Game Theoretic Techniques Research Articles

Trustworthy Anti-Collusion Federated Learning Scheme Optimized by Game Theory

Federated learning, a decentralized paradigm, offers the potential to train models across multiple devices while preserving data privacy. However, challenges such as malicious actors and model parameter leakage have raised concerns. To tackle these issues, we introduce a game-theoretic, trustworthy anti-collusion federated learning scheme, which combines game-theoretic techniques and rational trust models with functional encryption and smart contracts for enhanced security. Our empirical evaluations, using datasets like MNIST, CIFAR-10, and Fashion MNIST, underscore the influence of data distribution on performance, with IID setups outshining non-IID ones. The proposed scheme also showcased scalability across diverse client counts, adaptability to various tasks, and heightened security through game theory. A critical observation was the trade-off between privacy measures and optimal model performance. Overall, our findings highlight the scheme’s capability to bolster federated learning’s robustness and security.

Game theory and governance of protected areas – Peneda-Gerês National Park

The literature shows that integrating tourism management with place governance practices is conducive to harmony and development in urban areas. However, protected areas face peculiar governance and management challenges. This article offers an in-depth study of the governance of the Peneda-Gerês National Park in Portugal. We model the incentive structure of some of its stakeholders through game theoretical techniques and find that early phases were not conducive to cooperation among relevant stakeholders, potentially leading to outcomes that were less than optimal, both to them and to the park. The most recent, co-management, model seems much more promising.

A game theoretic technique for risk-based optimal bidding strategies in energy aggregators of markets: Knowledge management approach

The advent of green energies contributes to their high penetration in the energy sector. It could be guessed from the beginning that societies would so excited about the advantages of these energy sources to deploy in the infrastructure of energy systems even before deep investigation about their possible disadvantages. Unfortunately, the management of these sources has become challenging due to the uncertainty associated with environmental factors which can make social, economic, and technical issues. On the other hand, new emerging technologies such as electric vehicles (EVs) have helped much to reduce air pollution and thus make positive effects on human life. Still, we all accept that new technology can make the problem structure much more complicated. This article proposes an innovative management approach for risk-based optimal bidding strategies of energy aggregators in the energy market considering green sources. Moreover, it proposes a new strategy for the bidding offer strategy for green energy aggregators in the spot electricity market. An innovative bi-level optimization approach is developed for the maximization of the profit of the non-participatory companies considering all the technical and social limitations. The equilibrium market spots in this method are calculated as per the Nash equilibrium rule. In such a system, all producers and consumers are considered market players to propose the EV's behavior and as a result, provide a more competitive environment. The results clearly show the high performance of the proposed model for energy management in a green social and economic framework.

From intelligent agents to trustworthy human-centred multiagent systems

The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS). We have made substantial scientific contributions across learning in MAS, game-theoretic techniques for coordinating agent systems, and formal methods for representation and reasoning. We highlight key results achieved by the group and elaborate on recent work and open research challenges in developing trustworthy autonomous systems and deploying human-centred AI systems that aim to support societal good.

A Survey of Game-Theoretic Approach for Resource Management in Cloud Computing

Cloud computing is a groundbreaking technique that provides a whole lot of facilities such as storage, memory, and CPU as well as facilities such as servers and web service. It allows businesses and individuals to subcontract their computing needs as well as trust a network provider with its data warehousing and processing. The fact remains that cloud computing is a resource-finite domain where cloud users contend for available resources to carry out desired tasks. Resource management (RM) is a process that deals with the procurement and release of resources. The management of cloud resources is desirable for improved usage and service delivery. In this paper, we reviewed various resource management techniques embraced in literature. We concentrated majorly on investigating game-theoretic submission for the management of required resources, as a potential solution in modeling the resource allocation, scheduling, provisioning, and load balancing problems in cloud computing. This paper presents a survey of several game-theoretic techniques implemented in cloud computing resource management. Based on this survey, we presented a guideline to aid the adoption and utilization of game-theoretic resource management strategy.

