Bayesian Game Research Articles

Cyber-Physical Security Evaluation in Manufacturing Systems with a Bayesian Game Model

Cyber-physical security has been taken more seriously recently in manufacturing systems because of highly increasing integration of cyber and network systems with physical production known as cyber-physical manufacturing systems (CPMS). This paper presents a novel proactive approach to forecast the interrelation of attackers and cyber-physical manufacturing systems to suggest a proper defense strategy to the system managerial. In this research, the zero-sum game payoff function developed in our previous studies is transformed into an epistemic type Bayesian game that addresses uncertainties on types of players. The risk preference behavior also is considered as a characteristic of different types of players and to form the Bayesian utility function. Finally, a numerical example is analyzed with a structured method by developing the Bayesian game, computing Bayes-Nash equilibria, and finding quantal response equilibrium. This approach enables managers to decide a proper defense strategy in advance of experiencing cyber-physical dilemma when there are not enough previous data.

Optimal network defense strategy selection based on Bayesian game

Existing passive defence methods cannot effectively guarantee network security; to solve this problem, a novel method is proposed that selects the optimal defence strategy. The network attack-defen...

Reasoned bargaining protocol in construction contracts using a novel Bayesian game

The objective of this paper is to provide new insights on some dimensions of the bargaining process - asymmetries and uncertainties in particular by using a Novel Expert Bayesian Game (NEBG). We develop decision support system for determining the price in construction supply contract. Our results confirm that uncertainty affects negotiators' behaviour and modifies the likelihood of a self-enforcing agreement to emerge. The validation analysis revealed that a novel approach to Bayesian Network (BN) construction by combining domain knowledge from experts possessing incomplete observation data substantially improved the estimation ability of negotiators. Exhibiting a high success rate and profit as well as short-negotiation time, the proposed model is superior to those reported in previous research.

Optimal network defense strategy selection based on Bayesian game

Optimal network defense strategy selection based on Bayesian game

The Family of Alpha,[a,b] Stochastic Orders: Risk vs. Expected Value

In this paper we provide a novel family of stochastic orders that generalizes second order stochastic dominance, which we call the $\alpha,[a,b]$-concave stochastic orders. These stochastic orders are generated by a novel set of ``very concave functions where $\alpha$ parameterizes the degree of concavity. The $\alpha,[a,b]$-concave stochastic orders allow us to derive novel comparative statics results for important applications in economics that cannot be derived using previous stochastic orders. In particular, our comparative statics results are useful when an increase in a lottery's riskiness changes the agent's optimal action in the opposite direction to an increase in the lottery's expected value. For this kind of situation, we provide a tool to determine which of these two forces dominates -- riskiness or expected value. We apply our results in consumption-savings problems, self-protection problems, and in a Bayesian game.

Reasoned bargaining protocol in construction contracts using a novel Bayesian game

The objective of this paper is to provide new insights on some dimensions of the bargaining process - asymmetries and uncertainties in particular by using a Novel Expert Bayesian Game (NEBG). We de...

On the Existence of Equilibrium in Bayesian Games Without Complementarities

On the Existence of Equilibrium in Bayesian Games Without Complementarities

Persuading Strategic Voters

A Sender wants to persuade multiple Receivers with homogeneous preferences and a common belief about the state of the world to vote in favor of a proposal. Prior to the vote Sender commits to a communication strategy which sends private, potentially correlated, signals to Receivers that are contingent on the true state of the world. While Sender benefits from using private messages rather than public communication if Receivers vote sincerely, under the optimal communication strategy, sincere voting is not in any Receiver’s best interest. If the proposal does not require unanimous agreement, Sender’s optimal communication strategy after which sincere voting indeed constitutes a Bayes-Nash equilibrium is such that no voter is ever pivotal.

Dynamic Game Model for Ranking Bitcoin Transactions Under GSP Mechanism

Bitcoin is the first and most successful Blockchain system so far. In the Bitcoin system, miners use transaction attached fees as a driving force to mine a new block and package transactions, while users compete by bidding transaction fees for faster confirmation. Considering the particularity of Bitcoin trading system, we take time series into consideration to analyze the transaction rules of Bitcoin system from the perspective of multiple cycles and establish a dynamic game model related to time under Generalised Second Price(GSP) mechanism, and also confirm the model's superiority on saving users' fees, compared with the static game model. Also, we propose the quantification of the user experience quantified by calculating the price difference between the transactions uploaded by the same user within adjacent times, making the transaction process of the Bitcoin system no longer the final say of the transaction price. The dynamic game model shows that there is a perfect Bayesian game equilibrium solution in the payment decision, so there is no incentive for users to change the attached fee, and the whole system is maintained stably. In addition, we verify the dynamic game model from computational experiment. Firstly, it is proved that with the help of revenue discount, the cost saving of the dynamic model is generally higher than that of the static model. Then the user's revenue under the dynamic model is showing an upward trend, and the transactions order under the dynamic model is more stable than that under static model, which can be illustrated mathematically and computationally that the proposed dynamic game model in this paper will help all transactions be processed more efficiently in a uniform pipeline.

