Coalitional Game Research Articles

Intrusion detection game for ubiquitous security in vehicular networks: A signaling game based approach

Wireless communications between smart vehicles can make travel quicker and more secure in vehicular networks. However, hackers can perform malicious activities against the communication system, which may cause accidents. To overcome this issue and insure universal public safety on roads, motoring organization needs to reinforce the security of the embedded systems to shield them from different vindictive attacks. In this research work, we present an intrusion detection approach to recognize noxious nodes. When an accident occurs, it gets stored in the vehicles data. Be that as it may, because of the presence of malicious nodes, this data can be erased from the network. Concerning this matter, we have developed a numerical model based on both the coalition game and the signaling game to design an Intrusion Detection Game (IDG). On the one hand, this approach aims to model the interactions between malicious nodes and the Coalition Head which is equipped with an Intrusion Detection System (CH-IDS). On the other hand, it intends to seek the Bayesian Nash Equilibrium (BNE) for an efficient intrusion detection. The simulation results have shown the effectiveness of the proposed approach, thus, the CH-IDS agents are able to select their optimal strategies to detect malicious nodes.

An algorithm to compute the average-of-awards rule for claims problems with an application to the allocation of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} emissions

The set of awards vectors for a claims problem coincides with the core of the associated coalitional game. We analyze the structure of this set by defining for each group of claimants a, so called, utopia game, whose core comprises the most advantageous imputations available for the group. We show that, given a claims problem, the imputation set of the associated coalitional game can be partitioned by the cores of the utopia games. A rule selects for each claims problem a unique allocation from the set of awards vectors. The average-of-awards rule associates to each claims problem the geometric center of the corresponding set of awards vectors. Based on the decomposition of the imputation set, we obtain an interpretation of the average-of-awards rule as a point of fairness between stable and utopia imputations and provide a backward recurrence algorithm to compute it. To illustrate our analysis, we present an application to the distribution of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} emissions.

Negotiating the Opening of International Organizations to Non-State Actors: The Case of the World Health Organization

Abstract Based on empirical fieldwork, this article analyzes the negotiation of a Framework of Engagement with Non-State Actors (FENSA) at the World Health Organization (WHO) between 2011 and 2016. The study examines the long and difficult negotiation process, which shifts from a mere adaptation of guidelines by the WHO Secretariat to an intergovernmental negotiation led by a core group of Member States. This negotiation process cannot simply be explained by traditional dialogues among delegations and hierarchical relationships within international organizations. Other factors played important roles in the process as well – the high political stakes of the issue, non-traditional coalition games, heterogeneity among the actors, and the WHO Secretariat’s role as a negotiating party – all advocating for the development of a triangular, multi-voice negotiation model within international organizations.

Energy management system in smart buildings based coalition game theory with fog platform and smart meter infrastructure

This paper proposes a central energy management system (EMS) in smart buildings. It is based on the coalition method for optimal energy sharing between smart buildings. Game theory is applied to obtain an optimal allocation of the building's surplus energy on the deficient energy buildings using the Shapley value, which enables the unequal energy distribution based on the energy demand. The main objective is reducing energy waste while preserving the generation/demand balance. The fog platform with memory storage is applied, which handles all the measured data from the smart buildings through Wi-Fi-based communication protocol and performs the EMS program. The smart meter links the smart buildings with the fog-based EMS central unit. Two scenarios are implemented based on the difference between total deficient and surplus energy. Coalition game theory is applied for optimal surplus energy allocation on deficient buildings when the total energy surplus is lower than the total energy deficient. Also, there is a one-to-one relationship between the surplus and deficient building; if the surplus energy is larger than the deficit, the extra surplus energy is stored for further usage. The proposed EMS is applied and tested using a smart city with 10 buildings in the MATLAB program. A comparison between the result obtained with and without applying the proposed method is performed. The performance of the fog platform is introduced based on the run and delay time and the memory size usage. The results show the effectiveness of the proposed EMS in a smart building.

