Coalitional Game Research Articles

Feature selection based on game theory optimization to achieve desired performance metrics in seizure onset detection

Background and Objective: Epilepsy is one of the common diseases of the nervous system, which affects almost 1% of the world’s population. The unpredictable nature of epileptic seizures has caused people suffering from it to be deprived of their normal life, and making a device that can detect a seizure onset can help them. Methods:Game theory is a branch of mathematical science that, after proving its ability in economics, has entered into the field of signal processing and is trying to solve complex problems with a new approach. In this paper, we use game theory and coalitional game concepts to provide a novel way of feature selection in the analysis of Electroencephalography (EEG) signals. We have used a variety of statistical, morphological, and frequency features, and with the proposed feature selection algorithm we have attempted to select the smallest coalition which discriminates between two brain states while appropriately allocating resources to satisfy our desired sensitivity, delay, and false alarm. A modified change point detection algorithm has also been introduced to be used to detect epileptic seizures. Results:The results of the test on the CHB-MIT dataset show that using the proposed algorithm, we can achieve improved performance in terms of sensitivity, delay, and false alarm with a few number of features. Our framework also provides a means to focus on desired criteria for different applications. In scenario which the focus was on sensitivity, the proposed framework achieved 99% average sensitivity, 0.4/h average false alarm, and 2.4 s average delay with only using about 2 features for each patient. Conclusion:By applying the proposed framework on the CHB-MIT dataset we observe that we can achieve better performance compared to recent studies with fewer features in terms of sensitivity, delay, and false alarm metrics. Such an algorithm can be used for the purpose of real-time detection and in wearable devices.

Priority coalitional games and claims problems

Priority coalitional games and claims problems

The Gaussian Multiple Access Wiretap Channel With Selfish Transmitters: A Coalitional Game Theory Perspective

The Gaussian Multiple Access Wiretap Channel With Selfish Transmitters: A Coalitional Game Theory Perspective

Energy and throughput aware adequate routing for wireless sensor networks using integrated game theory method

A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR.

Distributed Nash Equilibrium Seeking for Multiple Coalition Games by Coalition Estimate Strategies

Distributed Nash Equilibrium Seeking for Multiple Coalition Games by Coalition Estimate Strategies

Ground-truth validation of uni- and multivariate lesion inference approaches.

Lesion analysis aims to reveal the causal contributions of brain regions to brain functions. Various strategies have been used for such lesion inferences. These approaches can be broadly categorized as univariate or multivariate methods. Here we analysed data from 581 patients with acute ischaemic injury, parcellated into 41 Brodmann areas, and systematically investigated the inferences made by two univariate and two multivariate lesion analysis methods via ground-truth simulations, in which we defined a priori contributions of brain areas to assumed brain function. Particularly, we analysed single-region models, with only single areas presumed to contribute functionally, and multiple-region models, with two contributing regions that interacted in a synergistic, redundant or mutually inhibitory mode. The functional contributions could vary in proportion to the lesion damage or in a binary way. The analyses showed a considerably better performance of the tested multivariate than univariate methods in terms of accuracy and mis-inference error. Specifically, the univariate approaches of Lesion Symptom Mapping as well as Lesion Symptom Correlation mis-inferred substantial contributions from several areas even in the single-region models, and also after accounting for lesion size. By contrast, the multivariate approaches of Multi-Area Pattern Prediction, which is based on machine learning, and Multi-perturbation Shapley value Analysis, based on coalitional game theory, delivered consistently higher accuracy and specificity. Our findings suggest that the tested multivariate approaches produce largely reliable lesion inferences, without requiring lesion size consideration, while the application of the univariate methods may yield substantial mis-localizations that limit the reliability of functional attributions.

AI and game-based efficient resource allocation and interference mitigation scheme for D2D communication

Device-to-device (D2D) communication essentially means to communicate between two devices without the base station interfering. D2D communication is used in wireless 5G networks, the Internet of Things, Bluetooth and WiFi, and vehicular networks. D2D communication is useful as it increases spectral efficiency, network efficiency, user experience, and throughput, all of which are better than those available in cellular communication. Thus, D2D communication is a preferred mode of communication. However, D2D communication faces issues like mode selection, interference, resource allocation (RA), and security. The need for RA stands because multiple D2D users (DUs) compete for the same resources. Now, the goal should be to optimize RA to enhance the spectrum efficiency and network coverage. Still, the issues here are caused by eavesdroppers (ɛ), devices that can interfere with communication and encrypt/decrypt the messages. In our proposed scheme, we combined artificial intelligence (AI) and coalition game theory to resolve the issues of optimized RA and security. The coalition game is used for efficient RA, but it will take all the DUs that increase the overhead into the system. To mitigate this issue, we proposed an AI-based solution, which selects the best DUs based on their channel conditions. Our major evaluation parameters were accuracy, sum secrecy capacity and data rate.

