Search

Academic rankings, despite their pitfalls, play a major role in the "business of business schools." The claim that "rankings are here to stay" continues to be frequently accepted at face value. This essay challenges this situation, arguing that while many business schools perceive rankings as a source of legitimacy, their participation in rankings can actually undermine the very legitimacy they seek. By adopting an effectual stance—which focuses on problem-solving and innovation using available resources—we theorize about a phased approach to steer business schools (and other higher education institutions) away from reliance on "ranking-based legitimacy" toward "research-based legitimacy." In the first phase, deans and academics can consider short-term bridging measures that do not fundamentally change the ranking game but influence it in self-serving directions. Rather than being powerless "ranking takers," some proactive approaches can allow business schools to change the zero-sum game nature of rankings and become "ranking makers." In the second phase, deans and academics can challenge and reject the ranking game by reaffirming the true purpose of business education. We encourage deans and academics to seize back control and restore research-based legitimacy by rigorously addressing managerial, societal, and ethical issues. [ABSTRACT FROM AUTHOR]

In many game theoretic environments, the precise crisp values of payoffs are not always easy to gather as real-life decision-making problems are not inevitably precise and symmetrically distributed in nature. Hence, there is a need to explore matrix games having unbalanced probabilistic linguistic information. To fill this research gap, this study proposes a concept of probabilistic multiplicative unbalanced linguistic (PM-UL) game in which the players express their responses in terms of PM-UL term. This type of PM-UL game is a useful technique for multiple criteria analysis. Further, to thoroughly capture the uncertainty involved in payoffs of such games, they are converted to probabilistic unbalanced linguistic clouds (PULCs) that describe the uncertainties of qualitative payoffs by implementing the uncertain transformation between PM-UL payoffs and quantitative values. The PULCs are represented by three numerical characteristics, i.e. expectation, entropy and hyperentropy. Hence, this model overcomes the inherent limitation of existing linguistic games where only the fuzziness of concepts is evaluated, by providing a PULC strategy and PULC value of the game. This procedure overcomes the loss of information occurring in the process of transformation of payoff values into quantitative ones. The defined methodology is implemented on a real-life working problem to demonstrate the effect and applicability in decision-making circumstances. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid load frequency control (LFC) scheme is proposed for multi-area interconnected power systems to decouple the intricate double control objectives, by dividing all subareas into the responsible areas and the free areas. The LFC in the responsible area has the function of regulating both the local frequency and the tie-line power, while the control objective of the LFC in the free area is thus simplified to regulate the local frequency only. Then, addressing the complex network coupling and uncertain dynamics, an integrated LFC controller is proposed for the free areas, which consists of two parts, namely, the coupling attenuation baseline controller and the disturbance compensation controller. The coupling attenuation baseline controller satisfying the predefined bounded L2-Gain condition is derived based on the solution to a multi-player zero-sum differential game. Additionally, a novel generalized integral observer is designed to estimate the system's integrated disturbance, and the corresponding disturbance compensation controller is derived. After that, the ultimately uniformly bounded (UUB) stability of the integrated LFC controller combining baseline controller and disturbance compensation controller is proven rigorously. Finally, the performance superiority of the proposed hybrid LFC scheme is validated by the simulations in challenging operating modes. [ABSTRACT FROM AUTHOR]

The authors of the present article pose and consider in detail the highly relevant issue of whether capitalism has a future or whether it will be replaced by some new system. And if it does have a future, then what future will it be? These questions are nothing new, but in the recent few years, just like a hundred years ago, they have acquired exceptional importance, since not only the Left but also the representatives of the largest financial capital started speaking about the end of capitalism. The present article considers the following aspects: what is capitalism as an economic sector and as an economic system? How did capitalism evolve? What are its potentialities? The authors also make some forecasts for the next 50–100 years. Various scenarios for the future of capitalism are considered: from maintaining its leading role to its reduction to a sector. It is assumed that in the coming decades, the development of capitalism may proceed in different ways in developed and developing countries, which may result in a complex continuum of types, transitions, combinations, and forms of capitalism. In some countries, the development of capitalism has reached a level when it has already exhausted itself and needs transformations; meanwhile, there are countries where full-fledged market relations have not yet emerged. The authors analyze two main types of capitalism. The developed countries are likely to experience a shift toward strengthening of distributive institutions. In their turn, the developing countries that continue the processes of economic and socio-political modernization are likely to proceed toward strengthening of capitalist market institutions, though with some important restrictions preventing exploitation and environmental degradation. The authors believe that capitalism will be inevitably transformed in one way or another, but such transformations can vary greatly. [ABSTRACT FROM AUTHOR]