A Joint Learning and Game-Theoretic Approach to Multi-Dimensional Resource Management in Fog Radio Access Networks

Fog radio access networks (F-RANs) have been regarded as a promising paradigm to support latency-sensitive and computation-intensive services by leveraging computing and caching capabilities of fog access points (F-APs). To execute offloaded computation tasks, it is essential to pre-store necessary programs and databases at F-APs, referred as service caching. However, due to system dynamics and the coupling of decisions, cache, radio, and computation resource management at F-RANs is a challenging problem. In this paper, a joint multi-dimensional resource optimization problem is formulated, aiming at minimizing the weighted sum of expected latency cost and caching cost, which is featured by a two-timescale structure. For radio and computation resource allocation on a small timescale, a coalitional game based approach is proposed under given caching decisions. On a larger timescale, services are cached at each F-AP using a multi-agent reinforcement learning algorithm. The advantage of the proposed algorithms is that they can implicitly take the impact of small timescale resource allocation into account while adapting to the long-term statistics of channel coefficients and user service requests. We analyze the proposed algorithms with respect to their convergence, optimality, and complexity, and validate their performance with extensive simulations, where superior performance is observed over several baseline schemes.

Optimization of 3D Spectrum Management in Future Wireless Networks

In this paper, we propose an application customized network slicing mechanism to cope with dynamic traffic in future networks and various service requirements for network users, namely, context-aware network slicing (CANS). The resource allocation is carried out in frequency (f), space (s) and time (t) domains (referred to as three domains (3D) resource allocation) while the customization targets at low latency and high reliability, characteristic for IoT applications. We first formulate CANS as a two-step matching game with unknown preferences for access and back-haul networks. To dynamically resize mobile users' network slices (NSs), we propose application customized CANS++ method based on a coalition game to minimize the hand-off risk in the network. We then develop two-step stable matching (TSSM) and <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> -Pareto stability techniques to find the solutions to CANS and CANS++, respectively. To improve energy and cost efficiency, an artificial intelligence-based Dynamic Network Architecture (DNA) with ON/OFF scheduling (AI-DOS) is proposed to dynamically maximize NS utilization. Finally, we carry out extensive numerical analysis and demonstrate that context-aware 3D network slicing and resizing schemes can improve at least three times the users' utilities. These utilities are enhanced up to 180% by minimizing their hand-off risk. Furthermore, NS shrinking technique can reduce the number of active nodes (controlled in the s-domain) by factor five while the average utility of macro base stations, controlled in the t-domain, is improved between 70% and 500%.

Coalition Game Based Distributed Clustering Approach for Group Oriented Unmanned Aerial Vehicle Networks

Clustering is an effective solution to improve the management efficiency of large-scale systems. One one hand, UAVs performing the same task have a similar moving tendency. One the other hand, the network topology of the UAV swarms is dynamically changing. Considering the above two aspects, our main contributrons are designing a group-oriented distributed clustering algorithm based on the coalition game that couples task attributes and communication attributes. The clustering goal is to divide UAVs performing the same task into one cluster and make each cluster have more UAVs under the cluster size limits, thus improving communication efficiency. The proposed algorithm comprehensively considers the task group information and communication link stability as the coalition value. UAVs decide whether to leave their current coalition based on coalition values. Through periodic parallel switch operations selection, UAVs are divided into the desired clustering structure. Simulations verify that our clustering algorithm is effective and better than the existing ones, especially in communication link stability, cluster numbers, and load balance.

Repositioning Game for Ambulance Services

In emergency medical services, ambulances are the first line of response, and the location of ambulances is one of the critical factors that influence the response time and, in turn, the coverage that is used as a metric to quantify the performance of an ambulance network. In this paper, we contribute to the literature of the dynamic repositioning of ambulances using compliance tables to improve coverage. We use a centrality measure, the Shapley value—a solution concept from coalitional game theory that measures the importance of a node in a network—to develop a compliance table for ambulance repositioning. Since the problem of computing the Shapley value often comes under the complexity class of [Formula: see text], we propose a compact representation of the ambulance repositioning problem called the repositioning game. The repositioning game includes the unavailability of ambulances and the variability in travel times. We show that the Shapley value of the repositioning game can be computed in polynomial [Formula: see text] time. Using the ambulance demand data of Chennai, India, we conduct discrete event simulation, and the simulation results indicate that the Shapley-value-based compliance table provides higher coverage than the criticality-based compliance table and the compliance table developed using the maximum expected covering location problem (MEXCRP) model when the ambulance network is constrained.