Modelling the triple helix system innovation of the main economies from Latin America: a coalitional game theory approach

Modelling the triple helix system innovation of the main economies from Latin America: a coalitional game theory approach

Coalitional Pareto optimal solution of one differential game

This paper is devoted to the differential positional coalitional games with non-transferable payoffs (games without side payments). We believe that the researches of the objection and counter-objection equilibrium for non-cooperative differential games that have been carried out over the last years allow to cover some aspects of non-transferable payoff coalitional games. In this paper we consider the issues of the internal and external stability of coalitions in the class of positional differential games. For a differential positional linear-quadratic six-player game with a two-coalitional structure, the coefficient constraints are obtained which provide an internal and external stability of the coalitional structure.

A Dynamic Threat Prevention Framework for Autonomous Vehicle Networks based on Ruin-theoretic Security Risk Assessment

In recent years, Autonomous Vehicle Networks (AVNs) have gained significant attention for their potential to make transportation safer and more efficient. These networks rely on Vehicle-to-Vehicle (V2V) communication to exchange critical information, such as location, speed, and driving intentions. However, V2V communication also introduces security vulnerabilities that can be exploited to compromise the safety and privacy of drivers and passengers. Malicious or selfish drivers can potentially intercept, modify, and manipulate V2V communication, causing confusion among vehicles or stealing sensitive data. Therefore, in order to identify and mitigate security threats that could jeopardize V2V communication in AVNs, the implementation of a threat prevention framework is imperative. This paper presents a threat prevention framework that assesses security risks dynamically to facilitate secure message forwarding in V2V communication. First, we propose a dynamic risk assessment technique that utilizes the PIER approach to evaluate the level of security threats posed to V2V communication, and ultimately generate a risk score. Second, we develop a security decay assessment method that utilizes ruin theory to continuously monitor security risk within the AVNs. Third, we design a risk-aware message forwarding protocol based on coalitional game theory to facilitate secure V2V communication. Our experiments using the simulator Veins demonstrate the efficiency and scalability of the proposed framework in preventing potential damage caused by common security threats and enhancing the security of the Automated Highway System (AHS).

Maxileximin Envy Allocations and Connected Goods

Fair allocation of indivisible goods presents intriguing challenges from both a social choice perspective and an algorithmic standpoint. Due to the indivisibility of goods, it is common for one agent to envy the bundle of goods assigned to another agent and, indeed, envy-free solutions do not exist in general. In line with the classical game-theoretic concept of Nucleolus in coalitional games, we propose that a fair allocation should minimize the agents’ dissatisfaction profile in a lexicographic manner, where the dissatisfaction of an agent is defined as her maximum envy towards other agents. Therefore, we seek allocations that minimize the maximum envy. In cases where multiple solutions have an equal maximum value, we minimize the second-worst value, and so on. Additionally, as is customary in fair division problems, we also consider an efficiency requirement: among the allocations with the best agents’ dissatisfaction profile, we prioritize those that maximize the sum of agents’ utilities, known as maximum social welfare. Such allocations, referred to as maxileximin allocations, always exist. In this study, we analyze the computational properties of maxileximin allocations in the context of fair allocation problems with constraints. Specifically, we focus on the Connected Fair Division problem, where goods correspond to the nodes of a graph, and a bundle of goods is allowed if the subgraph formed by those goods is connected. We demonstrate that the problem is F∆P2 -complete, even for instances with simple graphical structures such as path and star graphs. However, we identify islands of tractability for instances with more intricate graphs, such as those having bounded treewidth, provided that the number of agents is bounded by a fixed number and utility functions use small values.

ABP: Attribute-Based Broadcast Proxy Re-Encryption With Coalitional Game Theory

ABP: Attribute-Based Broadcast Proxy Re-Encryption With Coalitional Game Theory

DMUST360: Distortion‐aware multicast‐unicast superposed transmission for multiuser 360‐degree video

Abstract360‐degree video has gained popularity since it is associated with immersive user experience of virtual reality content. However, providing high‐quality 360‐degree video for a large number of users is difficult, especially in limited bandwidth conditions. Nonorthogonal multiple access technology‐enabled multicast offers a possible solution, but it also introduces some challenges, including in terms of satisfying users' individual requirements, ensuring video quality and so forth. In order to address these issues, we introduce a novel distortion‐aware multicast‐unicast superposed transmission scheme for multiuser 360‐degree video, which combines the advantages of multicast, unicast, and nonorthogonal multiple access technologies with consideration of the field of view of multiple users. To improve video quality, a distortion minimization problem is formulated. Based on matching theory, we proposed a multicast‐unicast coalition game algorithm to solve the problem efficiently. A series of trace‐driven simulations are carried out, which have shown the effectiveness of the proposed solution in terms of video quality and throughput.

