Bayesian Game Research Articles

Observing actions in global games

We study Bayesian coordination games where agents receive noisy private information over the game’s payoffs, and over each others’ actions. If private information over actions is of low quality, equilibrium uniqueness obtains in a manner similar to a global games setting. On the contrary, if private information over actions (and thus over the game’s payoff coefficient) is precise, agents can coordinate on multiple equilibria. We argue that our results apply to phenomena such as bank-runs, currency crises, recessions, or riots and revolutions, where agents monitor each other closely.

Feature Selection Using Games with Imperfect Information (FSGIN)

Game Theory (GT) is the study of strategic decision making. By virtue of its importance, several GT based methodologies for Feature Selection (FS) are proposed in recent times. FS problem can be abstracted as a game by considering each feature as a player and their values as their strategies. Additionally, overall goal of the game is set to classify a data instance appropriately. Most of the existing GT based FS techniques are restricted to Zero Sum Games, Non-Zero Sum Games and Cooperative Games. The classical setting of assuming that all the details of players are known to all players cannot hold in many real-world problems. When the given features are independent, they cannot be treated alike and a characteristics based uncertainty persists among the features. This uncertainty is handled by none of the game forms used in the existing methods. Unlike the mentioned game techniques, Bayesian Games (BG) address the games with imperfect information. This paper investigate the FS problem in terms of BG and proposes a novel method to select the best features. The proposed BG based FS method is a filter type FS method and it starts with identifying Principle Features (PF) and proceeds to play global pairwise Bayesian games between those PF to obtain feature scores. Later, the features are ranked using these scores. In the final stage, a forward selection method with Support Vector Machine (SVM) is used to evaluate the classification performance of the ranked features and helps in the selection of the optimal set of features. Besides these, to improve the scalability of the proposed method MapReduce paradigm is exploited. In order to show the efficacy of the proposed method, experiments are carried out with seven real-world datasets from UCI and Statlog repositories. The promising results showed a significant improvement in the classification performance with fewer selected features than which is achieved using the existing methods.

Interactive influences of ecosystem services and socioeconomic factors on watershed eco-compensation standard “popularization” based on natural based solutions

Watershed eco-compensation is a policy tool to realize watershed environment improvement and regional economic development. It is important to eliminate the influence of economic differences between upstream & downstream regions and realize the fairness of regional social development based on Natural based Solutions (NbS). At present, lack of clarity in coupling and coordination analysis of ecosystem services & socioeconomic based on NbS could hamper watershed eco-compensation standards “popularization” and reduce the ability to successfully ecological governance. To meet the needs of economic development and ecological service value realization, dynamic equilibrium game research based on multidimensional relationship coordination and a multi-objective optimization solution of economic benefit distribution was carried out. To achieve the bargaining Bayesian/Nash equilibrium of the watershed eco-compensation standard in the game, the existence conditions of the equilibrium solution of the eco-compensation standard based on the mixed equilibrium game implementation process were studied. To carry out the complete information dynamic game, the equilibrium solution of the watershed eco-compensation standard based on the dynamic transfer payment was solved, and the rational analysis of the dynamic Bayesian equilibrium game of bargaining based on the incentive compatibility mechanism was also discussed. Water quantity and quality eco-compensation can ensure balanced development between ecological protection and the social economy in the Mihe River Basin. Combined with the variation law of socioeconomic water intake-utilization standards and the water use value, the city of Shouguang City & Qingzhou City should pay Linqu County 4.78 million US$ and 1.29 million US$ as watershed eco-compensation standards per year based on NbS, respectively. To verify the rationality of the results derived from the economically optimal model, two modes of “bargaining” & “perfect competition”, were used to study the characteristics of the protocols generated by the equilibrium game, and the applicable conditions of the nonzero-sum game solution upstream and downstream of the watershed were also explored. Based on the nonzero-sum processing of the survey results, the current relationship between the input value of eco-compensation and the willingness to pay satisfies v≥c+1/4. Based on the dynamic game & Bayesian equilibrium solution of bargaining, the watershed eco-compensation quota of water quantity & quality is 6.07 million US$, the willingness to pay is 65.63 US$/month. These findings contribute to the quantifying process of bargaining & dynamic equilibrium by transforming “ambiguous” information to achieve sustainable ecosystem service management and develop socioeconomic strategies associated with different compensation features based on NbS, thus helping to inform watershed management.

