Search Results (27)

In this work, we study n-agent Bayesian games with m-dimensional vector types and linear payoffs, also called linear multidimensional Bayesian games. This class of games is equivalent with n-agent, m-game uniform multigames. We distinguish between games that have a discrete type space and those with a continuous type space. More specifically, we are interested in the existence of pure Bayesian Nash equilibriums for such games and efficient algorithms to find them. For continuous priors, we suggest a methodology to perform Nash equilibrium searches in simple cases. For discrete priors, we present algorithms that can handle two-action and two-player games efficiently. We introduce the core concept of threshold strategy and, under some mild conditions, we show that these games have at least one pure Bayesian Nash equilibrium. We illustrate our results with several examples like the double-game prisoner’s dilemma (DGPD), the game of chicken, and the sustainable adoption decision problem (SADP). Full article

Objectives: We introduce the conceptual framework for the Adversarial Claim Robustness Diagnostics (ACRD) protocol, a novel tool for assessing how factual claims withstand ideological distortion. Methods: Based on semantics, adversarial collaboration, and the devil’s advocate approach, we develop a three-phase evaluation process combining baseline evaluations, adversarial speaker reframing, and dynamic AI calibration along with quantified robustness scoring. We introduce the Claim Robustness Index that constitutes our final validity scoring measure. Results: We model the evaluation of claims by ideologically opposed groups as a strategic game with a Bayesian-Nash equilibrium to infer the normative behavior of evaluators after the reframing phase. The ACRD addresses shortcomings in traditional fact-checking approaches and employs large language models to simulate counterfactual attributions while mitigating potential biases. Conclusions: The framework’s ability to identify boundary conditions of persuasive validity across polarized groups can be tested across important societal and political debates ranging from climate change issues to trade policy discourses. Full article

Common data environments (CDEs) are centralized repositories in the architecture, engineering, and construction (AEC) industry designed to improve collaboration and project efficiency. However, CDEs hosted on cloud platforms face significant risks from insider threats, as stakeholders with legitimate access may act maliciously. To address these vulnerabilities, we developed a game-theoretic framework using Bayesian games that account for incomplete information, modeling both simultaneous and sequential interactions between insiders and data defenders. In the simultaneous move game, insiders and defenders act without prior knowledge of each other’s decisions, while the sequential game allows the defender to respond after observing insider actions. Our analysis used Bayesian Nash Equilibrium to predict malicious insider behavior and identify optimal defense strategies for safeguarding CDE data. Through simulation experiments and validation with real project data, we illustrate how various parameters affect insider–defender dynamics. Our results provide insights into effective cybersecurity strategies tailored to the AEC sector, bridging theoretical models with practical applications and supporting data security within the increasingly digitalized construction industry. Full article

In recent years, the development of online food service has facilitated consumers’ catering needs, but it also has adverse environmental impacts. To mitigate pollution and attract environmentally conscious customers, restaurants have embarked on making green efforts, such as donating to environmental charities, using organic ingredients, and adopting green packaging. This study investigates the pricing strategies of restaurants that currently engage in green efforts. We develop demand models for online food services, considering customer green awareness and competition among restaurants. Both competitive pricing problems with symmetric and asymmetric information are discussed. The Nash equilibrium and Bayesian Nash equilibrium are derived in these scenarios. Optimal pricing strategies are presented, and the impacts of parameters on the optimal strategies are discussed. Our findings reveal that when the baseline utility derived from a green restaurant is lower than that from a regular restaurant, and customers are not highly concerned about environmental protection, the online food service from the green restaurant may have no market share. If the baseline utility derived from green restaurant services is higher than that from regular restaurant services, the optimal price tends to increase as the green utility increases. However, if the baseline utility derived from the green service falls within a certain range, the optimal price may first increase and then decrease as the green utility rises. Full article

This paper addresses two challenges in AV motion planning: adherence to right-of-way and handling uncertainties, using two game-theoretic frameworks, namely Stackelberg and Nash Bayesian (Bayesian). By modeling the interactions between road users as a hierarchical relationship, the proposed approach enables the AV to strategically optimize its trajectory while considering the actions and priorities of other road users. Additionally, the Bayesian equilibrium aspect of the framework incorporates probabilistic beliefs and updates them based on sensor measurements, allowing the AV to make informed decisions in the presence of uncertainty in the sensory system. Experimental assessments demonstrate the efficacy of the approach, emphasizing its ability to improve the reliability and adaptability of AV motion planning. Full article

