Game Theoretic Approach Research Articles

A game-theoretical constructive approach for the multi-objective frequency assignment problem

This study presents the game modeling of the multi-objective frequency assignment problem (FAP) in cellular networks considering two conflicting objectives: interference and separation costs, while respecting separation constraints, and thus improving the QoS for end users. Two types of cooperative games are suggested depending on the TRX and frequency selection strategies each using two players, one for each considered objective. A feasible solution consisting of a frequency assignment plan is built during the constructive process. At each step of the game, a player assigns a frequency to a TRX according to a given strategy. The proposed solution being part of both multi-objective optimization (MOO) and game theory (GT), we used the most important measures in these two fields: the hypervolume (HV) and the Nash value, to evaluate the performance of our solutions to able to compare our results with those of these two communities: MOO and GT. The proposed games achieve a good trade-off between the two considered objectives corroborated by tests on two real-world instances, Denver and Seattle, demonstrating the performance of the proposed approach in solving the problem. Furthermore, the proposed performance evaluation method is innovative and also generic, since it can be adapted to any algorithm based on both GT and MOO, taking into account these two aspects.

Optimal DoS attack strategy for cyber-physical systems: A Stackelberg game-theoretical approach

In this paper, the optimal DoS attack on cyber-physical systems is studied in Stackelberg game framework, which is manifested by energy allocation on communication channels. To give a realistic picture of DoS attack on a normal user launched by several hackers, a Stackelberg game between one defender and multiple attackers is considered. Compared with the existing literature, which mainly focus on equilibrium in static games, the dynamic process of the Stackelberg game is also reflected in this paper, which demonstrates the intelligence of attackers switching channels to allocate aggressive energy. In the solution of Stackelberg equilibrium, self-adaptive Particle Swarm Optimization (PSO) algorithm is utilized to cope with the nonlinearity of the reward function. Besides, for the purpose of better performance of both sides in choosing channels to allocate energy, an online computation algorithm is proposed. Finally, numerical examples are equipped to illustrate the theoretical results proved in this work.

A novel game theoretic approach for market-driven dynamic spectrum access in cognitive radio networks

A novel game theoretic approach for market-driven dynamic spectrum access in cognitive radio networks

Investigation of metabolic pathways from gut microbiome analyses regarding type 2 diabetes mellitus using artificial neural networks

BackgroundType 2 diabetes mellitus is a prevalent disease that contributes to the development of various health issues, including kidney failure and strokes. As a result, it poses a significant challenge to the worldwide healthcare system. Research into the gut microbiome has enabled the identification and description of various diseases, with bacterial pathways playing a critical role in this context. These pathways link individual bacteria based on their biological functions. This study deals with the classification of microbiome pathway profiles of type 2 diabetes mellitus patients.MethodsPathway profiles were determined by next-generation sequencing of 16S rDNA from stool samples, which were subsequently assigned to bacteria. Then, the involved pathways were assigned by the identified gene families. The classification of type 2 diabetes mellitus is enabled by a constructed neural network. Furthermore, a feature importance analysis was performed via a game theoretic approach (SHapley Additive exPlanations). The study not only focuses on the classification using neural networks, but also on identifying crucial bacterial pathways.ResultsIt could be shown that a neural network classification of type 2 diabetes mellitus and a healthy comparison group is possible with an excellent prediction accuracy. It was possible to create a ranking to identify the pathways that have a high impact on the model prediction accuracy. In this way, new associations between the alteration of, e.g. a biosynthetic pathway and the presence of diabetes mellitus type 2 disease can also be discovered. The basis is formed by 946 microbiome pathway profiles from diabetes mellitus type 2 patients (272) and healthy comparison persons (674).ConclusionWith this study of the gut microbiome, we present an approach using a neural network to obtain a classification of healthy and type 2 diabetes mellitus and to identify the critical features. Intestinal bacteria pathway profiles form the basis.

A game theoretic approach to sustainable freight transportation: competition between green, non-green and semi-green transportation networks under government sustainable policies

A game theoretic approach to sustainable freight transportation: competition between green, non-green and semi-green transportation networks under government sustainable policies

Game-theoretic approach to epidemic modeling of countermeasures against future malware evolution

Recently, vulnerability mining techniques that discover unknown vulnerabilities based on machine learning have been attracted much attention for protecting software. Although we benefit from these techniques for cyber security, they could be exploited by malicious attackers. For example, the literature has introduced a concept of future malware exploiting vulnerability mining techniques. It discovers vulnerabilities of hosts by performing vulnerability mining with the use of the computing resources of hosts infected with the malware. In this paper, we propose a game-theoretic approach to epidemic modeling for discussing how to counter such future malware evolution. In the proposed approach, we consider a countermeasure model that constructs a countermeasure group aiming to discover vulnerabilities earlier than malware or malicious attackers, and repair them to protect hosts not to get infected with the malware. This paper provides stochastic epidemic modeling for the countermeasure model, which represents the infection dynamics of future malware based on a continuous-time Markov chain under countermeasure environments. Furthermore, we apply evolutionary games on complex networks to the epidemic model in order to represent the selfish behavior of hosts participating in the countermeasure group. Through simulation experiments, we reveal strategies to efficiently counter the future malware evolution.