Performance Analysis of Network Selection Using ELECTRE Method

In this type of networks, overall network stability, utilization of resources, User satisfaction and most conducive to quality of service and selecting a Trusted access network (AN) is network in HWN (ANS) paper Proposes plan proposed project user, Can consider operator and/or quos perspectives general multicriteria software assistant (SA) was used to present and design. In imperfect wireless networks Bayesian information on the network selection problem we make it a game. Generally, a mobile user's preference (i.e., usage) is personal information. Therefore, each user provides others an optimal network selection decision must be made. Bayesian Nash equilibrium is assumed to be the solution of this game, also between the Bayesian Nash equilibrium and the equilibrium distribution of aggregate dynamics there is a one-to-one mapping. This is a Bayesian network selection game Integration of overall optimal response dynamics Numerical results. Show that Referring to the notification, the current paper is called Exams presents a review on the application of MCDM to determine the best PC architecture among three PC architectures, i.e.: A, B&C using ELECTRE technique based on four criteria. Provides a dynamic response to the problem. Henceforth this strategy is incorporated in this study. Arrangements obtained by AHP and ELECTRE technique are considered in the present study. Alternatives (eg wireless networks) represent attributes such as Network Selection 1, Network Selection 2, Network Selection 3, Network Selection 4, and Network Selection 5. Delay (msec), Jitter (msec), BER (• 10), Throughput (kbps), Cost (units) is an evaluation method. In this type of analysis, Gray Correlation Analysis (GRA) methods determine the best solution for a short distance to negativity and a long distance to solution. As a result, Wireless Network 5 ranked first and Wireless Network 4 ranked lowest.

Strategic Successive Refinement With Interdependent Decoders Cost Functions

In decentralized and decision-oriented communication paradigms, autonomous devices strategically implement information compression policies. In this work, we study a strategic communication game between an encoder and two decoders. An i.i.d. information source, observed by the encoder, is transmitted to the decoders via two perfect links, one reaching the first decoder only and the other reaching both decoders, as in the successive refinement setup. All three communicating devices are assumed to be rational, i.e. they want to minimize their respective cost functions, that depend on the source variable and the output symbols of both decoder. The game takes place as follows: the encoder commits to implementing an encoding strategy which induces a Bayesian game among the two decoders. The encoder is the Stackelberg leader and the two decoders are the Stackelberg followers, they select simultaneously the output sequences that minimize their respective long-run costs. We characterize the asymptotic behavior of the long-run optimal cost of the encoder, when the decoders implement decoding strategies that form a Bayes-Nash equilibrium. We show that this optimal cost converges to a single-letter expression which involves two auxiliary random variables and single-letter incentive constraints of the decoders.

Towards Intelligent Control of Beaconing Power and Beaconing Rate in Vehicular Ad Hoc Networks

In this paper, to avoid congestion in the wireless channel of vehicular ad hoc networks (VANETs), a joint beaconing rate and beaconing power based on game theory are proposed in this paper. The game is formulated as a non-cooperative game, a Bayesian game, and a cooperative game. Three distributed and iterative algorithms (Best Response Algorithm, Fictitious Play Algorithm, and Cooperative Bargaining Algorithm) are proposed for computing the beaconing power and beaconing rate of each vehicle. Extensive simulations show the convergence of a proposed algorithm to the equilibrium beaconing power and beaconing rate and give some insights on how the game parameters may vary the game outcome.