Energy and Network Aware Workload Management for Geographically Distributed Data Centers

Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution. With increasing data volumes in emerging cloud workloads, it is critical to factor in the network costs for transferring workloads across data centers. For geo-distributed data centers, many researchers have been exploring strategies for energy cost minimization and intelligent inter-data-center workload distribution separately. However, prior work does not comprehensively and simultaneously consider data center energy costs, data transfer costs, and data center queueing delay. In this paper, we propose a novel game theory-based workload management framework that takes a holistic approach to the cloud operating cost minimization problem by making intelligent scheduling decisions aware of data transfer costs and the data center queueing delay. Our framework performs intelligent workload management that considers heterogeneity in data center compute capability, cooling power, interference effects from task co-location in servers, time-of-use electricity pricing, renewable energy, net metering, peak demand pricing distribution, and network pricing. Our simulations show that the proposed game-theoretic technique can minimize the cloud operating cost more effectively than existing approaches.

FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks

Stability of the wireless sensor network (WSN) is the most critical factor in real-time and data-sensitive applications like military and surveillance systems. Many energy optimization techniques and algorithms have been proposed to extend the stability of a wireless sensor network. Clustering is a well regarded method in the research communities among them. Hence, this paper presents hybrid hierarchical artificial intelligence based clustering techniques, named FLAG and I-FLAG. The first phase of these algorithms use game-theoretic technique to elect suitable cluster heads (CHs) and later phase of the algorithms use fuzzy inference system to select appropriate super cluster heads (SCHs) among CHs. The I-FLAG is an improved version of FLAG where additional parameters like energy and distance are considered to elect CHs. Simulations are performed to check superiority of the proposed algorithms over the existing protocols like LEACH, CHEF, and CROSS. Simulation results show that the average stability period of WSN is better in FLAG and I-FLAG compared to other protocols, and so is the throughput of WSN during the stability period.

Identifying the strongest self-report predictors of sexual satisfaction using machine learning

Sexual satisfaction has been robustly associated with relationship and individual well-being. Previous studies have found several individual (e.g., gender, self-esteem, and attachment) and relational (e.g., relationship satisfaction, relationship length, and sexual desire) factors that predict sexual satisfaction. The aim of the present study was to identify which variables are the strongest, and the least strong, predictors of sexual satisfaction using modern machine learning. Previous research has relied primarily on traditional statistical models which are limited in their ability to estimate a large number of predictors, non-linear associations, and complex interactions. Through a machine learning algorithm, random forest (a potentially more flexible extension of decision trees), we predicted sexual satisfaction across two samples (total N = 1846; includes 754 individuals forming 377 couples). We also used a game theoretic interpretation technique, Shapley values, which allowed us to estimate the size and direction of the effect of each predictor variable on the model outcome. Findings showed that sexual satisfaction is highly predictable (48–62% of variance explained) with relationship variables (relationship satisfaction, importance of sex in relationship, romantic love, and dyadic desire) explaining the most variance in sexual satisfaction. The study highlighted important factors to focus on in future research and interventions.

Development of recommendation systems using game theoretic techniques

In the present work, we inquire the use of game theoretic techniques for the development of recommender systems. Initially, the interaction of the two aspects of the systems, query reformulation and relevance estimation, is modelled as a cooperative game where the two players have a common utility, to supply optimal recommendations, which they try to maximize. Based on this modelling, three basic recommendation methods are developed, namely collaborative filtering, content based filtering and demographic filtering. The different methods are then combined to create hybrid systems. In the weighted combination, the use of game theoretic techniques is extended, as it is modelled as a cooperative game. Finally, the methods are combined with the use of a genetic algorithm where game theory is used for the parent selection process. Our work offers a baseline for the efficient combination of recommendation methods through game theory and in addition the novelty method, Choice by Game, for the parent selection process in genetic algorithms which offers consistent performance improvements.

