Game Theory Based Model for Predictive Analytics Using Distributed Position Function

This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are distributed over a position spectrum. We generalize the concept of position in the model to incorporate continuous positions for the actors, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between the positions of actors. To validate the proposed extension, we demonstrate the trustworthiness of our model’s performance and interpretation by replicating the results based on data used in earlier studies.

On Ethical Literary Criticism and Game Theory--An Interview with Professor Nie Zhenzhao

In 2014,Huang Kaihong,a professor at School of Foreign Languages and Cultures,Southwest University of Science and Technology,interviewed the Doctoral advisor Professor Nie Zhenzhao during the period of his academic visiting to Central China Normal University.As early as in 2005,Huang Kaihong conducted an interview with Professor Nie Zhenzhao on the topic of the general introduction of ethical literary criticism.So around 11 years later,the second interview mainly covers not only the ethical literary criticism theory,but the game theory and the relationship between them as well.Professor Nie thinks whether the game theory can be applied to literature research is still under discussion.The theory of ethical literary criticism is a kind of methodology based on science and it can get the attention of literary critics at home and abroad,which is because it fits the practical needs of literary criticism,draws the literary criticism away from only emphasizing criticism genres and the research of criticism terms,and pays attention to the true nature of the literary text in literature research.After consulting Professor Nie Zhenzhao about some related questions from the perspective of game theory.Huang Kaihong gets some significant information concerning literature research and understands the latest core terms and the concrete application method of ethical literary criticism,especially the relationship between the instructing and aesthetic functions of literature.

Hybrid data-driven framework for shale gas production performance analysis via game theory, machine learning, and optimization approaches

A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.

A label noise filtering and label missing supplement framework based on game theory

Labeled data is widely used in various classification tasks.However,there is a huge challenge that labels are often added artificially.Wrong labels added by malicious users will affect the training effect of the model.The unreliability of labeled data has hindered the research.In order to solve the above problems,we propose a framework of Label Noise Filtering and Missing Label Supplement(LNFS).And we take location labels in Location-Based Social Networks(LBSN)as an example to implement our framework.For the problem of label noise filtering,we first use FastText to transform the restaurant's labels into vectors,and then based on the assumption that the label most similar to all other labels in the location is most representative.We use cosine similarity to judge and select the most representative label.For the problem of label missing,we use simple common word similarity to judge the similarity of users'comments,and then use the label of the similar restaurant to supplement the missing labels.To optimize the performance of the model,we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model.Finally,a case study is given to illustrate the effectiveness and reliability of LNFS.

Efficient Power Control for UAV Based on Trajectory and Game Theory

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network throughput.Unmanned Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this article.In the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless nodes.The quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility functions.An energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless nodes.It has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a kind.The proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas sent.This is taken into consideration to agree with the objective of 5G networks.In order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory

In real-time strategy(RTS)games,the ability of recognizing other players’goals is important for creating artifical intelligence(AI)players.However,most current goal recognition methods do not take the player’s deceptive behavior into account which often occurs in RTS game scenarios,resulting in poor recognition results.In order to solve this problem,this paper proposes goal recognition for deceptive agent,which is an extended goal recognition method applying the deductive reason method(from general to special)to model the deceptive agent’s behavioral strategy.First of all,the general deceptive behavior model is proposed to abstract features of deception,and then these features are applied to construct a behavior strategy that best matches the deceiver’s historical behavior data by the inverse reinforcement learning(IRL)method.Final,to interfere with the deceptive behavior implementation,we construct a game model to describe the confrontation scenario and the most effective interference measures.

Cooperative game theory-based steering law design of a CMG system

Spacecraft require a large-angle manoeuvre when performing agile manoeuvring tasks, therefore a control moment gyroscope(CMG) is employed to provide a strong moment.However, the control of the CMG system easily falls into singularity, which renders the actuator unable to output the required moment. To solve the singularity problem of CMGs, the control law design of a CMG system based on a cooperative game is proposed. First, the cooperative game model is constructed according to the quadratic programming problem, and the cooperative strategy is constructed. When the strategy falls into singularity, the weighting coefficient is introduced to carry out the strategy game to achieve the optimal strategy. In theory, it is proven that the cooperative game manipulation law of the CMG system converges, the sum of the CMG frame angular velocities is minimized, the energy consumption is small, and there is no output torque error. Then, the CMG group system is simulated.When the CMG system is near the singular point, it can quickly escape the singularity. When the CMG system falls into the singularity, it can also escape the singularity. Considering the optimization of angular momentum and energy consumption, the feasibility of the CMG system steering law based on a cooperative game is proven.

Game Theory-Based Dynamic Weighted Ensemble for Retinal Disease Classification

An automated retinal disease detection system has long been in exis-tence and it provides a safe,no-contact and cost-effective solution for detecting this disease.This paper presents a game theory-based dynamic weighted ensem-ble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection.The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features,and XGBoost classifier for the classification.The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2.A novel ensemble classifier based on the game theory approach is proposed for the fusion of the outputs of the transfer learning model and the XGBoost classifier model.The ensemble approach significantly improves the accuracy of retinal disease pre-diction and results in an excellent performance when compared to the individual deep learning and feature-based models.