This paper investigates a parameter-free H∞ differential game approach for nonlinear active vehicle suspensions. The study accounts for the geometric nonlinearity of the half-car active suspension and the cubic nonlinearity of the damping elements. The nonlinear H∞ control problem is reformulated as a zero-sum game between two players, leading to the establishment of the Hamilton–Jacobi–Isaacs (HJI) equation with a Nash equilibrium solution. To minimize reliance on model parameters during the solution process, an actor–critic framework employing neural networks is utilized to approximate the control policy and value function. An off-policy reinforcement learning method is implemented to iteratively solve the HJI equation. In this approach, the disturbance policy is derived directly from the value function, requiring only a limited amount of driving data to approximate the HJI equation's solution. The primary innovation of this method lies in its capacity to effectively address system nonlinearities without the need for model parameters, making it particularly advantageous for practical engineering applications. Numerical simulations confirm the method's effectiveness and applicable range. The off-policy reinforcement learning approach ensures the safety of the design process. For low-frequency road disturbances, the designed H∞ control policy enhances both ride comfort and stability. [ABSTRACT FROM AUTHOR]

This paper focuses on investigating the optimal actuator location for achieving minimum norm controls in the context of approximate controllability for degenerate parabolic equations. We propose a formulation of the optimization problem that encompasses both the actuator location and its associated minimum norm control. Specifically, we transform the problem into a two-person zero-sum game problem, resulting in the development of four equivalent formulations. Finally, we establish the crucial result that the solution to the relaxed optimization problem serves as an optimal actuator location for the classical problem. [ABSTRACT FROM AUTHOR]

The affine formation maneuver (AFM) control problem using the zero-sum differential game is studied for a second-order multi-agent system (MAS) against cyber-injection attacks on the actuators. First and foremost, the distributed control signals and the cyber-injected attack signals are viewed as two competing teams. Then, the leader-follower tracking error system is established where the followers aim to track the AFMs of the leaders. The leader-follower distributed optimal control policies and the cyber-attack attenuation policies are derived from the Nash equilibrium solution of the Hamilton-Jacobi-Isaac (HJI) equation. The solution of the HJI equation is estimated with the aid of the critic neural network. The weights of the critic system are learned online based on the adaptive dynamic programming (ADP) scheme. The uniformly ultimately bounded stability of the closed-loop system under the formulated zero-sum differential game control strategy has been proven by the Lyapunov stability theorem. A simulation example illustrates that the presented control method can attenuate cyber-injection attacks on the actuators and maintain the AFMs of the agents. [ABSTRACT FROM AUTHOR]

This paper deals with a two-person zero-sum differential game for a dynamical system described by a Caputo fractional differential equation of order α ∈ (0 , 1) and a Bolza cost functional. The differential game is associated to the Cauchy problem for the path-dependent Hamilton–Jacobi–Bellman–Isaacs equation with so-called fractional coinvariant derivatives of order α and the corresponding right-end boundary condition. A notion of a viscosity solution of the Cauchy problem is introduced, and the value functional of the differential game is characterized as a unique viscosity solution of this problem. [ABSTRACT FROM AUTHOR]

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]

To effectively grasp the interactive evolution mechanism of relevant entities in the process of competitive neutrality and reveal the impact of the implementation effect of the principle of competitive neutrality on the evolution path of the game system is the key to guiding the development of the government, industry associations and enterprises with reasonable policies. The equilibrium point of the game is analyzed by the four-party evolutionary game model, and the influence of the change of relevant parameters on the evolution result of the game system is further simulated by numerical simulation. The results show that the central government supervision plays an important role in guiding the strategy selection of different game players. By raising the penalty standards for nonneutral competitive behavior of market players and increasing the non-neutral penalty for local governments, the system can evolve faster to the ideal state of "loose supervision by the central government, neutral implementation by local governments, strong supervision by industry associations, and active cooperation by enterprises." Therefore, to improve the execution effect of competitive neutrality, the central government shall formulate a reasonable regulatory system, and to maximize inspire the enthusiasm of local government, industry associations and enterprises, to promote stability in the sustainable development of the market. [ABSTRACT FROM AUTHOR]

This paper endeavours to directly establish the observability inequality of backward stochastic heat equations involving a Lévy process for measurable sets. As an immediate application, we attain the approximate controllability of forward stochastic heat equations featuring a Lévy process. Subsequently, we embark upon the formulation of a nuanced optimization problem, encompassing both the determination of an optimal actuator location and its associated minimum norm control. This formulation is elegantly cast as a two-person zero-sum game problem. We then proceed to articulate a set of conditions–both sufficient and necessary–required for optimizing the solution through the prism of a Nash equilibrium. At last, we prove that the relaxed optimal solution is an optimal actuator location for the classical problem. [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]

In recent years, with the accelerated development of technology towards automation and intelligence, autonomous decision-making capabilities in unmanned systems are poised to play a crucial role in contemporary aerial two-player zero-sum games (TZSGs). Deep reinforcement learning (DRL) methods enable agents to make autonomous manoeuvring decisions. This paper focuses on current mainstream DRL algorithms based on fundamental tactical manoeuvres, selecting a typical aerial TZSG scenario—within visual range (WVR) combat. We model the key elements influencing the game using a Markov decision process (MDP) and demonstrate the mathematical foundation for implementing DRL. Leveraging high-fidelity simulation software (Warsim v1.0), we design a prototypical close-range aerial combat scenario. Utilizing this environment, we train mainstream DRL algorithms and analyse the training outcomes. The effectiveness of these algorithms in enabling agents to manoeuvre in aerial TZSG autonomously is summarised, providing a foundational basis for further research. [ABSTRACT FROM AUTHOR]