Optimizing Packet Forwarding Performance in Multiband Relay Networks via Customized Reinforcement Learning

In next-generation wireless networks, relay-based packet forwarding, emerged as an appealing technique to extend network coverage while maintaining the required service quality. The incorporation of multiple frequency bands, ranging from MHz/GHz to THz frequencies, and their opportunistic and/or simultaneous exploitation by relay nodes can significantly improve system capacity, however at the risk of increased packet latency. Since a relay node can use different bands to send and receive packets, there is a pressing need to design an efficient channel allocation algorithm without a central oracle. While existing greedy heuristics and game-theoretic techniques, which were developed for multi-band channel assignment to relay nodes, achieve minimum packet latency, their performance drops significantly when network dynamism (i.e., user mobility, non-quasi-static channel conditions) is introduced. Since this problem involves multiple relay nodes, we model it as a Markov Decision Process (MDP) involving various stages, which essentially means that achieving an optimal and stable solution is a computationally hard problem. Since solving the MDP, traditionally, consumes a great deal of time and is intractable for relay nodes, we explore how to approximate the optimal solution in a distributed manner by reformulating a reinforcement learning-based, smart channel adaptation problem in the considered multi-band relay network. By customizing a Q-Learning algorithm that adopts an epsilon-greedy policy, we can solve this re-formulated reinforcement learning problem. Extensive computer-based simulation results demonstrate that the proposed reinforcement learning algorithm outperforms the existing methods in terms of transmission time, buffer overflow, and effective throughput. We also provide the convergence analysis of the proposed model by systematically finding and setting the appropriate parameters.

Is Infidelity Predictable? Using Explainable Machine Learning to Identify the Most Important Predictors of Infidelity

ABSTRACT Infidelity can be a disruptive event in a romantic relationship with a devastating impact on both partners’ well-being. Thus, there are benefits to identifying factors that can explain or predict infidelity, but prior research has not utilized methods that would provide the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict in-person and online infidelity across two studies (one individual and one dyadic, N = 1,295). We also used a game theoretic explanation technique, Shapley values, which allowed us to estimate the effect size of each predictor variable on infidelity. The present study showed that infidelity was somewhat predictable overall and interpersonal factors such as relationship satisfaction, love, desire, and relationship length were the most predictive of online and in person infidelity. The results suggest that addressing relationship difficulties early in the relationship may help prevent infidelity.

Capitalizing on Game Theory for Optimal Marketing Decision in Service Industry: Evidence From Telecommunication Industry in Nigeria

This article aims to capitalize on game theoretic techniques for optimal service marketing decision based on existing literature review. The study collated relevant literature and reviewed the relationship that exists between game theory and marketing mix—product, price, place, and promotion within the Telecommunication industry. Using the game theory techniques—prisoner’s dilemma, Cournot model, Bertrand model, quasi-game model, Stackelberg model for making marketing decisions—the article shows that there is a consensus among scholars that game theoretic techniques facilitate product, pricing, promotion, and distribution decision in the service industry. In addition, an integrated marketing mix model was developed to support this study.

Spheres of legislation: polarization and most influential nodes in behavioral context

Game-theoretic models of influence in networks often assume the network structure to be static. In this paper, we allow the network structure to vary according to the underlying behavioral context. This leads to several interesting questions on two fronts. First, how do we identify different contexts and learn the corresponding network structures using real-world data? We focus on the U.S. Senate and apply unsupervised machine learning techniques, such as fuzzy clustering algorithms and generative models, to identify spheres of legislation as context and learn an influence network for each sphere. Second, how do we analyze these networks to gain an insight into the role played by the spheres of legislation in various interesting constructs like polarization and most influential nodes? To this end, we apply both game-theoretic and social network analysis techniques. In particular, we show that game-theoretic notion of most influential nodes brings out the strategic aspects of interactions like bipartisan grouping, which structural centrality measures fail to capture.

Centers for Disease Control and Prevention as a Strategic Agent in the Pediatric Vaccine Market: An Analytical Approach