Joint Optimization of Imperfect Preventive Maintenance and Production Scheduling for Single Machine Based on Game Theory Method

In this study,an optimization model of a single machine system integrating imperfect preventive maintenance planning and production scheduling based on game theory is proposed.The costs of the production department and the maintenance department are minimized,respectively.Two kinds of three-stage dynamic game models and a backward induction method are proposed to determine the preventive maintenance(PM)threshold.A lemma is presented to obtain the exact solution.A comprehensive numerical study is provided to illustrate the proposed maintenance model.The effectiveness is also validated by comparison with other two existed optimization models.

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks.The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things(IoT)network is widely recognized.Sensor nodes are low-power devices with low power devices,storage,and quantitative processing capabilities.The existing system uses the Artificial Immune System-Particle Swarm Optimization method to mini-mize the energy and improve the network’s lifespan.In the proposed system,a hybrid Energy Efficient and Reliable Ant Colony Optimization(ACO)based on the Routing protocol(E-RARP)and game theory-based energy-efficient clus-tering algorithm(GEC)were used.E-RARP is a new Energy Efficient,and Reli-able ACO-based Routing Protocol for Wireless Sensor Networks.The suggested protocol provides communications dependability and high-quality channels of communication to improve energy.For wireless sensor networks,a game theo-ry-based energy-efficient clustering technique(GEC)is used,in which each sen-sor node is treated as a player on the team.The sensor node can choose beneficial methods for itself,determined by the length of idle playback time in the active phase,and then decide whether or not to rest.The proposed E-RARP-GEC improves the network’s lifetime and data transmission;it also takes a minimum amount of energy compared with the existing algorithms.

Energy-Efficient Clustering Using Optimization with Locust Game Theory

Wireless sensor networks(WSNs)are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data.WSNs use sensor nodes(SNs)to collect and transmit data.However,the power supplied by the sensor network is restricted.Thus,SNs must store energy as often as to extend the lifespan of the network.In the proposed study,effective clustering and longer network lifetimes are achieved using mul-ti-swarm optimization(MSO)and game theory based on locust search(LS-II).In this research,MSO is used to improve the optimum routing,while the LS-II approach is employed to specify the number of cluster heads(CHs)and select the best ones.After the CHs are identified,the other sensor components are allo-cated to the closest CHs to them.A game theory-based energy-efficient clustering approach is applied to WSNs.Here each SN is considered a player in the game.The SN can implement beneficial methods for itself depending on the length of the idle listening time in the active phase and then determine to choose whether or not to rest.The proposed multi-swarm with energy-efficient game theory on locust search(MSGE-LS)efficiently selects CHs,minimizes energy consumption,and improves the lifetime of networks.The findings of this study indicate that the proposed MSGE-LS is an effective method because its result proves that it increases the number of clusters,average energy consumption,lifespan extension,reduction in average packet loss,and end-to-end delay.

Computation of PoA for Selfish Node Detection and Resource Allocation Using Game Theory

The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks(DCN).As the network has the characteristics like decentralized and dynamic,few nodes in the network may not associate with other nodes.These uncooperative nodes also known as selfish nodes corrupt the performance of the cooperative nodes.Namely,the nodes cause congestion,high delay,security concerns,and resource depletion.This study presents an effective selfish node detection method to address these problems.The Price of Anarchy(PoA)and the Price of Stability(PoS)in Game Theory with the Presence of Nash Equilibrium(NE)are discussed for the Selfish Node Detection.This is a novel experiment to detect selfish nodes in a network using PoA.Moreover,the least response dynamic-based Capacitated Selfish Resource Allocation(CSRA)game is introduced to improve resource usage among the nodes.The suggested strategy is simulated using the Solar Winds simulator,and the simulation results show that,when compared to earlier methods,the new scheme offers promising performance in terms of delivery rate,delay,and throughput.

Unraveling Predatory Pricing:Insights From Case Studies and Game Theory Modeling

This paper develops a game-theory model for predatory pricing via in-depth analyses of three case studies:Brooke Group Ltd.v.Brown&Williamson Tobacco Corp.,Matsushita Electric Industries Co.v.Zenith Radio Corporation,and AKZO Chemie BV v.Commission of the European Communities.This model is based on subsequent action game theory models and rational economics behavior,offering a chronological outline of the“predation”stages.It presents the predator’s decisions,the prey’s potential responses,possible loops,and the two distinctive outcomes.The analysis of the model in context of the three case studies demonstrates its practicality in assessing real-life predatory pricing scenarios and players’strategies.It’s flexibility also allows applications in related fields.Overall,this paper offers a comprehensive framework that bridges the gap between law,economics,and game theory in the study of predatory pricing,informing future research in this area.