Shapley values reveal the drivers of soil organic carbon stock prediction

Abstract. Insights into the controlling factors of soil organic carbon (SOC) stock variation are necessary both for our scientific understanding of the terrestrial carbon balance and to support policies that intend to promote carbon storage in soils to mitigate climate change. In recent years, complex statistical and algorithmic tools from the field of machine learning have become popular for modelling and mapping SOC stocks over large areas. In this paper, we report on the development of a statistical method for interpreting complex models, which we implemented for the study of SOC stock variation. We fitted a random forest machine learning model with 2206 measurements of SOC stocks for the 0–50 cm depth interval from mainland France and used a set of environmental covariates as explanatory variables. We introduce Shapley values, a method from coalitional game theory, and use them to understand how environmental factors influence SOC stock prediction: what is the functional form of the association in the model between SOC stocks and environmental covariates, and how does the covariate importance vary locally from one location to another and between carbon-landscape zones? Results were validated both in light of the existing and well-described soil processes mediating soil carbon storage and with regards to previous studies in the same area. We found that vegetation and topography were overall the most important drivers of SOC stock variation in mainland France but that the set of most important covariates varied greatly among locations and carbon-landscape zones. In two spatial locations with equivalent SOC stocks, there was nearly an opposite pattern in the individual covariate contribution that yielded the prediction – in one case climate variables contributed positively, whereas in the second case climate variables contributed negatively – and this effect was mitigated by land use. We demonstrate that Shapley values are a methodological development that yield useful insights into the importance of factors controlling SOC stock variation in space. This may provide valuable information to understand whether complex empirical models are predicting a property of interest for the right reasons and to formulate hypotheses on the mechanisms driving the carbon sequestration potential of a soil.

Energy management in residential microgrid using model predictive control-based reinforcement learning and Shapley value

This paper presents an Energy Management (EM) strategy for residential microgrid systems using Model Predictive Control (MPC)-based Reinforcement Learning (RL) and Shapley value. We construct a typical residential microgrid system that considers fluctuating spot-market prices, highly uncertain user demand and renewable generation, and collective peak power penalties. To optimize the benefits for all residential prosumers, the EM problem is formulated as a Cooperative Coalition Game (CCG). The objective is to first find an energy trading policy that reduces the collective economic cost (including spot-market cost and peak-power cost) of the residential coalition, and then to distribute the profits obtained through cooperation to all residents. An MPC-based RL approach, which compensates for the shortcomings of MPC and RL and benefits from the advantages of both, is proposed to reduce the monthly collective cost despite the system uncertainties. To determine the amount of monthly electricity bill each resident should pay, we transfer the cost distribution problem into a profit distribution problem. Then, the Shapley value approach is applied to equitably distribute the profits (i.e., cost savings) gained through cooperation to all residents based on the weighted average of their respective marginal contributions. Finally, simulations are performed on a three-household microgrid system located in Oslo, Norway, to validate the proposed strategy, where a real-world dataset of April 2020 is used. Simulation results show that the proposed MPC-based RL approach could effectively reduce the long-term economic cost by about 17.5%, and the Shapley value method provides a solution for allocating the collective bills fairly.

Solving the Urban Domestic Waste Classification Dilemma from a Coalitional Game Perspective

The effective classification of urban domestic waste is the key to achieve a “waste-free city” and provides an essential guarantee for resource utilization. This article takes a coalitional game perspective to study the dilemmas in urban domestic waste separation from the cooperative interaction of residents, government, and enterprises. The study finds that urban domestic waste classification in China is currently facing many problems, focusing on: 1) insufficient consensus among residents, 2) shortage of input funds, 3) corporate profitability difficulties, 4) weak policy constraints, and 5) difficulties in integrating goals. In this regard, each participating body still needs to focus on collective interests, coalitional games, break the dilemma society, and promote the long-term management of urban domestic waste.