The EU emissions trading system and airline low-carbon transition: A game-theoretic approach

Aviation is among one of the fastest-growing sources of greenhouse gas emissions (GHG). Thus, transitioning into a more low-carbon approach for future sustainable growth in the airline industry is necessary. Inspired by European Union's GHG emissions trading system that allows automobile makers to “pool” their carbon emissions, we study three emission pooling scenarios for the airline industry. Under the EU emissions trading system (EU ETS), passenger and cargo airlines such as Lufthansa and FedEx can either (1) pay a premium for each ton of carbon dioxide equivalent (CO2eq) they fall short of free allowances (Pay Premium Strategy) or (2) buy allowances from other industries in the EU ETS market (Market-Based Strategy) (3) partnership with other airlines to “pool” their carbon emissions (Cooperative Strategy). These three approaches correspond to different market structures. The latest strategy has a co-opetition relationship between airlines: traditional airlines operating older aircraft purchase emission allowances from airlines operating newer aircraft. The purpose of this paper is to examine the conditions for airlines' paying penalties, purchasing allowances from the ETS market, or cooperation. We demonstrate that the EU ETS will always be detrimental to airlines using older aircraft. A hefty premium may effectively induce airlines to pursue a low-carbon transition plan. Our study shows that the ETS is not a silver bullet; it only works when the premium mechanism is designed properly and a fair market for emission allowances is available.

Improving urban resilience against climate change through government tax policies to housing companies: A game-theoretic approach

Mass housing companies play a vital role in improving the resilience of urban infrastructure against disasters. Equipping residential settlements against disasters caused by climate change increases the safety of urban infrastructure against these disasters. However, the continuation of these measures requires government support due to their high cost. The present study establishes a three-stage stackelberg game model and examines the role of government tax policies in increasing the resilience of residential settlements. This model consists of the government, mass housing companies, and whole building buyers. The decisions of the players and their utility are obtained in equilibrium conditions. Then, the model's efficiency is examined by presenting the city of Karaj as a case study. Also, through sensitivity analysis, the effect of different parameters on the interests of the government and companies is investigated, and various managerial insights are suggested for urban management. Based on the results obtained, urban management can simultaneously improve the economic situation, urban infrastructure resilience, and resilient mass housing companies by providing a tax exemption policy within a specific range.

Game Theoretic Approach for Robust Trajectory Tracking by a Multirotor

In this article, the problem of precisely tracking a nominal trajectory by an under-actuated multirotor platform is formulated as a game against bounded external disturbances, measurement noise, and initial condition uncertainty. The nominal trajectory is composed of desired position and yaw angle, which is known to be a differentially flat output for the multirotor state dynamics. Using this property, a linearized error dynamics is obtained in the vicinity of the nominal trajectory. Using the linearized error dynamics, a linear-quadratic differential game approach is used to propose an integrated estimation and control method for trajectory tracking. A salient feature of this approach is that the drag coefficients are estimated online from the information of the tracking error. Moreover, there are only six parameters to be tuned for both estimation and control loops. The intuitive rationale behind the tuning of these parameters is also discussed. Simulations as well as experimental validations are presented to demonstrate the performance and applicability of the controllers.

Modelling communication-enabled traffic interactions.

A major challenge for autonomous vehicles is handling interactions with human-driven vehicles-for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver's behaviours. We demonstrate our model's potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model's decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.

Defender Policy Evaluation and Resource Allocation With MITRE ATT&CK Evaluations Data

Protecting against multi-step attacks of uncertain start times and duration forces the defenders into indefinite, always ongoing, resource-intensive response. To allocate resources effectively, the defender must analyze and respond to an uncertain stream of potentially undetected multiple multi-step attacks and take measures of attack and response intensity over time into account. Such response requires estimation of overall attack success metrics and evaluating effect of defender strategies and actions associated with specific attack steps on overall attack metrics. We present a novel game-theoretic approach GPLADD to attack metrics estimation and demonstrate it on attack data derived from MITRE's ATT&CK Framework and other sources. In GPLADD, the time to complete attack steps is explicit; the attack dynamics emerges from attack graph and attacker-defender capabilities and strategies and therefore reflects ‘`physics’' of attacks. This makes time a physical constraint on attack success parameters and enables comparing different defender resource allocation strategies across different attacks. We solve for attack success metrics by approximating attacker-defender games as discrete-time Markov chains and show evaluation of return on detection investments associated with different attack steps. We apply GPLADD to MITRE's ATT&CK Evaluations data and show how this methodology enables defender optimization and resource allocation against multi-step attacks.