Optimal Generalized Bayesian Nash Equilibrium of Frequency-Constrained Electricity Market in the Presence of Renewable Energy Sources

In this paper, the problem of frequency-constrained electricity market (FCEM) is modeled in the presence of renewable energy sources (RESs) and price-maker players by using equilibrium problem with equilibrium constraints formulation. It is worth mentioning that due to the intermittency of RESs, the FCEM problem in the presence of price-maker players becomes more imperative as stability of power system frequency is a public good and the free-rider problem arises. Moreover, as players of FCEM do not know their rivals' objective functions, the problem is modeled based on Bayesian game theory. The FCEM problem is converted into a game of complete but imperfect information under the common prior assumption. Here, an optimal generalized Bayesian Nash equilibrium (OGBNE) for the FCEM problem is presented, from which price-maker players do not have any incentive to deviate while power system dynamic is improved simultaneously. The effectiveness of proposed OGBNE compared with other specific dispatches is evaluated by implementing it on an IEEE 30-bus test system in the case study section. Results verify the superiority and appropriateness of the proposed OGBNE dispatch.

Privacy Protection: An Anti-Fraud Scheme Based on Improved Bayesian Game Model in Multimedia Communication System

In the multimedia communication system, users are often exposed to fraud information from malicious applications (such as providing fake bids and false content), which can easily cause privacy leakage. And multimedia information pluralism make it more difficult to protect users’ privacy. To solve these problems, this article proposed an anti-fraud scheme based on improved Bayesian game model. First, we designed a Bargain-bayesian game model for modeling the interaction between applications and regular users. We used the Two-round bargain method to establish payoff matrix for two players and adjust it through regular user’s detection rate dynamically. Then we obtained the application’s best bid in the first round of bargain by backward induction to prevent malicious applications from sending fake bids. Second, we customized a group of test users and developed another interaction model between applications and users (i.e., regular and test) as the Two-side Bayesian game model based on sliding adaptive logistic regression method, then we put three influencing factors into the payoff matrix: test user’s ratio, malicious application’s ratio, and regular user’s ratio. Through Bayesian Nash Equilibria analysis, we obtained values of three influencing factors when malicious applications provide true content, and thus solved the problem of malicious applications providing false content. Finally, experiment results proved that the new scheme had effectively raised expected payoffs for both players and their transaction achievement rate, and lowered the probability of users being deceived by malicious applications, which successfully solved the issue of users’ privacy disclosure.

Social Advantage with Mixed Entangled States

It has been extensively shown in past literature that Bayesian game theory and quantum non-locality have strong ties between them. Pure entangled states have been used, in both common and conflict interest games, to gain advantageous payoffs, both at the individual and social level. In this paper, we construct a game for a mixed entangled state such that this state gives higher payoffs than classically possible, both at the individual level and the social level. Also, we use the I-3322 inequality so that states that aren’t useful advice for the Bell-CHSH1 inequality can also be used. Finally, the measurement setting we use is a restricted social welfare strategy (given this particular state).

Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines

Tremendous amounts of oil and gas products are transported in pipelines worldwide resulting in increasing interest to identify the hazards and evaluate the associated risks associated with this critical infrastructure. Third-party intrusion is one of the least quantifiable factors being considered during the pipeline hazard assessment stage despite the substantial contributing to the total number of oil and gas pipeline incidents. This is because a probabilistic risk assessment cannot reliably model human actions and be applied to intentional acts. Due to the distinctive motivations of third-party damage, an unintentional third-party damage Bayesian Network model and a game-theoretic model on malicious intrusion will therefore be built, to examine the mechanism of pipeline failure caused by this mode. This study is conducted aiming at investigating pipeline risk resulting from third-party damage, and will formulate risk assessment models to identify threats, prioritize risks and determine which integrity plan should apply to different pipeline segments given the condition of third-party interference (both the accidental damage and malicious acts).

Impacts of Manufacturer Overconfidence on Supply Chain Performance with Demand Forecasting

In this paper, we construct a dyadic supply chain consisting of a single manufacturer with overconfidence and a rational retailer under the MTO strategy. The manufacturer and the retailer play a Bayesian Stackelberg game in our model. We consider that the manufacturer may underestimate the variability in his forecast precision (PR bias). We derive the optimal decisions for two supply chain scenarios: one dominated by manufacturers (MS) and one dominated by retailers (RS). Our results suggest that both the manufacturer and the retailer can achieve profit growth under PR bias, but the forecasts made by each side must meet certain conditions. Further, the impacts of each type of overconfidence on SC performance are similar under the MS and RS models, and the manufacturer is always more likely to benefit from his confidence under the RS model than under the MS model. In addition, the influences of the overconfidence on retailers’ profits require additional judgment with effort costs.

