Bayesian Game Research Articles

Cooperative Merging Strategy Considering Stochastic Driving Style at on-Ramps: A Bayesian Game Approach

Cooperative Merging Strategy Considering Stochastic Driving Style at on-Ramps: A Bayesian Game Approach

Enhancing Transshipment Decision-making in Transportation Companies through Computational Intelligence: A Bayesian Game Model with Data-Driven Simulations at Guangzhou Port

The challenge of port congestion significantly impedes the efficiency of global trade flows and the economic vitality of hinterland regions. Addressing this issue, our study harnesses computational intelligence to construct a sophisticated Bayesian game model between the government and multiple transportation companies, where the transshipment costs, considered as private information to the companies, play a pivotal role. This research integrates data analytics and machine learning techniques to analyze the strategic decision-making processes of transportation companies, which decide on transshipment based on a critical cost threshold influenced by government subsidies and a probabilistic assessment of transshipment costs. Utilizing backward induction, the study outlines how the government can leverage computational models to devise an optimal subsidy strategy for transshipment, taking into account the anticipated responses of the transportation companies. The Bayesian Nash equilibrium identified through our model suggests that companies with costs below a predefined threshold are incentivized by government subsidies to opt for transshipment. This conclusion is further validated through evolutionary game theory analysis, enriched by data-driven simulations. Employing real-world data from Guangzhou Port, we conducted extensive computational simulations to quantify the impact of transshipment subsidies. The findings reveal a substantial alleviation in port congestion, with a 33.7% reduction in congestion levels and a 35% decrease in congestion-related costs, alongside a notable 1.9% increase in government revenue. These simulations, powered by advanced computational algorithms and data analytics, not only underscore the effectiveness of informed subsidy strategies in mitigating port congestion but also demonstrate the potential of integrating computational approaches in logistical and transportation decision-making. This study contributes a novel computational framework to the logistics and transportation literature, offering practical insights for policymakers to tackle the enduring problem of port congestion through data-driven strategies.

Interdependent network restoration games with incomplete information and bounded rationality.

Communities face the challenge of finding restoration strategies in the aftermath of disasters. In particular, independent and self-interested utility managers devise such strategies for infrastructure through a heuristic decentralized process. This paper takes a game-theoretic approach to model the decentralized and strategic restoration decision making with application to interdependent infrastructure. Particularly, we model the decision process using simultaneous games to investigate decision makers' conflicting preferences. We employ Bayesian games to incorporate the realistic assumptions of poor interagent communication, resulting in incomplete information. Also, we account for behavioral biases such as bounded rationality, cooperative behavior or lack thereof, and equality-driven resource allocations. We test our models using ideal, synthetic interdependent networks, and the realistic infrastructure of Shelby County, TN. Results show that cooperation leads to the best-performing decisions even if others are not cooperative. The necessity of cooperation is even higher when there is a dominant player whose service is vital to other players. Our sensitivity results highlight the significant influence of resource availability and allocation on the performance of restoration plans. Our approach enhances the practicality of decision models for community resilience, and unravels novel policy strategies such as cooperationincentives.

A Game-Theoretical Self-Adaptation Framework for Securing Software-Intensive Systems

Security attacks present unique challenges to the design of self-adaptation mechanism for software-intensive systems due to the adversarial nature of the environment. Game-theoretical approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive policies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To address such issues, we propose a new self-adaptation framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity of components. Under security attacks, the architecture model of the system is automatically translated, by the proposed translation process with designed algorithms, into a multi-player Bayesian game. This representation allows each component to be modelled as an independent player, while security attacks are encoded as variant types for the components. By solving for pure equilibrium (i.e., adaptation response), the system’s optimal defensive strategy is dynamically computed, enhancing system resilience against security attacks by maximizing system utility. We validate the effectiveness of our framework through two sets of experiments using generic benchmark tasks tailored for the security domain. Additionally, we exemplify the practical application of our approach through a real-world implementation in the Secure Water Treatment System to demonstrates the applicability and potency in mitigating security risks.

Analysis of Bayesian game theoretic approach for the target channel selection of secondary user under heterogeneous cognitive radio network

Analysis of Bayesian game theoretic approach for the target channel selection of secondary user under heterogeneous cognitive radio network

A behavioral approach to repeated Bayesian security games

A behavioral approach to repeated Bayesian security games

A Bayesian Game Based Bidding Scheme for Mobile Charging Services in IoEV

A Bayesian Game Based Bidding Scheme for Mobile Charging Services in IoEV

Quantum advantage beyond entanglement in Bayesian game theory

Quantum discord has been utilised in order to find quantum advantage in an extension of the Clauser, Horne, Shimony, and Holt game. By writing the game explicitly as a Bayesian game, the resulting game is modified such the payoff’s are different. Crucially, restrictions are imposed on the measurements that Alice and Bob can perform. By imposing these restrictions, it is found that there exists quantum advantage beyond entanglement for a given quantum state. This is shown by decomposing the expected payoff into a classical and quantum term. By optimising over the expected payoff, the classical limit is surpassed for the given state in the restricted measurement space. This gives an operational framework in order to witness and determine the quantum discord for specific states, whilst demonstrating the importance of measurement in quantum advantage.