A game theoretic approach to balance privacy risks and familial benefits

As recreational genomics continues to grow in its popularity, many people are afforded the opportunity to share their genomes in exchange for various services, including third-party interpretation (TPI) tools, to understand their predisposition to health problems and, based on genome similarity, to find extended family members. At the same time, these services have increasingly been reused by law enforcement to track down potential criminals through family members who disclose their genomic information. While it has been observed that many potential users shy away from such data sharing when they learn that their privacy cannot be assured, it remains unclear how potential users’ valuations of the service will affect a population’s behavior. In this paper, we present a game theoretic framework to model interdependent privacy challenges in genomic data sharing online. Through simulations, we find that in addition to the boundary cases when (1) no player and (2) every player joins, there exist pure-strategy Nash equilibria when a relatively small portion of players choose to join the genomic database. The result is consistent under different parametric settings. We further examine the stability of Nash equilibria and illustrate that the only equilibrium that is resistant to a random dropping of players is when all players join the genomic database. Finally, we show that when players consider the impact that their data sharing may have on their relatives, the only pure strategy Nash equilibria are when either no player or every player shares their genomic data.

Deceptive behaviour and coopetition: the role of heterogeneous absorptive capacities and product specialisation

PurposeThis paper aims to study the role of absorptive capacities in coopetitive alliances that involve leakages of sensitive private knowledge regarding firms’ production processes.Design/methodology/approachThis paper uses a game theoretic approach to model a differentiated product market in which two firms asymmetrically informed about the economic value of a business opportunity must cooperate to exploit this opportunity. Under coopetition, firms gain access to their partners’ core knowledge as the result of inevitable leakages of information. Firms differ in their absorptive capacities, which affects their abilities to leverage this new knowledge outside the collaborative activity.FindingsFirms with superior absorptive capacities are more likely to devise alliances whose purpose is to gain access to their partners’ core knowledge. This opportunistic behaviour does not disappear even if firms compensate their partners for the damages caused by this deceptive business practice. This paper also finds that a highly specialised product safeguards firms with limited absorptive capacities against these opportunistic behaviours.Originality/valueThis paper provides a theoretical analysis of the role that absorptive capacities and product specialisation play in influencing the emergence of opportunistic behaviours in coopetitive alliances. The theoretical analysis underscores the extent to which the risk of opportunism associated with the exploitation of a partner’s specific core knowledge outside the scope of the cooperative activity affects not only the nature and intensity of market competition but also the incentives to pursue coopetitive alliances.

Fuel-Based Nash Bargaining Approach for Adaptive Signal Control in an N-Player Cooperative Game

This paper presents a fuel-based game-theoretic approach for adaptive signal control. Our controller applies Nash bargaining (NB) in an n-player cooperative game to identify optimal phasing splits considering future traffic demands. The fuel-based NB controller utilizes an objective function that combines operational measures (delays and stops) with fuel consumption measures at intersections. The proposed controller was encoded in Python and then implemented and evaluated in a VISSIM microscopic traffic simulation model in an intersection with increasing volumes. The performance of the NB controller was compared to a traditional actuated control as the baseline. The results show that the NB controller was able to achieve superior environmental gains with a 17% saving in fuel consumption and a 17% reduction in CO emissions. In addition, the proposed controller was capable of maintaining acceptable operational conditions as it achieved a 20% reduction in delay, 8% reduction in the number of stops, and 8% reduction in queue lengths compared to the actuated controller. Compared to similar studies that applied NB for adaptive signal control, our fuel-based NB controller stands out as a promising approach to significantly improve fuel consumption at signalized intersections.

A game theoretic approach for advertising and pricing decisions using a new environmental transparency-based demand function considering different government policies

A game theoretic approach for advertising and pricing decisions using a new environmental transparency-based demand function considering different government policies

Fungicide resistance and misinformation: A game theoretic approach

AbstractFungicide resistance is a serious problem for agriculture today. This analysis provides additional insight into the strategic behavior of farmers when their fungicide use generates a negative intertemporal production externality in the form of fungicide resistance. We find that when farmers encounter this type of externality, they choose fungicide levels that exacerbate fungicide resistance. We examine a compensation mechanism in which a farmer reduces fungicide use in exchange for a transfer. This mechanism reduces fungicide use; however, misinformation about the severity of fungicide resistance generates distortions. We find that one‐sided misinformation could lead a farmer to choose socially optimal fungicide levels, which makes the compensation mechanism less necessary. In addition, we show that when both farmers are misinformed, the mechanism could lead farmers to choose fungicide levels below the socially optimal level depending on their pessimistic beliefs about the severity of fungicide resistance.