With the rapid development of intelligent transportation systems and information technology, the security of road traffic signal systems has increasingly attracted the attention of managers and researchers. This paper proposes a new method for detecting attacks on traffic signal systems based on game theory and Generative Adversarial Networks (GAN). First, a game theory model was used to analyze the strategic game between the attacker and the defender, revealing the diversity and complexity of potential attacks. A Bayesian game model was employed to calculate and analyze the attacker’s choice of position. Then, leveraging the advantages of GAN, an adversarial training framework was designed. This framework can effectively generate attack samples and enhance the robustness of the detection model. Using empirical research, we simulated the mapping of real traffic data, road network data, and network attack data into a simulation environment to validate the effectiveness of this method. In a comparative experiment, we contrasted the method proposed in this paper with the traditional Support Vector Machine (SVM) algorithm, demonstrating that the model presented here can achieve efficient detection and recognition across various attack scenarios, with significantly better recall and F1 scores compared to traditional methods. Finally, this paper also discusses the application prospects of this method and its potential value in future intelligent transportation systems. Full article

In order to analyze the choice of the optimal strategy of cyber security attack and defense in the unmanned aerial vehicles’ (UAVs) cyber range, a game model-based UAV cyber range risk assessment method is constructed. Through the attack and defense tree model, the risk assessment method is calculated. The model of attack and defense game with incomplete information is established and the Bayesian–Nash equilibrium of mixed strategy is calculated. The model and method focus on the mutual influence of the actions of both sides and the dynamic change in the confrontation process. According to the calculation methods of different benefits of different strategies selected in the offensive and defensive game, the risk assessment and calculation of the UAV cyber range are carried out based on the probability distribution of the defender’s benefits and the attacker’s optimal strategy selection. An example is given to prove the feasibility and effectiveness of this method. Full article

Credit purchase enables the manufacturers in the e-commerce environment to provide pre-sales service that consumers can experience first and pay later. This paper considers demand associated with price and green promotion service level and builds four decentralized game models to study two green promotion service allocation strategies and demand forecasting information sharing strategies in a dual-channel environment. The effects of the degree of dual-channel competition and free-riding on the perfect Bayesian Nash equilibrium are studied. The results show that the retailer should actively cooperate with the manufacturer and share private forecasting information if the coefficient of channel substitution is relatively high. Sharing information will aggravate double marginalization and hurt the retailer. In addition, the retailer’s profit is positively influenced by the forecasting accuracy in four models. When the manufacturer invests in the green promotion service, the prediction accuracy hurts the manufacturer’s profit without information sharing and there is a positive impact with information sharing. In particular, when a retailer provides service, we take the consumer’s free-riding behavior into account, and we find that free-riding hurts both parties and the whole supply chain. In addition, the manufacturer’s profit is irrelevant to the prediction accuracy without information sharing and positively influenced by the accuracy with information sharing. Full article

We start from the fundamental premise that any physical interaction can be interpreted as a game. To demonstrate this, we draw upon the free energy principle and the theory of quantum reference frames. In this way, we place the game-theoretic Nash Equilibrium in a new light in so far as the incompleteness and undecidability of the concept, as well as the nature of strategies in general, can be seen as the consequences of certain no-go theorems. We show that games of the generic imitation type follow a circularity of idealization that includes the good regulator theorem, generalized synchrony, and undecidability of the Turing test. We discuss Bayesian games in the light of Bell non-locality and establish the basics of quantum games, which we relate to local operations and classical communication protocols. In this light, we also review the rationality of gaming strategies from the players’ point of view. Full article

Machiavellianism refers to the propensity of taking advantage of people within a society. Machiavellians have reputations for being cunning and competitive. They are also skilled long-term strategists and planners. Other than their “victories,” there are no other successful conclusions for them. The belief component of Machiavellianism includes cynical views of human nature (e.g., manipulated and manipulating individuals), interpersonal exploitation as a technique (e.g., strategic thinking), and a lack of traditional morality that would forbid their behaviors (e.g., immoral behaviors). This paper focuses on a game that involves manipulation. The game was conceptualized using the best and worst Nash equilibrium points as part of our contribution. We constrained the problem to homogeneous, finite, ergodic, and controllable Bayesian–Markov games. Machiavellian players pretended to be in one state when they were actually in another. Moreover, they pretended to perform one action while actually playing another. All Machiavellian individuals engaged in some form of interpersonal manipulation. Manipulating players exhibited a higher preference compared to manipulated participants. The Pareto frontier is defined as the line where manipulating players play the best Nash equilibrium and manipulated players play the worst Nash equilibrium. It is also considered a sequential Bayesian–Markov manipulation game involving multiple manipulating players and manipulated players. Finally, a tractable characterization of the manipulation equilibrium results is provided. To guarantee that the game’s solution converged into a singular solution, we used Tikhonov’s penalty regularization method. A numerical example describes the results of our model. Full article