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select suitable crowdsourcers while contributing information to crowdsourcing tasks, CROWDMATCH considers incentives, information availability and cost, and the decisions of fellow crowdworkers to model the utility functions for both the crowdworkers and the crowdsourcers. Specifically, the paper presents an initial Approximate CROWDMATCH mechanism grounded in matching theory principles, eliminating externalities from crowdworkers’ decisions and enabling each entity to maximize its utility. Subsequently, the Accurate CROWDMATCH mechanism is introduced, which is initiated by the outcome of the Approximate CROWDMATCH mechanism, and coalition game-theoretic principles are employed to refine the matching process by accounting for externalities. The paper’s contributions include the introduction of the CROWDMATCH system model, the development of both Approximate and Accurate CROWDMATCH mechanisms, and a demonstration of their superior performance through comprehensive simulation results. The mechanisms’ scalability in large-scale crowdsourcing systems and operational advantages are highlighted, distinguishing them from existing methods and highlighting their efficacy in empowering crowdworkers in crowdsourcer selection.

Quantitatively Interpreting Residents Happiness Prediction by Considering Factor–Factor Interactions

Exploring the high-effect factors of residents’ happiness is good for a wide range of policy-making for economics and politics in most countries. Limited to the concerns of sociologists, previous efforts manually predefined the relationship among multifactors to analyze the factor–factor interactions with high interpretability by regression models. Recently, deep learning methods show great promising prediction accuracy by automatically learning additive interaction between factors, while it meets the challenges of interpretability. To this end, an unbiased post-hoc and model-agnostic method is promising to quantitatively interpret the results of the happiness prediction model. Thus, this article proposes a novel solution based on the deep neural network (DNN) and the Shapley value to compute the factor–factor interactions in different coalitions based on coalitional game theory. Aiming at evaluating the pairwise interactions interpretability quality of our solution, experiments are conducted on the Chinese General Social Survey (CGSS) and European Social Survey (ESS) questionnaire datasets. By systematic reviews, the experimental results are highly consistent with academic studies in social science. Analyzed by different factor categories, the new finding is that some factors, e.g., body mass index (BMI) and social media, play a crucial role in happiness prediction but are rarely considered in social science studies. Specifically, there are some heavy interactions across multicategories, such as personal information (health and age) with economics (insurance and income). Therefore, this solution can theoretically support the implications of social decision-making.

Hierarchical Task and Power Coalition Game of Radars for Maneuvering Target Tracking in Self-Defensive Suppressive Jamming

Hierarchical Task and Power Coalition Game of Radars for Maneuvering Target Tracking in Self-Defensive Suppressive Jamming

Coalitional Game Theory for Stormwater Management and Green Infrastructure Practices

Coalitional Game Theory for Stormwater Management and Green Infrastructure Practices

A Blockchain-Enabled Coalitional Game Framework for Carbon Emission Trading

A Blockchain-Enabled Coalitional Game Framework for Carbon Emission Trading

Enabling Active Distribution Systems’ Participation in Tertiary Frequency Regulation Through Coalitional Game Theory-Based Reserve Allocation

Enabling Active Distribution Systems’ Participation in Tertiary Frequency Regulation Through Coalitional Game Theory-Based Reserve Allocation

Prediction and Interpretation of Total N and Its Key Drivers in Cultivated Tropical Peat using Machine Learning and Game Theory

Currently, there is a growing interest among research communities in the development of statistical learning-based pedotransfer functions/PtFs to predict mineral soil nutrients; however, similar studies in peatlands are relatively rare. Moreover, extracting meaningful information from these ‘black-box’ models is crucial, particularly concerning their algorithmic complexity and the non-linear nature of the soil covariate interrelationships. This study employed the Pulunggono (2022a) dataset and the bootstrapping method, to (1) develop and evaluate seven PtF models, including both general linear models (GLM) and machine learning (ML) regressors for estimating total nitrogen (N) in tropical peat that has been drained and cultivated for oil palm (OP) in Riau, Indonesia and (2) explaining model functioning by incorporating Shapley Additive Explanation (SHAP), a tool derived from coalitional game theory. This study demonstrated the superior predictive performance of ML-based PtFs in estimating total N compared to GLM algorithms. The top-performing algorithms for PtF models were identified as GBM, XGB, and Cubist. The SHAP method revealed that sampling depth and organic C were consistently identified as the most important covariates across all models, irrespective of their algorithmic capabilities. Additionally, ML algorithms identified the total Fe, pH, and bulk density (BD) as significant covariates. Local explanations based on Shapley values indicated that the behavior of PtF-based algorithms diverged from their global explanations. This study emphasized the critical role of ML algorithms and game theory in accurately predicting total N in peatlands subjected to drainage and cultivation for OP and explaining their model behavior in relation to soil biogeochemical processes.