Agent Transformation of Bayesian Games

A new transformation that converts a Bayesian game to a so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex-ante</i> agent game (a normal-form game) is proposed. Differently from the existing transformation proposed by R. Selten that changes a Bayesian game to an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interim</i> agent game (also a pure normal-form game), we prove that the new transformation preserves potentiality. In addition, there is a nonpotential Bayesian game whose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex-ante</i> agent game is potential. We also prove that there is one-to-one correspondence between pure Bayesian Nash equilibria (BNE) of Bayesian games (if one exists) and pure Nash equilibria (NE) of the resulting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex-ante</i> agent games. Then, we provide a sufficient and necessary condition for a Bayesian game to have an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex-ante</i> agent potential game. By using these results, one can transform pure BNE seeking in Bayesian games to pure NE seeking in their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex-ante</i> agent games [by using the potential functions of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex-ante</i> agent games (if one exists)], where previously pure BNE seeking in Bayesian games by using potential functions can only be done in Bayesian potential games (BPGs). Particularly, we prove for two-player games that BPGs are exactly the Bayesian games having <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex-ante</i> agent potential games. Furthermore, by using the semi-tensor product of matrices, a potential equation for finite Bayesian games is developed. Based on the potential equation, algorithms for verifying potentiality and for searching pure BNE in finite Bayesian games are designed. Finally, the results are applied to a routing problem with incomplete information.

Exploiting Non-Cooperative Game Against Cache Pollution Attack in Vehicular Content Centric Network

Vehicular Content Centric Network (VCCN) has been proposed to address the issues of user mobility and sporadic connectivity in Vehicular Ad hoc Network (VANET). The in-network caching of VCCN can indeed improve the performance of content distribution in terms of cache hit and delivery latency by making each node store frequently accessed data. However, data caching unfortunately suffers from Cache Pollution Attack (CPA) that sends out fabricated requests to pollute the data cache. To prevent the degradation of network performance caused by such an attack, we propose a detection and defense scheme for CPA by adopting game theory. We model the attack scenario into a non-cooperative game model. First, we prove the non-existence of pure strategy Nash Equilibrium (NE) in the attacker and defender game, and propose a normal form game model with complete information and an extensive Bayesian form game model with incomplete information. Under the guidance of NE, we propose a punishment strategy to prevent the conspiracy attack and make the attackers behave normally. Moreover, we propose a cooperative detection scheme to give the Road Side Unit (RSU) the final decision on CPA based on the direct and indirect suspicious lists which are generated by each node according to the observed traffic pattern. Simulation evaluations demonstrate that our scheme outperforms state-of-the-art schemes in terms of cache hit, detecting ratio, and cache accuracy for detecting and defending CPA.

Cooperative maneuver decision making for multi-UAV air combat based on incomplete information dynamic game

Cooperative autonomous air combat of multiple unmanned aerial vehicles (UAVs) is one of the main combat modes in future air warfare, which becomes even more complicated with highly changeable situation and uncertain information of the opponents. As such, this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat. Firstly, a cooperative situation assessment model is presented to measure the overall combat situation. Secondly, an incomplete information dynamic game model is proposed to model the dynamic process of air combat, and a dynamic Bayesian network is designed to infer the tactical intention of the opponent. Then a reinforcement learning framework based on multi-agent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model. Finally, a series of simulations are conducted to verify the effectiveness of the proposed method, and the simulation results show effective synergies and cooperative tactics.

Bayesian game model based unsupervised sentiment analysis of product reviews

Sentiment Analysis is a task of computationally recognizing and contextualizing opinions stated in a text. We mainly assess whether the writer's attitude towards a specific topic, or a product, is positive, negative, or neutral. Numerous machine learning and fuzzy logic methods have been reconnoitered for sentiment analysis. Yet, the application of mathematical optimization techniques for sentiment tagging is still unexplored. This study presents a novel mathematical framework for sentiment analysis of reviews based on Game Theory. We identify whether the sentiment of a review is positive or negative. In the first step, we comprehend a review and derive context scores from review comments using the SentiWordNet lexicon. We comprehensively combine the computed context and rating scores using the Bayesian Game Model to deduce the sentiment of reviews. Experimental results on three benchmark review datasets, viz. Food, Mobile, and Electronics demonstrate that the proposed model yields state-of-the-art results. We also statistically validated the stability and correctness of the results. The proposed model ensures rational and consistent results. The utility of the game theory model for sentiment analysis creates a new paradigm for diverse NLP tasks.

The market for lemons and information theory

Spence’s theory of signaling shows that signals can resolve adverse selections in job markets. Instead of signals, I consider what Spence calls indices and show that if the costs of knowing the indices are not nil, they could play the role of resolving adverse selection in sequential Bayesian games. I also show that by incorporating costs that are measured by the mutual information of indices, adverse selection can be resolved if the costs fall within a certain range.