Unraveling the Dysbiosis of Vaginal Microbiome to Understand Cervical Cancer Disease Etiology-An Explainable AI Approach.

Microbial Dysbiosis is associated with the etiology and pathogenesis of diseases. The studies on the vaginal microbiome in cervical cancer are essential to discern the cause and effect of the condition. The present study characterizes the microbial pathogenesis involved in developing cervical cancer. Relative species abundance assessment identified Firmicutes, Actinobacteria, and Proteobacteria dominating the phylum level. A significant increase in Lactobacillus iners and Prevotella timonensis at the species level revealed its pathogenic influence on cervical cancer progression. The diversity, richness, and dominance analysis divulges a substantial decline in cervical cancer compared to control samples. The β diversity index proves the homogeneity in the subgroups' microbial composition. The association between enriched Lactobacillus iners at the species level, Lactobacillus, Pseudomonas, and Enterococcus genera with cervical cancer is identified by Linear discriminant analysis Effect Size (LEfSe) prediction. The functional enrichment corroborates the microbial disease association with pathogenic infections such as aerobic vaginitis, bacterial vaginosis, and chlamydia. The dataset is trained and validated with repeated k-fold cross-validation technique using a random forest algorithm to determine the discriminative pattern from the samples. SHapley Additive exPlanations (SHAP), a game theoretic approach, is employed to analyze the results predicted by the model. Interestingly, SHAP identified that the increase in Ralstonia has a higher probability of predicting the sample as cervical cancer. New evidential microbiomes identified in the experiment confirm the presence of pathogenic microbiomes in cervical cancer vaginal samples and their mutuality with microbial imbalance.

A game-theoretic approach for investigating the competition between energy producers under the energy resilience index: A case study of Iran

Nowadays, resilience has increasingly attracted scientific attention and has become a controversial issue on the global agenda and sustainable development goals. Energy is one of the most pertinent fields of application of resilience, so we developed the energy resilience index and addressed its role in the competition between a traditional energy producer and a renewable energy producer by applying a game-theoretic approach. In addition to energy producers, the government is considered another energy supply agent. The decision variables for energy producers are the amount of production and, for the government, the amount of subsidy allocated to the renewable energy producer. The results illustrate that higher energy output is attained through renewable energy rather non-renewable energy under energy resilience and government support of energy supply. Consequently, the price of renewable energy is determined lower than the price of non-renewable energy. Moreover, the results extrapolated suggest that the recovery level during disruption is considered more important than the distance between the power plant and the customers in the profitability of the non-renewable energy producer. Finally, several managerial implications are presented for energy supply chain decision-makers.

Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis

Objective:Ovarian cancer is a significant health issue with lasting impacts on the community. Despite recent advances in surgical, chemotherapeutic and radiotherapeutic interventions, they have had only marginal impacts due to an inability to identify biomarkers at an early stage. Biomarker discovery is challenging, yet essential for improving drug discovery and clinical care. Machine learning (ML) techniques are invaluable for recognising complex patterns in biomarkers compared to conventional methods, yet they can lack physical insights into diagnosis. eXplainable Artificial Intelligence (XAI) is capable of providing deeper insights into the decision-making of complex ML algorithms increasing their applicability. We aim to introduce best practice for combining ML and XAI techniques for biomarker validation tasks. Methods:We focused on classification tasks and a game theoretic approach based on Shapley values to build and evaluate models and visualise results. We described the workflow and apply the pipeline in a case study using the CDAS PLCO Ovarian Biomarkers dataset to demonstrate the potential for accuracy and utility. Results:The case study results demonstrate the efficacy of the ML pipeline, its consistency, and advantages compared to conventional statistical approaches. Conclusion:The resulting guidelines provide a general framework for practical application of XAI in medical research that can inform clinicians and validate and explain cancer biomarkers.

Strategies in supply chain competition: A game theoretic approach

In competitive markets for substitutable products it is essential for firms to develop appropriate strategies to sustain competition. This research focuses on the strategies of quality production, product pricing and marketing efforts. The competition between substitutable products is modelled by considering two competing supply chains, each with a manufacturer and a retailer. A supply chain game is developed in which the two manufacturers move first and retailers move next. The horizontal competition between the two manufacturers is modelled as a simultaneous move game, similar is the case with the retailers. The vertical competition between the manufacturer and the retailer in each supply chain is modelled as a manufacturer led Stackelberg game in which the manufacturer optimally decides the quality level of production. The retailer responds by optimally deciding the retail price of the product and the extent of marketing efforts to be undertaken. The findings of this study indicate that the quality level of the product increases with competition intensity among the supply chains. Comparisons with the centralized setting and monopolistic markets are also performed. Finally the competition between supply chains in an environment of asymmetric information about the manufacturer’s cost for quality production is made.