Bayesian and stochastic game joint approach for Cross-Layer optimal defensive Decision-Making in industrial Cyber-Physical systems

The propagation of cyber-attacks targeting modern industrial cyber-physical systems (ICPSs) is considered a sophisticated and persistent cross-layer penetration process, posing significant cyber-to-physical (C2P) risks to critical industrial infrastructures. However, existing defensive solutions for ICPSs lack the global defensive decision-making capacity against such persistent and stealthy cross-layer threats. This paper proposes a Bayesian-Stochastic hybrid game-theoretic approach that can generate optimal defensive strategies throughout the cross-layer penetration lifecycle, even without full information about attackers. Specifically, we first present a unified quantification framework to guarantee consistent utility function configurations in a cyber-physical layer integrated system. Then, we propose a state transition-based stochastic game model to characterize the dynamic evolution process of cross-layer penetration. The attack-defense interaction in each state is modeled as an incomplete-information Bayesian game to capture the inherent uncertainties of attacker information. Furthermore, a multi-agent Bayesian Q-learning (MABQL) algorithm is developed to learn the optimal defensive strategies despite incomplete information. The proposed approach is implemented and assessed based on a real-world ICPS testbed and the numerical results validate its feasibility and effectiveness for making optimal defensive decisions.

Dynamic Game Theoretic Electric Vehicle Decision Making

<div>Real-world driving in diverse traffic must cope with dynamic environments including a multitude of agents with uncertain behaviors. This poses a challenging motion planning and decision-making problem, as suitable algorithms should manage to obtain optimal solutions considering nearby vehicles. The state-of-the-art way of environment and action generalization is built on mathematical modeling and probabilistic programming of idealistic incidents. In this article we present dynamic anytime decision making, a decision-making algorithm that takes advantage of natural evolutionary and developmental processes to make decisions for an autonomous vehicle navigating in traffic. The methodology to achieve multidimensional judgment under unforeseen circumstances is to enable stochastic Bayesian game theory when modeling interactive properties and scenario estimation. In particular, we employ a dual-layer optimization strategy that substantially enhances the capability to extract mutual influence into decision matrix that organically synthesize decision rules according to encountered situations. Furthermore, our decision-making algorithm incorporates incremental capability that computes modified control variable due to unexpected vehicle behavior or intention. This results in improved performance gain where dynamic conditions receive reasoning with more effectiveness. We produce swift policy correction by leveraging previous game mechanism, where cooperative characteristics of both the controlled vehicle and other agents are integrated without computation overload. Aiming to reach utmost accurate decision, our reward function is reinforced with weight-tuning function to deduce optimal hyper parameters in a wider spectrum of vehicle scenarios. Through coupled decision tree optimization and incremental trajectory control, economical commands are computed with enhanced precision under complex traffic pattern. To this end, our system provides a robust framework in terms of handling dynamic vehicle interactive control, as decision is computed by synergy between progressive interface and external rationalization. We demonstrate the algorithm performing preeminent passing maneuvers and merging in a number of different simulated scenarios.</div>

Mitigating DDoS attacks with an intrusion detection and prevention system based on 2-player Bayesian game theory

Distributed Denial of Service (DDoS) attacks are very dangerous to the availability and security of networks, so they need improved ways to be stopped. This article suggests a new way to fight DDoS attacks that uses Intrusion Detection and Prevention Systems (IDPS) and 2-Player Bayesian Game Theory. Traditional IDPSs often have trouble responding quickly to changing attack tactics, which makes them less effective as defenses. The suggested structure, on the other hand, imagines the attacker and defense interacting as a Bayesian game. This lets them make proactive choices and come up with flexible ways to respond.The system uses Bayesian reasoning to describe the attacker’s actions and plans’ doubt, which lets it better assess the threat and decide how to respond. By constantly changing probability distributions based on what it sees attackers doing and what it sees defenders doing, the IDPS can quickly and effectively change its defenses to deal with new threats. The strategy contact between the attacker and the defense adds a competition factor that makes attackers less likely to start DDoS attacks by making them more expensive and risky. The proposed method works to stop different kinds of DDoS attacks while reducing the number of fake positives and negatives through a lot of simulations and experiments.