The Method of Trajectory Selection Based on Bayesian Game Model

To cope with the problem that most of the en-route spatial-temporal resource allocation in the collaborative trajectory options program (CTOP) only considers the air traffic control system command center (ATCSCC) while ignoring the needs of the airlines, which results in the loss of fairness, this study explores resource allocation methods oriented to airline trajectory preferences with optional trajectory and entry slots of flights over the flow constrained area (FCA) as the research object. Using game theory to analyze airline trajectory preference information and a Bayesian game model based on mixed strategies is constructed, the process of incomplete information game among airlines is studied. The equilibrium theory is used to solve the guarantee strategy of airline trajectory selection, which makes the airline trajectory selection strategy robust and provides a basis for the selection of schemes for ATCSCC to implement en-route network resource allocation under the CTOP. Experimental analysis was carried out to verify the feasibility of the method based on the actual operation data of high-altitude sectors of Shanghai. The results show that the solution obtained by the game can provide airlines with flight trajectory and entry slots over the FCA that are more in line with their actual operational needs and which provide data reference for the ATCSCC to select the final plan in multiple global Pareto optimal solutions in the subsequent process of the CTOP so as to better play the decision-making role of airlines in the CTOP while improving the fairness of en-route resource allocation.

GaDQN-IDS: A Novel Self-Adaptive IDS for VANETs Based on Bayesian Game Theory and Deep Reinforcement Learning

Due to the nature of high mobility and dynamic network topology, Intrusion Detection Systems (IDSs) in Vehicular Ad-hoc Networks (VANETs) face lots of challenges, especially in balancing the accuracy and efficiency of detection. Current researches about the deployment of IDSs in VANETs mainly focus on a tradeoff between the effectiveness and efficiency, but few efforts have been done about the adaptability of the tradeoff in the changeable networks. Thus, we address two crucial problems: 1) how to perceive the environmental change in the perspective of an IDS? 2) how to make the IDS adaptive in different scenarios? In this paper, a Bayesian Game theory and Deep Q-learning Network-based IDS is proposed for VANETs, called GaDQN-IDS. The interactions between an IDS and attackers are formulated as a dynamic intrusion detection game, in which the IDS decides either to just adjust the tradeoff between the accuracy and efficiency or to be retrained completely when its detection capacity has declined. The Nash Equilibria (NE) of the game is derived to reveal how the optimal decision of the IDS depends on the detection performance and road conditions. Moreover, a Deep Q-learning Network (DQN)-Adjustment is proposed to realize the self-adaptation of the IDS in the dynamic game, while an Error Priority Learning (EPL) is further designed for IDS retraining in changing VANETs. Simulation results show that the GaDQN-IDS has better performance than other existing IDSs with higher detection rate as well as lower detection time and overhead.

Research on Game Theory of Air Traffic Management Cyber Physical System Security

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.

A Bayesian nonatomic game and its applicability to finite-player situations

We investigate a nonatomic game (NG) involving a random state of the world. Every player receives only a signal of the state’s realization. Not knowing the true state, the player would strive for a higher average payoff conditioned on her received signal. We demonstrate that equilibria exist for the game under reasonable conditions. Not only are they in existence, but these equilibrium points are also useful. When the state space is finite, such an equilibrium would help induce asymptotically equilibrium behaviors as n tends to +∞ in n-player Bayesian games whose player profiles are randomly generated from the original NG’s player distribution. More important, pure ϵ-equilibria would likely emerge even though the NG equilibrium might well be mixed to start with. Pure versions of the latter would exist when the NG is anonymous.