We consider a class of zero-sum stopper versus singular-controller games in which the controller can only act on a subset d0 < d of the d coordinates of a controlled diffusion. Due to the constraint on the control directions these games fall outside the framework of recently studied variational methods. In this paper we develop an approximation procedure, based on L1-stability estimates for the controlled diffusion process and almost sure convergence of suitable stopping times. That allows us to prove existence of the game's value and to obtain an optimal strategy for the stopper under continuity and growth conditions on the payoff functions. This class of games is a natural extension of (single-agent) singular control problems, studied in the literature, with similar constraints on the admissible controls. [ABSTRACT FROM AUTHOR]

In this paper, we study discounted stochastic games with an additive rewards and transitions (ARAT) structure. We assume that the transition probabilities are dominated by some probability measures on a countably generated measurable state space and are norm continuous in actions of the players. We prove the existence of Markov perfect equilibria in which the players can use randomization on two pure actions in each state. When the state space is countable, then we show that there exists a pure Markov perfect equilibrium. Assuming additionally that the transition probabilities have no conditional atoms, we establish some existence results on pure Markov perfect equilibria for ARAT games with uncountable state space using a version of Lyapunov's theorem for conditional expectation of correspondences due to Dynkin and Evstigneev. We also include a wide-ranging discussion on equilibria for discounted stochastic games with a general state space. [ABSTRACT FROM AUTHOR]

Content moderation is typically viewed as an affront to free expression. When companies remove online abuse, they face accusations of censorship. Lost in the discussion is the fact that victims of intimate privacy violations and cyberstalking typically-and regrettably-withdraw from on- and offline activities. Online assaults chase targeted individuals offline; they silence victims. Content moderation can secure opportunities for people to speak. Legal and corporate prohibitions against intimate privacy violations and cyberstalking can help provide the reassurance that victims need to stay online. They can endow individuals with a sense of trust so they continue to use networked technologies to express themselves. Those prohibitions are consonant with First Amendment doctrine and free speech values. Combating online abuse isn't a zero-sum game with free speech as the loser. Rather, it can free us to speak by changing the culture that rewards abuse and encourages self-censorship. [ABSTRACT FROM AUTHOR]

How are the advantage relations between a set of agents playing a game organized and how do they reflect the structure of the game? In this paper, we illustrate 'Principal Trade-off Analysis' (PTA), a decomposition method that embeds games into a low-dimensional feature space. We argue that the embeddings are more revealing than previously demonstrated by developing an analogy to Principal Component Analysis (PCA). PTA represents an arbitrary two-player zero-sum game as linear combination of simple games via the projection of policy profiles into orthogonal 2D feature planes. We show that the feature planes represent unique strategic trade-offs and truncation of the sequence provides insightful model reduction and visualization. We demonstrate the validity of PTA on a quartet of games (Kuhn poker, RPS + 2, Blotto and Pokemon). In Kuhn poker, PTA clearly identifies the trade-off between bluffing and calling. In Blotto, PTA identifies game symmetries and specifies strategic trade-offs associated with distinct win conditions. These symmetries reveal limitations of PTA unaddressed in previous work. For Pokemon, PTA recovers clusters that naturally correspond to Pokemon types, correctly identifies the designed trade-off between those types, and discovers a rock-paper-scissor (RPS) cycle in the Pokemon generation type – all absent any specific information except game outcomes. [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]

Accepted by: Phil Scarf We model tactical changes in association football as a Markov game. The pitch is discretized into nine zones and the states of the Markov game are defined according to the zone in which the ball is located in-play, the team in possession and the score. We first model tactical changes in a Markov decision process framework, wherein one team maximizes their probability of winning. Then, we model tactical changes in a two-person zero-sum Markov game framework, wherein both teams maximize their probability of winning. Fundamental to our modelling is the notion that tactical changes impact upon transition rates. We verify the models using data from matches in a season of the Japan Professional Football League. We define a change in transition rates that can be realized by changes in tactics, and illustrate an example of optimal tactical changes when both teams can vary their tactics. The models we develop in the paper can support managers who are considering important decisions about substitutions and changes to formation, for example, when a match is in-play. [ABSTRACT FROM AUTHOR]

To achieve the intelligent interception of different types of maneuvering evaders, based on deep reinforcement learning, a novel intelligent differential game guidance law is proposed in the continuous action domain. Different from traditional guidance laws, the proposed guidance law can avoid tedious manual settings and save cost efforts. First, the interception problem is transformed into the pursuit–evasion game problem, which is solved by zero-sum differential game theory. Next, the Nash equilibrium strategy is obtained through the Markov game process. To implement the proposed intelligent differential game guidance law, an actor–critic neural network based on deep deterministic policy gradient is constructed to calculate the saddle point of the differential game guidance problem. Then, a reward function is designed, which includes the tradeoffs among guidance accuracy, energy consumption, and interception time. Finally, compared with traditional methods, the interception accuracy of the proposed intelligent differential game guidance law is 99.2%, energy consumption is reduced by 47%, and simulation time is shortened by 1.58 s. All results reveal that the proposed intelligent differential game guidance law has better intelligent decision-making ability. [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]