The Triple Helix of innovation as a double game involving domestic and foreign actors

Purpose:The collaboration relationships between innovation actors at a geographic level may be considered as grouping two separate layers,the domestic and the foreign.At the level of each layer,the relationships and the actors involved constitute a Triple Helix game.The paper distinguished three levels of analysis:the global grouping together all actors,the domestic grouping together domestic actors,and the foreign related to only actors from partner countries.Design/methodology/approach:Bibliographic records data from the Web of Science for South Korea and West Africa breakdown per innovation actors and distinguishing domestic and international collaboration are analyzed with game theory.The core,the Shapley value,and the nucleolus are computed at the three levels to measure the synergy between actors.Findings:The synergy operates more in South Korea than in West Africa;the government is more present in West Africa than in South Korea;domestic actors create more synergy in South Korea,but foreign more in West Africa;South Korea can consume all the foreign synergy,which is not the case of West Africa.Research limitations:Research data are limited to publication records;techniques and methods used may be extended to other research outputs.Practical implications:West African governments should increase their investment in science,technology,and innovation to benefit more from the synergy their innovation actors contributed at the foreign level.However,the results of the current study may not be sufficient to prove that greater investment will yield benefits from foreign synergies.Originality/value:This paper uses game theory to assess innovation systems by computing the contribution of foreign actors to knowledge production at an area level.It proposes an indicator to this end.

A Value for Games Defined on Graphs

Given a graph g=( V,A ) , we define a space of subgraphs M with the binary operation of union and the unique decomposition property into blocks. This space allows us to discuss a notion of minimal subgraphs (minimal coalitions) that are of interest for the game. Additionally, a partition of the game is defined in terms of the gain of each block, and subsequently, a solution to the game is defined based on distributing to each player (node and edge) present in each block a payment proportional to their contribution to the coalition.

“the game theory”的汉译法

本文从源头、意义和文体风格三个方面讨论了"the game theory"的汉译问题,认为把其译作"博弈论"是不太妥当的,不仅文体风格很不对应,而且给人以高不可攀的印象,形成了吓阻他人的学术包装效果,倒不如译为"对策论"更符合原语风格,既言简意赅,又通俗易懂。

Game theory approach to optimal capital cost allocation in pollution control

This paper tries to integrate game theory, a very useful tool to resolve conflict phenomena, with optimal capital cost allocation issue in total emission control. First the necessity of allocating optimal capital costs fairly and reasonably among polluters in total emission control was analyzed. Then the possibility of applying game theory to the issue of the optimal capital cost allocation was expounded. Next the cooperative N person game model of the optimal capital cost allocation and its solution ways including method based on Shapley value, least core method, weak least core methods, proportional least core method, CGA method, MCRS method and so on were delineated. Finally through application of these methods it was concluded that to apply game theory in the optimal capital cost allocation issue is helpful to implement the total emission control planning schemes successfully, to control pollution effectively, and to ensure sustainable development.

A Novel Resource Allocation Method in Ultra-Dense Network Based on Noncooperation Game Theory

In ultra-dense networks(UDNs), large-scale deployment of femto base stations is an important technique for improving the network throughput and quality of service(QoS). In this paper, a multidimensional resource allocation algorithm based on noncooperation game theory is proposed to manage the resource allocation in UDNs, including transmission point association, user channels, and power. The algorithm derives a multidimensional resource optimization model and converts into a noncooperation game model according to the analysis of transmission point association user channel and power allocation. The algorithm includes two phases: transmission point association, and channel and power allocation. Then, feasible domain and discrete variables relaxation approaches are introduced to derive an approximate optimal multidimensional resource allocation solution with low complexity. Simulation results show that this method has some advantages in suppressing interference and improves the overall system throughput, while ensuring the QoS of femtocell users.

Fuzzy mathematics and game theory based D2D multicast network construction

Device to device(D2 D) multi-hop communication in multicast networks solves the contradiction between high speed requirements and limited bandwidth in regional data sharing communication services. However, most networking models demand a large control overhead in eNodeB. Moreover, the topology should be calculated again due to the mobility of terminals, which causes the long delay. In this work, we model multicast network construction in D2 D communication through a fuzzy mathematics and game theory based algorithm. In resource allocation, we assume that user equipment(UE) can detect the available frequency and the fuzzy mathematics is introduced to describe an uncertain relationship between the resource and UE distributedly, which diminishes the time delay. For forming structure, a distributed myopic best response dynamics formation algorithm derived from a novel concept from the coalitional game theory is proposed, in which every UE can self-organize into stable structure without the control from eNodeB to improve its utilities in terms of rate and bit error rate(BER) while accounting for a link maintenance cost, and adapt this topology to environmental changes such as mobility while converging to a Nash equilibrium fast. Simulation results show that the proposed architecture converges to a tree network quickly and presents significant gains in terms of average rate utility reaching up to 50% compared to the star topology where all of the UE is directly connected to eNodeB.

Distributed power control algorithm based on game theory for wireless sensor networks

Energy saving is the most important issue in research and development for wireless sensor networks. A power control mechanism can reduce the power consumption of the whole network. Because the character of wireless sensor networks is restrictive energy, this paper proposes a distributed power control algorithm based on game theory for wireless sensor networks which objects of which are reducing power consumption and decreasing overhead and increasing network lifetime. The game theory and OPNET simulation shows that the power control algorithm converges to a Nash Equilibrium when decisions are updated according to a better response dynamic.