Multiproduct pricing under the multinomial logit model with local network effects

AbstractMotivated by direct interactions with practitioners and real‐world data, we study a monopoly firm selling multiple substitute products to customers characterized by their different social network degrees. Under the multinomial logit model framework, we assume that the utility a customer with a larger network degree derives from the seller's products is subject to more impact from her neighbors and describe the customers' choice behavior by a Bayesian Nash game. We show that a unique equilibrium exists as long as these network effects are not too large. Furthermore, we study how the seller should optimally set the prices of the products in this setting. Under the homogeneous product‐related parameter assumption, we show that if the seller optimally price‐discriminates all customers based on their network degrees, the products' markups are the same for each customer type. Building on this, we characterize the sufficient and necessary condition for the concavity of the pricing problem, and show that when the problem is not concave, we can convert it to a single‐dimensional search and solve it efficiently. We provide several further insights about the structure of optimal prices, both theoretically and numerically. Furthermore, we show that we can simultaneously relax the multinomial logit model and homogeneous product‐related parameter assumptions and allow customer in‐ and out‐degrees to be arbitrarily distributed while maintaining most of our conclusions robust.

Efficient and stable data-sharing in a public transit oligopoly as a coopetitive game

In this study, various forms of data sharing are axiomatized. A new way of studying coopetition, especially data-sharing coopetition, is proposed. The problem of the Bayesian game with signal dependence on actions is observed; and a method to handle such dependence is proposed. We focus on fixed-route transit service markets. A discrete model is first presented to analyze the data-sharing coopetition of an oligopolistic transit market when an externality effect exists. Given a fixed data sharing structure, a Bayesian game is used to capture the competition under uncertainty while a coalition formation model is used to determine the stable data-sharing decisions. A new method of composite coalition is proposed to study efficient markets. An alternative continuous model is proposed to handle large networks using simulation. We apply these models to various types of networks. Test results show that perfect information may lead to perfect selfishness. Sharing more data does not necessarily improve transit service for all groups, at least if transit operators remain noncooperative. Service complementarity does not necessarily guarantee a grand data-sharing coalition. These results can provide insights on policy-making, like whether city authorities should enforce compulsory data-sharing along with cooperation between operators or setup a voluntary data-sharing platform.

Voluntary disclosure and adverse selection: Bayesian game theoretical inference for green bond labelling regimes

The impact finance market has sought to ‘internalise externalities and adjust risk perceptions’ (G20 Green Finance Study Group, 2016), demonstrating the private sector’s capability in resolving the climate free-rider problem through the ‘greening’ of economic activities, partially bypassing corrective government intervention. As the market continues to develop, however, the voluntary disclosure regime that the market operates under threatens to enforce an adverse selection problem and contribute to a fundamental erosion of confidence in the market segment, constraining the potential of impact finance instruments to effect positive social and environmental change. This paper relates the work of Crawford and Sobel (1982), Milgrom (1981), Verrecchia (1983), Jung and Kwon (1988), Myers and Majluf (1984) to the green bond market and draws inferences to inform recommendations for policy-led solutions aimed at ensuring the alignment of green bonds’ proceeds to the interests of society (desirable outcomes within the scope of the Paris agreement, for example) and upholding the market’s credibility. This paper additionally explores the transplantation of sustainability linker mechanisms into green bond architecture to ensure simultaneous issuer-level alignment.

Robust Design for RIS-Assisted Anti-Jamming Communications With Imperfect Angular Information: A Game-Theoretic Perspective

This paper utilizes a reconfigurable intelligent surface (RIS) to enhance the anti-jamming performance of wireless communications, due to its powerful capability of constructing smart and reconfigurable radio environment. In order to establish the practical interactions between the base station (BS) and the jammer, a Bayesian Stackelberg game is formulated, where the BS is the leader and the jammer acts as the follower. Specifically, with the help of a RIS-assisted transmitter, the BS attempts to reliably convey information to users with maximum utilities, whereas the smart jammer tries to interfere the signal reception of users with desired energy efficiency (EE) threshold. Since the BS and the jammer are not cooperative parties, the practical assumption that neither side can obtain the other&#x0027;s strategies is adopted in the proposed game, and the angular information based imperfect channel state information (CSI) is also considered. After tackling the practical assumption by using Cauchy-Schwarz inequality and the imperfect angular information by using the discretization method, the closed-form solution of both sides can be obtained via the duality optimization theory, which constitutes the unique Stackelberg equilibrium (SE). Numerical results demonstrate the superiority and validity of our proposed robust schemes over the existing approaches.

Incentive mechanism for federated learning based on blockchain and Bayesian game

Incentive mechanism for federated learning based on blockchain and Bayesian game