based on a theorem that is proven here, a theoretic reference SRS is found for each of the players. This reference SRS is applied to find a reference SPS, which is used for validating the algorithm. Next, a comparison study is performed between the proposed archive‐based co‐evolutionary algorithm and an elite‐based version of this algorithm. The results clearly show that the archive‐based algorithm is superior to the elite‐based version, yielding results that correspond well to the theoretic sets. [ABSTRACT FROM AUTHOR]

Summary: This article presents a multiplayer zero‐sum game optimal control for modular robot manipulators (MRMs) via adaptive dynamic programming (ADP). The dynamic model of MRMs with interconnected dynamic couplings (IDCs) is formulated. The control law and IDC effect are regarded as players in each module. The optimal tracking control problem of the MRM system can be transformed into a multiplayer zero‐sum game. Base on ADP algorithm, the Hamilton–Jacobi–Issacs (HJI) equation is solved via critic neural network. Then, the optimal control law can be derivated. The closed‐loop robotic system is proved to be asymptotic stable and experiments are conducted to verify the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

Humanity's growing appetites for food and energy are placing unprecedented yield targets on our lands. Chasing those ever‐expanding land intensification targets gave rise to monocultures and sharpened the divide between food and energy production groups. Here, we argue that this does not have to be a zero‐sum game if food and energy can be co‐generated in the same land. Co‐generation can lead to sustainable intensification but requires a paradigm shift in the way we manage our resources, particularly light. Using an extended model of plant photosynthesis and transpiration, we demonstrate how plants react to different incident light spectra and show that manipulating light could be effective for boosting land and water efficiencies, thus potentially improving soil health. This knowledge can possibly unlock the real potential of promising modern agricultural technologies that target optimization of light allocations such as agrivoltaics. This study suggests that the blue part of the light spectrum is less efficient in terms of carbon assimilation and water use and could be more effectively used to produce solar energy, while the red part could efficiently produce biomass. A sensitivity analysis to the most important crop and environmental variables (irradiance, air temperature, humidity, and CO2 concentration) shows that plant response to different light treatments is sensitive to environmental boundary conditions and is species‐specific. Therefore, further research is necessary to assess which crops and climates are more suitable to optimize the proposed food‐water‐energy nexus. Plain Language Summary: Achieving sustainability for the deeply interlinked water, energy, and food systems requires revolutionary, rather than incremental, solutions at their nexus. This includes forms of food and energy co‐generation like agrivoltaics, which can boost land and water efficiencies and improve soil health. Advancements along those lines require deep understanding of how plants react to different incident light spectra. In this paper, we demonstrate the importance of different light spectra, and show that those spectra, if optimized in terms of their utilization, could lead to sustainable and more efficient food and energy systems. For example, the red part of the light spectrum is more efficient in terms of carbon assimilation and water use while the blue part could be more effectively used to produce solar energy. Key Points: We develop a photosynthesis and transpiration model to account for different light spectraThe red part of the light spectrum is more efficient for plant photosynthesis and water useThe blue part of the light spectrum could be used for solar energy production [ABSTRACT FROM AUTHOR]

In this paper, we outline how science teachers might engage in the work of creating educational equity. While acknowledging the historical inherent inequities associated with issues of access, opportunities to engage in science learning for individuals of marginalized identities (e.g., BIPOC individuals and women), and achievement, we broaden this definition to include social justice as a framework by which we can develop opportunities for the fostering of students' affinity identities with science. To this end, we draw on theorizations of equity within educational research, specifically discussed as excellence, equality, fairness, a zero‐sum game, and most recently, social justice. Additionally, we utilize McKinney de Royston and Nasir's (2017) Racialized Learning Ecologies framework. This framework provides a useful lens to notice the layers of (in)equity within education. We then extend this ecological model into science education and present three lenses (i.e., layers) through which equity operates within science teaching and learning. We conclude with a discussion of the practical implications of doing the work of equity, that is, recognizing, interpreting, and redressing inequity in science classrooms. Ultimately, we provide an actionable definition of equity that has the potential to facilitate transformative and socially just science teaching and learning. [ABSTRACT FROM AUTHOR]

An event‐triggered‐based approximate optimal control is developed for modular robot manipulators (MRMs) using zero‐sum game strategy. By utilizing the joint torque feedback method, robotic systems' dynamic model is formulated and state space equation is derived. According to event‐triggered mechanism and neural network algorithm, the tracking control issue is converted to a zero‐sum game optimal control issue. The optimal control policy and worst disturbance policy are acquired by Hamilton–Jacobi–Isaacs function respectively. The MRM system's tracking error is ultimately uniformly bounded. Finally, experiments demonstrate advantages of the method. [ABSTRACT FROM AUTHOR]