Unmanned surface pursuit is a complex and challenging engineering problem, especially when conducted by multiple intelligent surface vehicles (ISVs). To enhance the pursuit performance and facilitate strategic interaction during the target pursuit, this paper proposes a novel game theory-based maneuver planning method for pursuit ISVs. Firstly, a specific two-player zero-sum pursuit–evasion game (ZSPEG)-based target-pursuit model is formed. To ensure the vehicles reach a quick consensus, a target-guided relay-pursuit mechanism and the corresponding pursuit payoffs are designed. Meanwhile, under the fictitious play framework, the behavioral pattern and the strategies of the target could be fictitiously learned. Furthermore, mixed-strategy Nash equilibrium (MNE) is employed to determine the motions for the vehicles, the value of which is the best response in the proposed ZSPEG model. Finally, simulations verify the effectiveness of the above methods in multi-ISV surface pursuit. [ABSTRACT FROM AUTHOR]

Recent work on incorporating strong-correlation (sc) corrections into the scLH22t local hybrid functional [A. Wodyński and M. Kaupp, J. Chem. Theory Comput. 18, 6111–6123 (2022)] used a hybrid procedure, applying a strong-correlation factor derived from the reverse Becke–Roussel machinery of the KP16/B13 and B13 functionals to the nonlocal correlation term of a local hybrid functional. Here, we show that adiabatic-connection factors for strong-correlation-corrected local hybrids (scLHs) can be constructed in a simplified way based on a comparison of semi-local and exact exchange-energy densities only, without recourse to exchange-hole normalization. The simplified procedure is based on a comparative analysis of Becke's B05 real-space treatment of nondynamical correlation and that in LHs, and it allows us to use, in principle, any semi-local exchange-energy density in the variable used to construct local adiabatic connections. The derivation of competitive scLHs is demonstrated based on either a modified Becke–Roussel or a simpler Perdew–Burke–Ernzerhof (PBE) energy density, leading to the scLH23t-mBR and scLH23t-tPBE functionals, which both exhibit low fractional spin errors while retaining good performance for weakly correlated situations. We also report preliminary attempts toward more detailed modeling of the local adiabatic connection, allowing a reduction of unphysical local maxima in spin-restricted bond-dissociation energy curves (scLH23t-mBR-P form). The simplified derivations of sc-factors reported here provide a basis for future constructions and straightforward implementation of exchange-correlation functionals that escape the zero-sum game between low self-interaction and static-correlation errors. [ABSTRACT FROM AUTHOR]

We perform an exploratory data analysis on a data-set for the top 16 professional darts players from the 2019 season. We use this data-set to fit player skill models which can then be used in dynamic zero-sum games (ZSGs) that model real-world matches between players. We propose an empirical Bayesian approach based on the Dirichlet-Multinomial (DM) model that overcomes limitations in the data. Specifically we introduce two DM-based skill models where the first model borrows strength from other darts players and the second model borrows strength from other regions of the dartboard. We find these DM-based models outperform simpler benchmark models with respect to Brier and Spherical scores, both of which are proper scoring rules. We also show in ZSGs settings that the difference between DM-based skill models and the simpler benchmark models is practically significant. Finally, we use our DM-based model to analyze specific situations that arose in real-world darts matches during the 2019 season. [ABSTRACT FROM AUTHOR]

With the rapid development and wide application of sensing devices and communication networks, the traditional power system has transformed into a cyber physical power system (CPPS) gradually. The constant interaction of information flow and power flow exposes the grid to potential cyber-attack risks. In this paper, the defense strategy selection approaches are proposed based on incomplete information game against false data injection attack (FDIA) in different scenarios. Firstly, the attack-defense tree model containing economic indicators and a measure of attack is established, and the expected revenues of both attacker and defender are calculated in detail. Secondly, the static Bayesian attack-defense game model in which the defender grasps incomplete information is constructed, considering two cases of low-tech attacker and high-tech attacker. Then, the zero-sum attack-defense game model in which the attacker masters incomplete information is structured, considering the changing attack space. Finally, the simulations are carried out on the IEEE 30-bus system, and the optimal defense strategies in the case of two incomplete information are obtained by solving the equilibrium point. The simulation results demonstrate that the proposed method is effective in cyber security defense system, and can provide guiding ideology and theoretical basis for operators to deploy defense measures. [ABSTRACT FROM AUTHOR]