Problem definition: Pediatric vaccine markets in the United States are vulnerable to the development of monopolies due to few manufacturers and high research and development costs. This work addresses how the government can ensure the cost-effective procurement of pediatric vaccines by all U.S. children from private manufacturers. The Centers for Disease Control and Prevention’s (CDC) significant patronage of pediatric vaccines affords them leverage in negotiating public-sector prices that prevent the formation of monopolies, but existing vaccine pricing literature excludes the CDC as a rational player. Academic/practical relevance: We combine optimization and game theoretic techniques to address cost-effective immunization of all U.S. children. Methodology: Our optimization model from the CDC’s perspective minimizes negotiated government costs while ensuring adequate national vaccination levels, linking dynamics in public and private sectors, and incorporating competitive manufacturer behavior. The optimization model embeds an extant game theoretic price model to capture competitive interactions among manufacturers in the private sector, where they compete independently of the CDC. The model is validated in an extended case study of the Infanrix–Daptacel vaccine duopoly. Results: The study indicates that dissimilar products advantageously segment markets with asymmetric manufacturers. Furthermore, markets are at lower risk when high-capacity manufacturers have moderate target profits, especially in cases of high demand and asymmetry. We demonstrate that our model can help restabilize a market that experiences a vaccine shortage and that the CDC might mitigate the same shortage using strategies that depend on which manufacturer is limited. We also underline scenarios in which the CDC may be able to prevent monopolies through financial incentives to manufacturers. The results support a paradigm shift from annual contracts to ongoing negotiations, which would enable the CDC to exercise control over high-risk markets. Managerial implications: Our study demonstrates an analytical approach for managerial government officials to influence pediatric vaccine prices via the procurement of public-sector goods.

Policy Coordination and Outcomes: Theoretical Perspectives and Empirical Evidence in Nigeria

The paper discusses the fiscal-monetary coordination and the resultant outcomes in macroeconomic aggregates from theoretical and empirical perspectives. The game-theoretic technique was also used to analyse the policy mix conundrum vis-a-vis the fiscal-monetary policies interaction and how that translates into optimal outcomes in an economy. However, the situation of making or forcing monetary policy to be subordinate to fiscal policy may still not generate socially optimal results. This is not far-fetched as the payoffs in the game-theoretic model suggest the presence of minimal coordination problem but high policy conflict even if both authorities are disciplined. Coordination problem and goal conflict seem to be non-existent - when both fiscal and monetary policy blocks are committed and responsible in their choices. Further analyses indicate that the policy mix of both fiscal and monetary authorities for inflation seemed complementary. Inflation responded negatively to the shock of debt in the short run. However, in the medium term, the shock becomes positive and later returns to the initial state. The study suggests that policy designs in Nigeria must harmonise both stabilisation and growth objectives to have optimal outcomes.

Optimal planning of clustered microgrid using a technique of cooperative game theory

In this paper a cooperative type of game theoretical technique is proposed at the planning stage to model a grid-connected clustered microgrid. In order to make it more applicable, the selected microgrid consists of different combinations of generation resources like wind turbines, solar cells, and batteries. In the game model, generation resources are considered as players and their profit as the payoff. The technique of the Nash bargaining solution is adopted in this study for capacity allocation of generation resources and batteries, and also to maximize the annual profit of individual microgrids and its cluster. As a cooperative game model, all possible coalitions are discussed between the players to find their optimum sizes, and most the suitable one is selected based upon the game theory technique and maximum payoff value. For this purpose, a particle swarm optimization (PSO) algorithm is developed to find the most feasible Nash bargaining solution using MATLAB software. In the simulations and for the system analysis, the realistic electrical load data and weather forecast is taken for a remote town Mount Magnet in Western Australia. Finally, a sensitivity analysis is performed to validate and show the reasonability of the optimized results concerning the proposed clustered system.

Hyperfiniteness and Borel combinatorics

We study the relationship between hyperfiniteness and problems in Borel graph combinatorics by adapting game-theoretic techniques introduced by Marks to the hyperfinite setting. We compute the possible Borel chromatic numbers and edge chromatic numbers of bounded degree acyclic hyperfinite Borel graphs and use this to answer a question of Kechris and Marks about the relationship between Borel chromatic number and measure chromatic number. We also show that for every d > 1 there is a d -regular acyclic hyperfinite Borel bipartite graph with no Borel perfect matching. These techniques also give examples of hyperfinite bounded degree Borel graphs for which the Borel local lemma fails, in contrast to the recent results of Csóka, Grabowski, Máthé, Pikhurko, and Tyros. Related to the Borel Ruziewicz problem, we show there is a continuous paradoxical action of \mathbb Z/2\mathbb Z)^* on a Polish space that admits a finitely additive invariant Borel probability measure, but admits no countably additive invariant Borel probability measure. In the context of studying ultrafilters on the quotient space of equivalence relations under AD, we also construct an ultrafilter U on the quotient of E_0 which has surprising complexity. In particular, Martin’s measure is Rudin–Keisler reducible to U . We end with a problem about whether every hyperfinite bounded degree Borel graph has a witness to its hyperfiniteness which is uniformly bounded below in size.