Reinforcement learning has shown a great ability and has defeated human beings in the field of real-time strategy games. In recent years, reinforcement learning has been used in cyberspace to carry out automated and intelligent attacks. Traditional defense methods are not enough to deal with this problem, so it is necessary to design defense agents to counter intelligent attacks. The interaction between the attack agent and the defense agent can be modeled as a multi-agent Markov game. In this paper, an adversarial decision-making approach that combines the Bayesian Strong Stackelberg and the WoLF algorithms was proposed to obtain the equilibrium point of multi-agent Markov games. With this method, the defense agent can obtain the adversarial decision-making strategy as well as continuously adjust the strategy in cyberspace. As verified in experiments, the defense agent should attach importance to short-term rewards in the process of a real-time game between the attack agent and the defense agent. The proposed approach can obtain the largest rewards for defense agent compared with the classic Nash-Q and URS-Q algorithms. In addition, the proposed approach adjusts the action selection probability dynamically, so that the decision entropy of optimal action gradually decreases. Full article

With the continuous expansion of air traffic flow, the increasingly serious aviation network security threats have a significant and far-reaching impact on the safe operation of the aviation industry. The networked air traffic management (ATM) system is an integrated space–air–ground network integrating communication, network, satellite, and data chain technology, which adopts a network-centric structure to provide network-enabled services and applications for the air transportation system. In view of the serious network threats faced by networked ATM, this paper studies the basic theories and key technologies of ATM information security assurance and designs a credible security architecture to provide comprehensive and systematic security assurance for networked ATM. Starting from the problems and development trend of ATM security assurance, this paper investigates the dynamic and complex data processing process of ATM system, analyzes the complex interaction between its cyber and physical system, and then constructs the networked ATM cyber–physical fusion system model and threat model. Furthermore, the causal relationship between ATM security threat alert information is mapped into a Bayesian network, and the game model of networked ATM is proposed using Bayesian Nash equilibrium strategy. At last, the information security assurance architecture of networked ATM system based on blockchain technology is formed by establishing a distributed co-trust mechanism and multi-chain storage structure. We expect this paper to bring some inspiration to the related research in academia and aviation so as to provide useful reference for the construction of ATM safety and security system and the development of technology. Full article

Along with the prevalence of photovoltaic (PV) procurement contracts, the corruption between auctioneers and potential electricity suppliers has attracted the attention of energy regulators. This study considers a corruption-proof environment wherein corruption is strictly suppressed. It elaborates a mechanism to explore the impact of corruption-proof measures on PV procurement auctions. It adopts incentive compatible constraints based on revelation principle to reflect PV firms’ optimal utilities. It employs first-price and first-score auctions and uses the Bayesian Nash equilibrium to provide a description of market outcomes. The results show that several strategies have different impacts on social welfare, PV firms’ utility, and the benefits of corruption. First, a first-price auction cannot act as a suitable policy because it may encourage corruption. Second, the first-score choice is desirable for social welfare to fit the forthcoming high-quality and low-price surroundings. Third, the first-score strategy maximizes PV firms’ utility and total income. The implications suggest that regulators ought not to employ first-price auctions in the future PV market from the perspective of social welfare. Another disadvantage of the first-price approach is that it enables the PV firm to maintain the utmost benefit from corruption. Full article

For the air traffic management cyber physical system, if an attacker successfully obtains authority or data through a cyber attack, combined with physical attacks, it will cause serious consequences. Game theory can be applied to the strategic interaction between two parties, especially if the two parties have different goals. The offensive and defensive game process of the air traffic management cyber physical system is a non-cooperative and incomplete information dynamic game. The attacker can choose to camouflage the type of attack launched. The attack detection device configured in the system has a certain probability that the attack type can be successfully detected. According to the type of attack detected, the defender updates the posterior belief of the attack type and selects the corresponding protective strategies. According to the game process of offense and defense, a dynamic Bayesian game model of the air traffic management cyber physical system is established, the possible perfect Bayesian Nash equilibrium and its existence conditions are solved, and a complete mathematical model is constructed. The analysis shows that the dynamic Bayesian game model of the air traffic management cyber physical system can help the system defender to quickly obtain an equilibrium strategy and reduce the loss of the system as much as possible. Full article

We propose a model of network formation as a two-stage game with chance moves and players of various types. First, the leader suggests a connected communication network for the players to join. Second, nature selects a type vector for players based on the given probability distribution, and each player decides whether or not to join the network keeping in mind only his own type and the leader’s type. The game is of incomplete information since each player has only a belief over the payoff functions of others. As a result, the network is formed, and each player gets a payoff related to both the network structure and his type. We prove the existence of the Bayesian equilibrium and propose a new definition of the stable partially Bayesian equilibrium defining the network to be formed and prove its existence. The connection between the stable partially Bayesian equilibrium and the Nash equilibrium in the game is examined. Finally, we investigate the characteristics of the network structures under the stable partially Bayesian equilibrium in a three-player game with the major player as well as in the n-player game with a specific characteristic function. Full article