A finite zero-sum game defined on a subset of the unit square is considered. The game is a generalized progressive discrete silent duel, in which the kernel is skew-symmetric, and the players, referred to as duelists, have identical linear accuracy functions featured with an accuracy proportionality factor. As the duel starts, time moments of possible shooting become denser by a geometric progression. Apart from the duel beginning and end time moments, every following time moment is the partial sum of the respective geometric series. Due to the skew-symmetry, both the duelists have the same optimal strategies and the game optimal value is 0. If the accuracy factor is not less than 1, the duelist’s optimal strategy is the middle of the duel time span. If the factor is less than 1, the duel solution is not always a pure strategy saddle point. In a boundary case, when the accuracy factor is equal to the inverse numerator of the ratio that is the time moment preceding the duel end moment, the duel has four pure strategy saddle points which are of the mentioned time moments. For a trivial game, where the duelist possesses just one moment of possible shooting between the duel beginning and end moments, and the accuracy factor is 1, any pure strategy situation, not containing the duel beginning moment, is optimal. [ABSTRACT FROM AUTHOR]

The article has its roots in the concept of a community with a shared future for humanity, a Chinese innovation aimed at realizing the development and prosperity of human societies. This concept rests on an exploration, based in Marxism and sinicized Marxism, of how world history has evolved, and embodies six global initiatives as fundamental measures to respond to the real needs of world development at the current stage. Specifically, the Belt and Road Initiative, based on a new pattern of cooperation, strives to build a new model of common prosperity in both economic and cultural terms for countries around the world. The Global Development Initiative aims to upgrade the development agenda and formulate new guidelines for the common socio-economic development of all countries. The Global Security Initiative seeks to eliminate the old-style mentality formed by the zero-sum game thinking of the Cold War epoch, and to formulate a new conception of shared security for the international community. The Global Civilization Initiative sets out to disprove the myths of Western civilization and to develop a new concept of mutual learning between civilizations, while the Global AI Governance Initiative aims to build consensus and on that basis, to shape the common governance of scientific and technological progress. [ABSTRACT FROM AUTHOR]

In this paper, an optimisation problem for the monotone mean-variance (MMV) criterion is considered from the perspective of the insurance company. The MMV criterion is an amended version of the classical mean-variance (MV) criterion which guarantees the monotonicity of the utility function. With this criterion we study the optimal investment and reinsurance problem which is formulated as a zero-sum game between the insurance company and an imaginary player. We apply the dynamic programming principle to obtain the corresponding Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation. As the main conclusion of this paper, by solving the HJBI equation explicitly, the closed forms of the optimal strategy and the value function are obtained. Moreover, the MMV efficient frontier is also provided. At the end of the paper, a numerical example is presented. [ABSTRACT FROM AUTHOR]

Professor Peter Sudhölter, a renowned mathematician and economist, passed away on April 2, 2024, after a courageous battle with cancer. Throughout his career, he made significant contributions to the field of cooperative game theory, publishing 61 articles and one book. He obtained his PhD in mathematics from the University of Bielefeld in 1988 and went on to become a full professor at the University of Southern Denmark in 2013. Sudhölter's research focused on various topics, including cooperative games, non-transferable utility games, and the characterization of equilibrium concepts. His work has had a lasting impact on the field and will continue to influence future research. [Extracted from the article]

I describe a Bayesian persuasion problem where Receiver has a private type representing a cutoff for choosing Sender's preferred action, and Sender has maxmin preferences over all Receiver type distributions with known mean and bounds. This problem can be represented as a zero-sum game where Sender chooses a distribution of posterior mean beliefs that is a mean-preserving contraction of the prior over states, and an adversarial Nature chooses a Receiver type distribution with the known mean; the player with the higher realization from their chosen distribution wins. I formalize the connection between maxmin persuasion and similar games used to model political spending, all-pay auctions, and competitive persuasion. In both a standard binary-state setting and a new continuous-state setting, Sender optimally linearizes the prior distribution over states to create a distribution of posterior means that is uniform on a known interval with an atom at the lower bound of its support. [ABSTRACT FROM AUTHOR]

The paper analyzes the notion of an unbeatable strategy as a game-theoretic solution concept. A general framework (games with relative preferences) suitable for the analysis of this concept is proposed. Basic properties of unbeatable strategies are presented and a number of examples and applications considered. [ABSTRACT FROM AUTHOR]

This article serves as a preface to a special issue of the International Game Theory Review, dedicated to game theory and optimization in honor of Pierre Bernhard. It highlights Pierre's significant contributions to the scientific community, particularly in applied mathematics and game theory. The special issue contains 10 contributions from friends and collaborators of Pierre, covering a range of topics such as evolutionary economic models, dynamic game-theoretic frameworks, and mathematical biology. The article also includes testimonials from colleagues and friends, providing insight into Pierre's impact on their careers and his role in the development of INRIA-Sophia Antipolis. [Extracted from the article]