In this article, an output‐feedback Q‐learning algorithm is proposed for the discrete‐time linear system to deal with the H∞$$ {H}_{\infty } $$ tracking control problem. The problem is formulated as a zero‐sum game in the Stackelberg game framework with a discount factor to make the value function bounded. According to the principle of optimality, the game algebraic Riccati equation (GARE) is derived and solved by the Q‐learning algorithm to get the optimal solution of the Stackelberg game without requiring the knowledge of system dynamics and state. It is proved that the solution of the algorithm converges to the optimal control input and the worst‐case disturbance with excitation noises during training, and the Stackelberg strategy can achieve a lower L2$$ {L}_2 $$ disturbance attenuation level than the Nash one. Moreover, the impacts of the discount factor on the stability of the closed‐loop system and solvability of the GARE are analyzed to provide some criteria for the choice of the discount factor. Simulation examples are provided to validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

We define the distance between two information structures as the largest possible difference in value across all zero‐sum games. We provide a tractable characterization of distance and use it to discuss the relation between the value of information in games versus single‐agent problems, the value of additional information, informational substitutes, complements, or joint information. The convergence to a countable information structure under value‐based distance is equivalent to the weak convergence of belief hierarchies, implying, among other things, that for zero‐sum games, approximate knowledge is equivalent to common knowledge. At the same time, the space of information structures under the value‐based distance is large: there exists a sequence of information structures where players acquire increasingly more information, and ε > 0 such that any two elements of the sequence have distance of at least ε. This result answers by the negative the second (and last unsolved) of the three problems posed by Mertens in his paper "Repeated Games" (1986). [ABSTRACT FROM AUTHOR]

Network densification is recognized as the key technology to meet the ever growing demand of data traffic in next generation wireless networks. However the proliferation of small cell base stations (SCBSs) introduces vulnerabilities to information security, as they are prone to eavesdropping attacks. In this paper, it is studied how user association strategies can be specifically tailored to meet this security challenge in a ultra dense heterogeneous cellular network(UDHCN) with a group of colluding eavesdroppers. In particular, the situation is considered where eavesdroppers may have limited capabilities in intercepting and decoding data traffic. In this setting each eavesdropper has to make its own decision on eavesdropping targets, and they are able to take concerted actions to sabotage information security. To address this challenge, a zero sum game framework to reflect the conflicting interests of users and eavesdroppers is proposed and a user association scheme is devised that aims to maximize system sum secrecy rate against these adversaries whose actions intend to minimize sum secrecy rate. The case that all adversaries have limited eavesdropping capabilities is first considered, and it is shown that the corresponding zero sum game is indeed a bilinear game, and its Nash equilibrium solution can be readily approximated using a saddle point Frank Wolfe(SP‐FW)algorithm. Then the framework is extended to the case where adversaries with a diverse configuration of eavesdropping capabilities exist. In this case, it is shown that the underlying minimax optimization problem is indeed a nonsmooth convex‐nonconcave one. A two stage method is proposed by first smoothing the objective function with a Hermite cubic polynomial approximation, and then obtaining a nearly stationary solution via a recently proposed proximal point gradient descent method. Simulation results show that the proposed framework leads to significant increases in sum secrecy rate and secrecy probability against both the capability‐limited adversaries and a hybrid type of adversaries. [ABSTRACT FROM AUTHOR]

This article discusses the author's experience with the peer review process in clinical research and the impact it has on funding for respiratory disease research. The author emphasizes the importance of fair and constructive criticism in the peer review process, as well as the need for funding committees and editors to identify and address unfair reviews. The author suggests seeking feedback from trusted colleagues and utilizing internal peer review processes before submitting work. The article concludes by stating that funding and reporting on respiratory research should not be a zero-sum game. [Extracted from the article]

Abstract: The aim of this paper is to propose a solution method for 2‐player zero sum games with fuzzy payoffs, which takes different risk attitudes of the players into account. In the proposed model, players' risk attitudes are modeled with α‐cut concept of fuzzy logic, and the game is solved by multi‐objective linear programming approach. The model is implemented on an online marketing problem to show its applicability and use. The game solutions for both players are found by optimizing specific α‐cuts that represent the risk levels of players and given as the game value and corresponding efficient mixed strategies. The results indicated that the proposed model provides a flexible decision‐making environment to the players for their different risk attitudes. [ABSTRACT FROM AUTHOR]

Abstract: Gender‐based zero‐sum thinking reflects beliefs that women's status gains correspond directly with men's status losses. These beliefs may help explain people's resistance to gender equity. Here, two studies examined the association between men's zero‐sum thinking and workplace gender biases. In Study 1, men (N = 235) employed in workplaces with stronger masculinity contest norms reported observing stronger bias against women in the workplace, and this effect was mediated by an increase in their own zero‐sum thinking. In Study 2, college students (N = 269) read information that either threatened or affirmed the gender status hierarchy and then reported their zero‐sum thinking and their support for gender fair workplace policies that undermine the masculinity contest. Men, but not women, reduced support for gender fair policies following a gender hierarchy threat, and this effect was mediated by an increase in their zero‐sum thinking. Discussion considers the workplace policy implications of these findings. [ABSTRACT FROM AUTHOR]

We develop a theory of how the value of an agent's information advantage depends on the persistence of information. We focus on strategic situations with strict conflict of interest, formalized as stochastic zero-sum games where only one of the players observes the state that evolves according to a Markov operator. Operator Q is said to be better for the informed player than operator P if the value of the game under Q is higher than under P regardless of the stage game. We show that this defines a convex partial order on the space of ergodic Markov operators. Our main result is a full characterization of this partial order, intepretable as an ordinal notion of persistence relevant for games. The analysis relies on a novel characterization of the value of a stochastic game with incomplete information. [ABSTRACT FROM AUTHOR]