Multiplayer reputation-based coalition game model (MRCGM) with dominant strategy analysis for detecting malicious attacks in wireless sensor networks

Wireless Sensor Networks (WSNs) are turning into an essential portion of our lives. Devoid of assuring WSNs security (WS), there remain no broad implementations of WSNs. Because of sensor nodes’ constrained capacities concerning computation, communication, and energy, giving protection to WSNs remains competitive. Indeed, the procedure of applying WS remains adaptable and vibrant that develops consistently. Attack-defend’s crux in WS could be conveyed by collaborative schemes of interdependency when Game Theory (GT) could be employed to consider communications amidst schemes of logical decision-makers. Hence, learning WS alongside GT possesses greater logic. This study proffers a Multiplayer Reputation-based Coalition Game Model (MRCGM) for seeking the false data injection amidst the nodes. An attacker attempts to sustain an ideal level of belief by correlating the identifications within the network (NW) to initiate a victorious transfer. Concurrently, the attacker should acquire a few charges for sustaining the trustability of its identifications. This proffered procedure employs this notion and turns the attack expensive by fixing a global threshold for nodes or identifications to be trustable and functional within the NW. Additionally, the beneficial function of the attacker and defender is as well described. The MRCGM has been correlated with 2 advanced methodologies like security-aware routing scheme employing repeated game (SARSRGM) paradigm and a Game-based Fuzzy Q-learning (GBFQL) scheme concerning diverse criteria. Consequently, the proffered MRCGM attains 124 kbps of Packet Drop Rate, 76.14% of energy efficiency, 96.1% of detection accuracy, 25.45% of energy consumption, and 102.45 ms of routing latency.

Collaborative Coded Distributed Computing Scheme: A Two-Phase Cooperative Game Approach

As a modern communication paradigm, Artificial intelligence based Internet of Things (AIoT) can provide an interactive platform across the globe to enrich the quality of networking services. With the AIoT paradigm, coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. In this article, we propose a novel CDC control scheme in the AIoT platform. Based on the cooperative game theory, the main challenges of our scheme are i) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> value decision for the CDC process, and ii) edge node resource allocation for offloading tasks. Using the ideas of coalition game and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weighted Nash social welfare solution (WNSWS)</i> , our proposed scheme is developed as a two-phase game model to achieve a mutually desirable solution. At the first phase, a dynamic coalition formation is proceeded to select the most adaptable edge nodes for the offloading subtasks. At the second phase, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WNSWS</i> is adopted to effectively share each edge node’s computing resource. Based on the jointly design of these two cooperative games, we explore the synergy effect to optimize the CDC process. In the edge assisted distributed computing infrastructure, our reciprocal combinative approach can provide a fair-efficient solution through the sequential interactions of edge nodes and AIoT devices. In the performance evaluation, we provide extensive simulation analyses to show our scheme’s superiority by comparing with the existing baseline protocols.

A Partial Order for Strictly Positive Coalitional Games and a Link from Risk Aversion to Cooperation

A Partial Order for Strictly Positive Coalitional Games and a Link from Risk Aversion to Cooperation

Cost Minimization for Energy-Constrained Partial Offloading in Cognitive Capacity Harvesting Networks

In this article, we investigate how to provide computation offloading service for secondary users (SUs) in cognitive capacity harvesting networks. In particular, standing on the secondary service provider (SSP)’s point of view, we consider the partial offloading and minimize the total cost of the SSP while satisfying the energy and delay constraints of SUs. We optimize all the related variables comprehensively, including the partial offloading factor, the transmit power, the cognitive radio (CR) router selection, the offloading spectrum block selection, and the computing resource allocation in both SUs and CR routers. We formulate the considered problem as a mixed integer nonlinear programming. We theoretically show that when the selections of the offloading CR router and offloading spectrum block for SUs are given, the optimal computing frequency in SUs and CR routers can be determined by the transmit powers and partial offloading ratios. Based on this, the considered problem is decomposed into a transmit power and partial offloading ratio optimization subproblem, an offloading spectrum block selection subproblem, and a CR router selection subproblem. For the three subproblems, differential evolution, more complex task first, and coalition game algorithms are proposed to solve them, respectively. Simulation results show that the proposed algorithms can greatly improve the system performance.

Peer-to-Peer Sharing of Energy Storage Systems Under Net Metering and Time-of-Use Pricing

Sharing economy has become a socio-economic trend in the transportation and housing sectors. It develops business models leveraging underutilized resources. Like those sectors, power grid is also becoming smarter with many flexible resources, and researchers are investigating the impact of sharing resources here as well that can help to reduce cost and extract value. In this work, we investigate sharing of energy storage devices among individual households in a cooperative fashion. Coalitional game theory is used to model the scenario where the utility company imposes time-of-use (ToU) price and net metering (NM) billing mechanism. The resulting game has a non-empty core and we can develop a cost allocation mechanism with easy to compute analytical formula. Allocation is fair and cost-effective for every household. We design the price for the peer-to-peer (P2P) network and an algorithm for sharing that keeps the grand coalition always stable. Thus sharing electricity of storage devices among consumers can be effective in this set-up. Our mechanism is implemented in a community of 80 households in Texas using real data of load demand and solar irradiance and the results show significant cost savings for our method.