All the n possible returns on a financial asset are the components of an element of a linear space over R. This paper shows how to transfer all these n possible returns on a one-dimensional straight line. In this research work, two or more than two financial assets are studied. More than two financial assets are always studied in pairs, so they are treated inside the budget set of a given decision-maker. Two univariate financial assets give rise to a bivariate financial asset characterized by a bivariate (two-dimensional) distribution of probability. This research work shows how constrained choices being made by a given decision-maker under conditions of uncertainty and riskiness maximize his utility of an ordinal nature. For this reason, prevision bundles are dealt with. Furthermore, every choice identifies a zero-sum game. Since a specific kind of choice associated with two or more than two objects is investigated, new conceptual and mathematical outcomes related to financial decisions are shown. [ABSTRACT FROM AUTHOR]

We consider a variant of the hide-and-seek game in which a seeker inspects multiple hiding locations to find multiple items hidden by a hider. Each hiding location has a maximum hiding capacity and a probability of detecting its hidden items when an inspection by the seeker takes place. The objective of the seeker (respectively, hider) is to minimize (respectively, maximize) the expected number of undetected items. This model is motivated by strategic inspection problems, where a security agency is tasked with coordinating multiple inspection resources to detect and seize illegal commodities hidden by a criminal organization. To solve this large-scale zero-sum game, we leverage its structure and show that its mixed-strategy Nash equilibria can be characterized using their unidimensional marginal distributions, which are pure equilibria of a lower dimensional continuous zero-sum game. This leads to a two-step approach for efficiently solving our hide-and-seek game: First, we analytically solve the continuous game and derive closed-form expressions of the equilibrium marginal distributions. Second, we design a combinatorial algorithm to coordinate the players' resources and compute equilibrium mixed strategies that satisfy the marginal distributions. We show that this solution approach computes a Nash equilibrium of the hide-and-seek game in quadratic time with linear support. Our analysis reveals novel equilibrium behaviors driven by a complex interplay between the game parameters, captured by our closed-form solutions. Funding: This work was supported by the Georgia Tech Stewart Fellowship and the Georgia Tech New Faculty Start Up Grant. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0012. [ABSTRACT FROM AUTHOR]

A generalized class of the discrete game of timing is solved as a finite zero-sum game defined on a symmetric lattice of the unit square. The game is a progressive discrete silent duel whose kernel is skew-symmetric, and the players, referred to as duelists, have identical shooting accuracy functions featured with an accuracy proportionality factor a and a power constant β describing nonlinearity of the shooting accuracy. As the duel starts, time moments of possible shooting become denser by a geometric progression. Apart from the duel beginning and end time moments, every following time moment is the partial sum of the respective geometric series. Due to the skew-symmetry, both the duelists have the same optimal strategies and the game optimal value is 0. As the duelist has a single bullet, there is no reason for considering solutions in mixed strategies, if any, with non-singleton supports. If a ⩾ 2 β − 1 , the duelist's optimal strategy is the middle of the duel time span; otherwise, the duel solution may be not a pure strategy solution. The case of quadratic accuracy for β = 2 is further considered, which is reasoned by that linearly developing real-time processes are quite rare, and the quadratic accuracy is a non-linearity pattern that makes an interaction model more reliable by slightly diminishing the duelist's confidence. Thus, the case of a < 2 β − 1 is thoroughly studied for pure strategy solutions. A boundary case of a is found, by which the duel has four pure strategy solutions which are of the time moment preceding the duel end moment and the duel end moment itself. • Discrete game of timing is solved as a finite zero-sum game on a symmetric lattice. • Duelist is featured with shooting accuracy proportionality and nonlinearity factors. • The breaking point of the accuracy is found above which pure strategy solutions exist. • Pure strategy solutions are found below the breaking point for the quadratic accuracy. [ABSTRACT FROM AUTHOR]

This paper aims to present alternative characterizations for different types of set-valued robustness concepts. Equivalent scalar representations for various set order relations are derived when the sets are the union of sets. Utilizing these findings in conjunction with image space analysis, specific isolated sets are defined for different notions of robust solutions. These isolated sets serve as the basis for deriving both necessary and sufficient robust optimality conditions. The validity of the results is demonstrated through several illustrative examples. Additionally, the paper concludes with an application of our present approach to two-player zero-sum matrix games. [ABSTRACT FROM AUTHOR]

This paper studies two-player stochastic differential games with diffusion control on finite time horizons. The players' state processes are described in terms of controlled martingale problems, and we allow for correlation between the players' states, described by some correlation coefficient. We consider zero-sum ranking games in the sense that the criterion to optimize only depends on the difference of the two players' state processes at the finite time horizon. We explicitly compute saddle points of the game depending on the correlation coefficient. In particular, for correlation coefficients smaller than some explicit threshold, the game has a value and a saddle point (in strict controls). For correlation coefficients exceeding the threshold, we introduce the larger class of relaxed controls that can be interpreted as mixed strategies. We show that the game has a value and a saddle point in relaxed controls for all correlation coefficients. [ABSTRACT FROM AUTHOR]