This study is concerned with the optimal control and scheduling problem for linear networked control systems with communication constraints and security issues. The communication channel, which connects remote sensors with a controller, has limited transmission capability and may suffer denial of service attacks from an external attacker. Unlike most existing cyber-attack-related literature which presumed a prior well-designed control or estimating strategy on which triggering and jamming strategies were established, in the present work, optimal strategies of the controller, the trigger as well as the external attacker are considered simultaneously under the renowned linear quadratic criteria. A designer, who can jointly devise the controller and the trigger, aims to improve the system's performance while the attacker's goal is just the opposite. With the assumption that the trigger and the attacker have limited opportunities to send or attack, a zero-sum static game of complete information is first utilised to investigate the optimal strategies for both players, i.e. the designer and the attacker. The existence of Nash equilibrium (NE) point, i.e. the combination of optimal strategy for each player, is proven afterwards. Finally, the property of none pure-strategy NE in a special case is provided with rigorous proofs. A numerical example is employed to demonstrate how to derive the optimal strategies for the designer and the attacker. [ABSTRACT FROM AUTHOR]

In this paper, stochastic optimal strategy for unknown linear discrete-time system quadratic zero-sum games in input-output form with communication imperfections such as network-induced delays and packet losses, otherwise referred to as networked control system (NCS) zero-sum games, relating to the H8 optimal control problem is solved in a forward-in-time manner. First, the linear discrete-time zero sum state space representation is transformed into a linear NCS in the state space form after incorporating random delays and packet losses and then into the input-output form. Subsequently, the stochastic optimal approach, referred to as adaptive dynamic programming (ADP), is introduced which estimates the cost or value function to solve the infinite horizon optimal regulation of unknown linear NCS quadratic zero-sum games in the presence of communication imperfections. The optimal control and worst case disturbance inputs are derived based on the estimated value function in the absence of state measurements. An update law for tuning the unknown parameters of the value function estimator is derived and Lyapunov theory is used to show that all signals are asymptotically stable (AS) and that the estimated control and disturbance signals converge to optimal control and worst case disturbances, respectively. Simulation results are included to verify the theoretical claims. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we present an online learning algorithm to find the solution to the H ∞ control problem of continuous-time systems with input constraints. A suitable nonquadratic functional is utilized to encode the input constraints into the H ∞ control problem, and the related H ∞ control problem is formulated as a two-player zero-sum game with a nonquadratic performance. Then, a policy iteration algorithm on an actor-critic-disturbance structure is developed to solve the Hamilton-Jacobi-Isaacs (HJI) equation associated with this nonquadratic zero-sum game. That is, three NN approximators, namely, actor, critic, and disturbance, are tuned online and simultaneously for approximating the HJI solution. The value of the actor and disturbance policies is approximated continuously by the critic NN, and then on the basis of this value estimate, the actor and disturbance NNs are updated in real time to improve their policies. The disturbance tries to make the worst possible disturbance, whereas the actor tries to make the best control input. A persistence of excitation condition is shown to guarantee convergence to the optimal saddle point solution. Stability of the closed-loop system is also guaranteed. A simulation on a nonlinear benchmark problem is performed to validate the effectiveness of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, multiperson zero-sum differential games for a class of continuous-time uncertain nonlinear systems are solved using a new iterative adaptive dynamic programming (ADP) algorithm. The idea is to use ADP technique to obtain the optimal control pair iteratively that makes the performance index function reach the optimal solution of the zero-sum differential games without the system model. It proves that the iterative performance index functions are convergent to the optimal solution of the game. Stability properties of the system under the iterative control pairs are also presented. Neural networks are used to build the system model, approximate the performance index function, and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP method. Finally, two simulation examples are given to demonstrate the performance of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Two forces engage in a duel, with each force initially consisting of several heterogeneous units. Each unit can be assigned to fire at any opposing unit, but the kill rate depends on the assignment. As the duel proceeds, each force-knowing which units are still alive in real time-decides dynamically how to assign its fire, in order to maximize the probability of wiping out the opposing force before getting wiped out. It has been shown in the literature that an optimal pure strategy exists for this two-person zero-sum game, but computing the optimal strategy remained cumbersome because of the game's huge payoff matrix. This article gives an iterative algorithm to compute the optimal strategy without having to enumerate the entire payoff matrix, and offers some insights into the special case, where one force has only one unit. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 61: 56-65, 2014 [ABSTRACT FROM AUTHOR]

The article offers the author's criticisms of what he calls U.S. President Barack Obama's zero-sum philosophy and his related views on wealth redistribution. He offers examples of areas where redistribution has failed, discussing the economic situation in the euro zone as of January 2013, Somali pirates, and political corruption in the BRIC nations of Brazil, Russia, India, and China. China's economic philosophy, the al-Qaeda terrorist group, and imperialism in Darfur, Sudan, Kosovo, and Tibet, China.