A coalitional game-based joint monitoring mechanism for combating COVID-19

In the absence of effective treatment for COVID-19, disease prevention and control have become a top priority across the world. However, the general lack of effective cooperation between communities makes it difficult to suppress the community spread of the global pandemic; hence repeated outbreaks of COVID-19 have become the norm. To address this problem, this paper considers community cooperation in disease monitoring and designs a joint epidemic monitoring mechanism, in which adjacent communities cooperate to enhance their monitoring capability. In this work, we formulate the epidemiological monitoring process as a coalitional game. Then, we propose a Shapley value-based payoffs distribution scheme for the coalitional game. A comprehensive analytical framework is developed to evaluate the advantages and sustainability of the cooperation between communities. Experimental results show that the proposed mechanism performs much better than the conventional non-cooperative monitoring design and can greatly increase each community’s payoffs.

Towards a more efficient computation of individual attribute and policy contribution for post-hoc explanation of cooperative multi-agent systems using Myerson values

A quantitative assessment of the global importance of an agent in a team is as valuable as gold for strategists, decision-makers, and sports coaches. Yet, retrieving this information is not trivial since in a cooperative task it is hard to isolate the performance of an individual from the one of the whole team. Moreover, it is not always clear the relationship between the role of an agent and his personal attributes. In this work we conceive an application of the Shapley analysis for studying the contribution of both agent policies and attributes, putting them on equal footing. Since the computational complexity is NP-hard and scales exponentially with the number of participants in a transferable utility coalitional game, we resort to exploiting a-priori knowledge about the rules of the game to constrain the relations between the participants over a graph. We hence propose a method to determine a Hierarchical Knowledge Graph of agents’ policies and features in a Multi-Agent System. Assuming a simulator of the system is available, the graph structure allows to exploit dynamic programming to assess the importances in a much faster way. We test the proposed approach in a proof-of-case environment deploying both hardcoded policies and policies obtained via Deep Reinforcement Learning. The proposed paradigm is less computationally demanding than trivially computing the Shapley values and provides great insight not only into the importance of an agent in a team but also into the attributes needed to deploy the policy at its best.

Shapley values for cluster importance

This paper proposes a novel approach to explain the predictions made by data-driven methods. Since such predictions rely heavily on the data used for training, explanations that convey information about how the training data affects the predictions are useful. The paper proposes a novel approach to quantify how different data-clusters of the training data affect a prediction. The quantification is based on Shapley values, a concept which originates from coalitional game theory, developed to fairly distribute the payout among a set of cooperating players. A player’s Shapley value is a measure of that player’s contribution. Shapley values are often used to quantify feature importance, ie. how features affect a prediction. This paper extends this to cluster importance, letting clusters of the training data act as players in a game where the predictions are the payouts. The novel methodology proposed in this paper lets us explore and investigate how different clusters of the training data affect the predictions made by any black-box model, allowing new aspects of the reasoning and inner workings of a prediction model to be conveyed to the users. The methodology is fundamentally different from existing explanation methods, providing insight which would not be available otherwise, and should complement existing explanation methods, including explanations based on feature importance.

Centroids of the core of exact capacities: a comparative study

Capacities are a common tool in decision making. Each capacity determines a core, which is a polytope formed by additive measures. The problem of eliciting a single probability from the core is interesting in a number of fields: in coalitional game theory for selecting a fair way of splitting the wealth between the players, in the transferable belief model from evidence theory or for transforming a second order into a first order model. In this paper, we study this problem when the goal is to determine the centroid of the core of a capacity, and we compare four approaches: the Shapley value, the average of the extreme points, the incenter with respect to the total variation distance and the limit of a procedure of uniform contraction. We show that these four centroids do not coincide in general, we give some sufficient conditions for their equality, and we analyse their axiomatic properties. We also discuss how to define a notion of centrality measure indicating the degree of centrality of an additive measure in the core. Finally, we also analyse these four centroids in the more general context of imprecise probabilities.