In this paper, we consider the adaptive stabilization problem for a basic class of linearquadratic noncooperative stochastic differential games when the systems matrices are unknown to the regulator and the players. This is a typical problem of game-based control systems (GBCS) introduced and studied recently, which have a hierarchical decision-making structure: there is a controller at the upper level acting as a global regulator which makes its decision first, and the players at the lower level are assumed to play a typical zero-sum differential games. The main purpose of the paper is to study how the adaptive regulator can be designed to make the GBCS globally stable and at the same time to ensure a Nash equilibrium reached by the players, where the adaptive strategies of the players are assumed to be constructed based on the standard least squares estimators. The design of the global regulator is an integration of the weighted least squares parameter estimator, random regularization and diminishing excitation methods. Under the assumption that the system matrix pair (A, B) is controllable and there exists a stabilizing solution for the corresponding algebraic Riccati equation, it is shown that the closed-loop adaptive GBCS will be globally stable, and at the same time reach a Nash equilibrium by the players. [ABSTRACT FROM AUTHOR]

This study proposes an organizational mechanism that links ideology to the use of terrorism in mass dissident campaigns. Ideology affects the level of competition among factions within mass dissident campaigns by shaping whether actors see their interactions as a positive- or zero-sum game. We identify ideological diversity within a campaign and the degree to which ideologies embrace the principle of pluralism as key factors affecting the intensity of factional competition and, consequently, the occurrence of terrorism. We introduce new data on the ideologies of campaigns from the Nonviolent and Violent Campaigns and Outcomes 2.0 dataset and use causal mediation analysis to test our proposed mechanism. We find that greater ideological diversity within a campaign increases the likelihood of terrorism by increasing factional competition. We also find that the presence of a pluralist ideology is associated with a lower likelihood of terrorism by the lowering of factional competition. By shedding light on the mechanisms that link ideology to terrorism, this study helps advance our understanding of why dissident groups might decide to use terrorism tactics within the context of a campaign of mass resistance. [ABSTRACT FROM AUTHOR]

In this research, we investigate the horse racing game, a two-player game with six strategies. Our objective is to develop a theoretical framework for the game by employing the process of quantization. The Nash equilibrium (NE) of the quantum horse racing game is analyzed through an examination of the payoff matrix, and the results are compared to those of the classical horse racing game. Our findings demonstrate that the quantum horse racing game features both pure and mixed strategy NE, whereas the classical horse racing game possesses only mixed strategy NE. Additionally, our results indicate that the quantum horse racing game can mitigate the risks associated with the zero-sum game while outperforming the classical game. [ABSTRACT FROM AUTHOR]

The potential emergence of artificial general intelligence (AGI) systems has sparked intense debate among researchers, policymakers, and the public due to their potential to surpass human intelligence in all domains. This note argues that for an AI to be considered "general", it should achieve superhuman performance not only in zero-sum games but also in general-sum games, where winning or losing is not clearly defined. In this note, I propose a game-theoretic framework that captures the strategic interactions between a representative human agent and a potential superhuman machine agent. Four assumptions underpin this framework: Superhuman Machine, Machine Strategy, Rationality, and Strategic Unpredictability. The main result is an impossibility theorem, establishing that these assumptions are inconsistent when taken together, but relaxing any one of them results in a consistent set of assumptions. This note contributes to a better understanding of the theoretical context that can shape the development of superhuman AI. • A game between a human vs an Artificial General Intelligence system is introduced. • An AI system is general if it is successful in both zero-sum and general-sum games. • Assumptions are Superhuman AI, Machine Strategy, Rationality, and Unpredictability. • An impossibility theorem shows that these assumptions lead to inconsistency. • The result is tight; relaxing any of the assumptions resolves the inconsistency. [ABSTRACT FROM AUTHOR]

Political polarization is a systemic-level and multifaceted process that severs cross-cutting ties and shifts perceptions of politics to a zero-sum game. When it turns pernicious, political actors and supporters view opponents as an existential threat and the capacity of democratic institutions to process political conflict breaks down. The article identifies four common fault lines of polarization globally – who belongs, democracy, inequality and social contract. It argues that while Latin American countries experience, to varying degrees, all four of the fault lines, it is the deep-seated, persistent social hierarchies oriented around class, race, and place that stand out relative to other countries. Reaching consensus on reforms that may renew or reformulate agreements on the terms of the social contract, boundaries of community membership, and redressing social inequality is a tall task. Yet the region's sustained consensus on the democratic rules of the game can provide the mechanisms for addressing this task if new majority coalitions can be formed. [ABSTRACT FROM AUTHOR]

In this paper, we give an example of a statement concerning two-player zero-sum games which is undecidable, meaning that it can neither be proven or disproven by the standard axioms of mathematics. Earlier work has shown that there exist "paradoxical" two-player zero-sum games with unbounded payoffs, in which a standard calculation of the two players' expected utilities of a mixed strategy profile yield a positive sum. We show that whether or not a modified version of this paradoxical situation, with bounded payoffs and a weaker measurability requirement, exists is an unanswerable question. Our proof relies on a mixture of techniques from set theory and ergodic theory. [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]