A node representation learning approach for link prediction in social networks using game theory and K-core decomposition

The role of social networks in people’s daily life is undeniable. Link prediction is one of the most important tasks of complex network analysis. Predicting links is currently becoming a concerned topic in social network analysis. Although many link prediction methods have been proposed in the recent years, most of the existing link prediction methods have unsatisfactory performance because of high time complexity, network size, sparsity, and similarity measures between node pairs for processing topological information. In this paper, we proposed a method for node representation learning and also a new embedding technique is used for generating latent features. We also proposed an improved version of the weighted random walk based on game theoretical technique and k-core decomposition. Node representations are generated via skipgram method. Although most of the link prediction methods have high time complexity, since our method uses Stochastic Gradient Descent for the optimization process, it has linear time complexity with respect to the number of vertices. This causes our algorithm to be scalable to large networks. In addition to that, sparsity is a huge challenge in complex networks and we cannot infer enough information from the structure of the network to make predictions. By learning a low-dimensional representation that captures the network structure, classification of nodes and edges can be done more easily. The performance of the proposed method was evaluated with some benchmark heuristic scores and state-of-the-art techniques on link prediction in several real-world networks. The experimental results show that the proposed method obtains higher accuracy in comparison with considered methods and measures. However, the time complexity is not improved effectively.

Analyzing the effect of government subsidy on shippers’ mode switching behavior in the Belt and Road strategic context

The Chinese government is offering subsidies to promote the recently developed China-Europe rail freight corridor under its Belt and Road Initiative (BRI). Despite significant subsidies, the rail mode (China Railway Express) continues to struggle with low load factor. Accordingly, a competition model (rail vs. maritime) grounded on game-theoretic technique is proposed to analyze the effect of the government subsidy on shippers’ mode switching (from maritime to rail) behavior. Analytical results based on a case of China-Germany rail freight transportation describe shippers’ mode switching patterns in response to a mode subsidy and suggest a subsidy scheme for the government.

Game-based adaptive anomaly detection in wireless body area networks

Wireless Body Area Network (WBAN) is a quite suitable communication tool for medical IoT devices that are deployed to collect physiological parameters and forecast real-time events in order to facilitate the diagnostic decision-making for the medical staff. However, sensor readings may be inaccurate due to resource-constrained devices, sensor misplacement, hardware faults, and other environmental factors. Therefore, anomaly detection is envisioned as a promising approach to deal with unreliable and malicious data injection to improve remote patient monitoring systems and reduce false medical diagnosis. In this context, several data analysis and machine learning tools have been proposed to detect abnormal deviations in WBAN. Nevertheless, no one considers the dynamic context changes of WBAN to provide adaptive and dynamic outlier detection. In addition, most of them ignore the co-existence of strong spatial and temporal correlations between monitored physiological attributes. To this end, we propose a two-level lightweight and adaptive anomaly detection approach to discard false alarms caused by faulty measurements and raise alarms only when a patient seems to be in emergency situations. In the first level, a game-theoretic technique is introduced wherein body-worn sensor nodes exploit the spatiotemporal correlation among readings to locally and adaptively detect anomalous events according to the dynamic context changes of WBAN. In the second level, we apply the Mahalanobis distance in the Local Processing Unit (LPU) which has a global view for multivariate analysis. Our main objective is to ensure a tradeoff between detection accuracy, false positive rates, and network performance while considering the WBAN environment constraints. The proposed approach is evaluated through numerical simulations on a real physiological data set. Simulation results prove the effectiveness of the proposed approach in terms of achieving high detection accuracy with low false alarm rate and energy consumption.