Jamming Attack Mitigation in Wireless Federated Learning Networks using Bayesian Games

Jamming Attack Mitigation in Wireless Federated Learning Networks using Bayesian Games

AutoFL: A Bayesian Game Approach for Autonomous Client Participation in Federated Edge Learning

Given that devices (i.e., clients) participating in federated edge learning (FEL) are autonomous and resource-constrained in nature, it is critical to design effective incentive mechanisms to encourage client participation so as to improve the performance of FEL. In this paper, we aim to boost the FEL training efficiency by answering how much compute resource should clients autonomously contribute to maximize their utilities. To this end, we develop AutoFL, an autonomous client participation decision framework for federated learning at the network edge without assuming that each client possesses complete information. We first model the problem of autonomous client participation as a Bayesian game with incomplete information, where each player in the game is associated with a set of types according to network conditions. We optimize an individual client's decision based on the dynamics of the population estimated following the Bayes rule. We prove that AutoFL can converge to a unique Bayesian Nash equilibrium point. Empirical results on three real datasets show that AutoFL achieves a higher model accuracy with only 15.5-24.5% model aggregation time per global training round, and its energy cost saving on mobile devices is 82.2-86.8% compared to the state-of-the-art algorithms. Moreover, we can achieve a 2.75-3.2x long-term fairness compared to classical solutions.

Remote State Estimation Under DoS Attacks in CPSs With Arbitrary Tree Topology: A Bayesian Stackelberg Game Approach

Remote State Estimation Under DoS Attacks in CPSs With Arbitrary Tree Topology: A Bayesian Stackelberg Game Approach

A Stochastic Bayesian Game for Securing Secondary Frequency Control of Microgrids Against Spoofing Attacks With Incomplete Information

A Stochastic Bayesian Game for Securing Secondary Frequency Control of Microgrids Against Spoofing Attacks With Incomplete Information

Computing Bayes–Nash Equilibrium Strategies in Auction Games via Simultaneous Online Dual Averaging

Determining equilibria in auction games is computationally hard in general, and no exact solution theory is known. We introduce an algorithmic framework in which we discretize type and action space and then learn distributional strategies via online optimization algorithms. We show that the equilibrium of the discretized auction game approximates an equilibrium in the continuous game. In a wide variety of auction games, we provide empirical evidence that the approach approximates the analytical (pure) Bayes–Nash equilibrium closely. In standard models in which agents are symmetric, we find equilibrium in seconds. The method allows for interdependent valuations and different types of utility functions, and it can be used to find equilibrium in auction markets and beyond.

A multi-energy pricing strategy for port integrated energy system based on Bayesian Stackelberg game

This paper proposes a method for multi-energy pricing strategy by constructing Bayesian Stackelberg model, in order to increase the social welfare and extend the optimal results to the case where private information about energy users (EUs) is unknown. Specifically, in Bayesian Stackelberg game model, the port acts as a leader and EUs are competitive followers. Furthermore, the decision processes of port and EUs are coupled together by the probability of consuming electricity and heat. In addition, a price regulation mechanism is designed, which is composed of fictitious play (FP) algorithm and gradient descent algorithm. Consequently, the balance of energy supply and demand can be obtained by loop iteration of these two algorithms. Numerical results have demonstrated that the multi-energy pricing strategy can achieve the balance between supply and demand and improve the social welfare.

Cournot meets Bayes-Nash: A discontinuity in behavior in finitely repeated duopoly games

We conduct a series of Cournot duopoly market experiments with a high number of repetitions and fixed matching. Our treatments include markets with (a) complete cost symmetry and complete information, (b) slight cost asymmetry and complete information, and (c) varying cost asymmetries and incomplete information. For the case of complete cost symmetry and complete information, our data confirm the well-known result that duopoly players achieve, on average, partial collusion. However, as soon as any level of cost asymmetry or incomplete information is introduced, observed average individual quantities are remarkably close to the static Bayes-Nash equilibrium predictions.

LP formulation of stochastic Bayesian two‐player zero‐sum games with long horizon

AbstractThis article studies two‐player zero‐sum stochastic Bayesian games where each player has its own dynamic state that is unknown to the other. To compute the optimal strategy for the players, prior work uses sequence form to construct linear programming formulation, whose size grows exponentially with respect to the time horizon of the game. The exponential computational complexity restricts us in games with short horizon. While window‐by‐window or receding horizon methods can be used to extend the horizon, how to update the initial parameters in each window is still unknown. Based on the existing results about dual games, this article proposes LP formulations to update the initial parameters in each window. A window‐by‐window method is given with performance analysis. The main results are demonstrated in a security problem of underwater sensor networks.

Multi-agent Cooperative Optimization Strategy of a Virtual Power Plant Based on Game Theory

This paper proposes a multi-agent collaborative optimization method for virtual power plants based on the Bayesian game. Focusing on the internal scheduling of VPP, taking demand response load as the analysis object, aiming at the interactive optimization relationship between multiple intelligent communities in the virtual power plant under this scenario, a multi-agent collaborative optimization model of the virtual power plant is established by using Bayesian game theory. The effectiveness of the constructed game model and algorithm is verified by solving the actual example of the virtual power plant and analyzing the corresponding simulation results.