The minimax theorem for zero-sum games is easily proved from the strong duality theorem of linear programming. For the converse direction, the standard proof by Dantzig is known to be incomplete. We explain and combine classical theorems about solving linear equations with nonnegative variables to give a correct alternative proof more directly than Adler. We also extend Dantzig's game so that any max-min strategy gives either an optimal LP solution or shows that none exists. [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]

Porous media reconstruction is quite significant in the fields of environmental supervision, oil and natural gas engineering, biomedicine and material engineering. The traditional numerical reconstruction methods such as multi-point statistics (MPS) are based on the statistical characteristics of training images (TIs), but the reconstruction quality may be unsatisfactory and the process is time-consuming. Recently, with the rapid development of deep learning, its powerful ability in predicting features has been used to reconstruct porous media. Generative adversarial network (GAN) is one of the generative methods of deep learning, which is derived from the two-person zero-sum game through the confrontation between the generator and discriminator. However, the traditional GAN cannot pay special attention to the effective features in learning, and the degradation problem easily occurs with the increase of layer numbers. Besides, high-resolution (HR) and large FOV (field of view) are usually contradictory for physical imaging equipment. Therefore, in practical experiments, due to the limitations of the resolution of imaging equipment and the sample size, it is difficult to obtain large-scale HR images of porous media physically. At this point, numerical super-resolution (SR) reconstruction seems to be a cost-efficient way. In this paper, residual networks and attention mechanisms are combined with single-image GAN (SinGAN), which can learn the structural characteristics of porous media from a low-resolution (LR) 3D image, and then reconstruct 3D HR or large-scale images of porous media. Comparison to some other numerical methods has proved that our method can reconstruct high-quality HR images with practicability and effectiveness. [ABSTRACT FROM AUTHOR]

The increased use of social media has triggered a visual shift in digital political communication. One central strategy is visual (self-) personalization and many studies have analyzed why and to what extent political communication is focused on individuals. Yet, most research has concentrated on personalization, leaving aside the question of whether it comes at the expense of policy issues or whether people are brought into focus as facilitators of such issues. We fill this research gap by analyzing how visual (self-) personalization on Instagram pages of parties and politicians during election campaigns has changed over time and whether it comes at the cost of addressing policy issues. We use data from three quantitative content analyses of Instagram posts published by parliamentary parties (n = 7) and top candidates (n = 18) in the 'hot phases' before three national German elections (N = 2313), covering all nationwide elections since Instagram has become a common campaign tool. It is shown that personal depictions are very common in Instagram campaigns of parties and candidates while addressing policy issues in posts varies between elections. Using a multilevel binomial regression model, we show that addressing policy issues is negatively related to personalization but further analysis shows that it is not a zero-sum game at the cost of addressing policy issues. Our results provide insights into how the presentation of politics might change when image- and video-based platforms continue to gain importance in political communication. [ABSTRACT FROM AUTHOR]

Fuzzy set plays an important role in handling vagueness for controlling uncertain dynamical systems. However, conventional type-1 fuzzy set (T1FS) requires precisely defined membership function, which is usually unavailable in practical control applications. This study pioneers the use of IT2FS for the control design of uncertain dynamical systems to relax this limitation of conventional T1FS-based control design. Concretely, the considered (possibly fast) time-variant uncertainty is bounded with the bound lying within IT2FS (hence interval type-2 fuzzy dynamical system, IT2FDS). A robust control design approach is proposed without invoking any IF-THEN fuzzy rules, providing a two-layer performance. The lower layer renders uniform boundedness and uniform ultimate boundedness for the system by the Lyapunov analysis, ensuring the bottom line. The upper layer improves the fuzzy-based control performance by optimal gain design oriented by a two-player zero-sum game, taking advantage of the interval description of membership function. It is shown that the equilibrium of the game which contains the optimal gain always exists. A semi-analytical solution strategy for the equilibrium is given. This is the first endeavor in exploring IT2FS-based control design without using any IF-THEN rules. It is shown in the demonstrative examples that the proposed IT2FS-based approach can achieve better control performance than the conventional T1FS-based approach. [ABSTRACT FROM AUTHOR]

Uncertain noncausal systems (UNCSs) are uncertain singular systems that are supposed to be regular along.This study investigates two-player zero-sum games (TPZSGs) for UNCSs using the optimistic value criterion. The first step is to introduce a method to convert a linear uncertain noncausal system (UNCS) considering linear control terms into subsystems including two kinds of uncertain difference equations. Recurrence equations are derived for tackling a TPZSG subject to linear UNCSs. Using recurrence equations, we give an algorithm for solving the TPZSG subject to linear UNCSs and show how to apply the algorithm to find the saddle-point solution and the equilibrium value of this kind of game by a numerical example. To expand on these results, we provide the corresponding equations for solving TPZSGs subject to nonlinear UNCSs. Moreover, we give the saddle-point solution and equilibrium value of a TPZSG for a nonlinear UNCS considering quadratic control terms by solving the equations. [ABSTRACT FROM AUTHOR]