Optimal Network Defense Strategy Selection Based on Markov Bayesian Game

Existing passive defence methods cannot effectively guarantee network security; to solve this problem, a novel method is proposed that selects the optimal defence strategy. The network attack-defence process is modelled based on the Bayesian game. The payoff is quantified from the impact value of the attack-defence actions. The optimal defence strategy is selected that takes defence effectiveness as the criterion. The rationality and feasibility of the method are verified through a representative example, and the general rules of network defence are summarised. Compared to the classic strategy selection methods based on game theory, the proposed method can select the optimal strategy in the form of pure strategy by quantifying defence effectiveness, which was proven to perform better.

A Game-Theoretic Analysis of Energy-Depleting Jamming Attacks with a Learning Counterstrategy

Jamming may become a serious threat in Internet of Things networks of battery-powered nodes, as attackers can disrupt packet delivery and significantly reduce the lifetime of the nodes. In this work, we model an active defense scenario in which an energy-limited node uses power control to defend itself from a malicious attacker, whose energy constraints may not be known to the defender. The interaction between the two nodes is modeled as an asymmetric Bayesian game where the victim has incomplete information about the attacker. We show how to derive the optimal Bayesian strategies for both the defender and the attacker, which may then serve as guidelines to develop and gauge efficient heuristics that are less computationally expensive than the optimal strategies. For example, we propose a neural-network-based learning method that allows the node to effectively defend itself from the jamming with a significantly reduced computational load. The outcomes of the ideal strategies highlight the tradeoff between node lifetime and communication reliability and the importance of an intelligent defense from jamming attacks.

Pricing Game With Complete or Incomplete Information About Spectrum Inventories for Mobile Virtual Network Operators

In network virtualization, mobile virtual network operators (MVNOs) lease spectrum resources from mobile network operators (MNOs) and offer certain wireless services to end users. Each MVNO competes with others using optimal price and spectrum inventory so as to profit. However, the inventory of one MVNO is private information and maybe unknown to others, which makes pricing decisions difficult for MVNOs. In this work, we first study the pricing strategy given others’ inventory information. We model the pricing decision problem using the non-cooperative game theory, and develop an optimal price setting algorithm based on an ordinal potential game. Then we put forward three cooperation strategies for MVNOs and analyze the impacts of the coalitions structure on pricing decision. For the situation where the inventory of one MVNO is unknown to others, we use the Bayesian coalition formation game to formulate the pricing decision problem and propose an optimal price setting algorithm based on the Minimum Mean-Square Error to resolve the conflicts resulting from the uncertainty. Next we define a Belief Pareto Order to characterize the preferences of MVNOs regarding the coalition structures. Then we devise a distributed coalition formation algorithm with the proposed belief Pareto order to achieve a Bayesian-Nash stable coalition structure that enables each MVNO to maximize its own revenue. Finally, simulation results demonstrate comprehensive performance evaluation of the proposed game model and provide a guidance on pricing strategies for MVNOs.

A Game Theoretic Approach to Peer Review of Grant Proposals

Abstract This paper studies the grant peer review process employed by the Turkish regional development agencies, which is adapted from a review procedure of the Education, Audiovisual and Culture Executive Agency of the European Union. To model this process, we consider a Bayesian strategic-form game played by three reviewers who observe both a common and a private score signal about an evaluated project and assign their scores to minimize the sum of their disutilities from the false acceptance and false rejection of the project. We numerically compute the Bayesian Nash equilibria of this game and conduct several comparative statics exercises, after calibrating the model parameters accordingly. We also introduce two simpler review processes and compare their performances to that of the calibrated process in terms of outcome statistics, involving pass and fail rates of the evaluated projects, and manipulation statistics, involving the reviewers’ manipulation rate and size of scores.

Network Attack and Defense Game Theory Based on Bayes-Nash Equilibrium

In the process of constructing the traditional offensive and defensive game theory model, these are some shortages for considering the dynamic change of security risk problem. By analysing the critical indicators of the incomplete information game theory model, incomplete information attack and defense game theory model and the mathematical engineering method for solving Bayes-Nash equilibrium, the risk-averse income function for information assets is summarized as the problem of maximising the return of the equilibrium point. To obtain the functional relationship between the optimal strategy combination of the offense and defense and the information asset security probability and risk probability. At the same time, the offensive and defensive examples are used to visually analyse and demonstrate the incomplete information game and the Harsanyi conversion method. First, the incomplete information game and the Harsanyi conversion problem is discussed through the attack and defense examples and using the game tree. Then the strategy expression of incomplete information static game and the engineering mathematics method of Bayes-Nash equilibrium are given. After that, it focuses on the offensive and defensive game problem of unsafe information network based on risk aversion. The problem of attack and defense is obtained by the issue of maximizing utility, and then the Bayes-Nash equilibrium of offense and defense game is carried out around the security risk of assets. Finally, the application model in network security penetration and defense is analyzed by designing a simulation example of attack and defense penetration. The analysis results show that the constructed income function model is feasible and practical.

A Performance on Repeated Bayesian Game Theory in Wireless Sensor Networks

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