Game Theoretic Approach Research Articles

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.

An agent-based simulation study for escaping the “chicken-egg” dilemma between electric vehicle penetration and charging infrastructure deployment

Although charging infrastructure is a prerequisite for widespread electric vehicle (EV) penetration, its deployment is prone to a "chicken-egg" dilemma in the early stages of electrification. Previous studies mainly applied game theoretical approaches to identify the optimal strategy for escaping the dilemma, with minimal attention paid to the complex interactions between EV adoption and charging infrastructure deployment, which is the intrinsic cause of the “chicken-egg” dilemma. Hence, this study establishes an agent-based model to capture the dynamic interactions among EV adoption decisions, EV charging activities, and charging infrastructure investment and operation. Empirical data from Beijing is further applied to calibrate and validate the model before conducting simulation experiments to examine the effectiveness of financial subsidies and environmental factors for resolving the dilemma. The findings suggest that either purchase subsidies for EV adopters or operation subsidies and construction subsidies for charging point operators can help escaping the dilemma only if their subsidy level is within a reasonable range. Otherwise, subsidy strategies may lead to a heavy fiscal burden or an overallocation of charging resources. A substantial change in external factors such as EV sales prices, gasoline prices, and EV driving range can also initiate a virtuous circle between EVs and charging infrastructure, indicating that policy interventions towards these factors may shed light on tackling the dilemma.

Probabilistic coverage in mobile directional sensor networks: a game theoretical approach

Probabilistic coverage in mobile directional sensor networks: a game theoretical approach

An ICN-Based Data Marketplace Model Based on a Game Theoretic Approach Using Quality-Data Discovery and Profit Optimization

An ICN-Based Data Marketplace Model Based on a Game Theoretic Approach Using Quality-Data Discovery and Profit Optimization

Me, myself and I: A general theory of non-Markovian time-inconsistent stochastic control for sophisticated agents

We develop a theory for continuous-time non-Markovian stochastic control problems which are inherently time-inconsistent. Their distinguishing feature is that the classical Bellman optimality principle no longer holds. Our formulation is cast within the framework of a controlled non-Markovian forward stochastic differential equation, and a general objective functional setting. We adopt a game-theoretic approach to study such problems, meaning that we seek for sub-game perfect Nash equilibrium points. As a first novelty of this work, we introduce and motivate a refinement of the definition of equilibrium that allows us to establish a direct and rigorous proof of an extended dynamic programming principle, in the same spirit as in the classical theory. This in turn allows us to introduce a system consisting of an infinite family of backward stochastic differential equations analogous to the classical HJB equation. We prove that this system is fundamental, in the sense that its well-posedness is both necessary and sufficient to characterise the value function and equilibria. As a final step we provide an existence and uniqueness result. Some examples and extensions of our results are also presented.

Learning How to Configure LoRa Networks With No Regret: A Distributed Approach

Long range (LoRa) is one of the most popular technologies for low-power wide area networks. It offers long-range communication with a low energy consumption, which makes it ideal for many applications in the Internet of Things. The performance of LoRa networks depends on the communication parameters used by individual nodes. Several works have proposed different solutions, typically running on a central network server, to select these parameters. However, existing approaches have not addressed the need to (re-)assign parameters when channel conditions suddenly vary due to additional traffic, changes in the weather or the presence of obstacles. Moreover, allocation strategies that require a central entity to decide communication parameters do not scale due to the large number of configuration packets that must be sent to the nodes. To address these issues, this article proposes <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NoReL</monospace> , a distributed game-theoretic approach that allows nodes to autonomously update their parameters and maximize their packet delivery ratio. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NoReL</monospace> is based on a stochastic variant of no-regret learning, which is proven to reach an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -coarse correlated equilibrium in LoRa networks. Extensive simulations show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NoReL</monospace> achieves a higher delivery ratio than the state of the art in both static and dynamic environments, with an improvement up to 12%.

Game theoretical approach for critical sizing of energy storage systems for residential prosumers

The integration of Energy Storage Systems (ESS) into the distribution system has resulted in changes in the behavior of energy management systems. The primary objective of ESS management on the consumer side is to minimize operational costs reflected in energy bills, while the main goal for the Distribution System Operator (DSO) is to improve and smooth the aggregated load profile. Utilizing ESSs in distribution systems requires thorough research, as incorrect implementation can reduce controllability of distribution networks. This paper investigates the relationship between energy cost and the load responsiveness index of PV-ESS equipped residential loads using game theoretic approach. Sensitivity analysis is conducted to examine the relationship between load responsiveness and factors such as load profile, PV size, and energy cost. The goal of the paper is to propose a new approach for optimal sizing of ESS.

Cloud service selection in IoFT-enabled Multi-access Edge Computing: a Game Theoretic approach

Cloud service selection in IoFT-enabled Multi-access Edge Computing: a Game Theoretic approach