Dynamical Systems Theory Compared to Game Theory: The Case of the Salamis’s Battle

In this paper, we present an innovative non–linear, discrete, dynamical system trying to model the historic battle of Salamis between Greeks and Persians. September 2020 marks the anniversary of the 2500 years that have passed since this famous naval battle which took place in late September 480 B.C. The suggested model describes very well the most effective strategic behavior between two participants during a battle (or in a war). Moreover, we compare the results of the Dynamical Systems analysis to Game Theory, considering this conflict as a “war game”.

Optimal Active Defence Using Dynamic Multi-Stage Signalling Game

Nowadays, security defence of network uses the game theory, which mostly applies complete information game model or even the static game model. To get closer to the actual network and defend actively, we propose a network attack-defence game model by using signalling game, which is modelled in the way of dynamic and incomplete information. We improve the traditional attack-defence strategies quantization method to meet the needs of the network signalling game model. Moreover, we give the calculation of the game equilibrium and analyse the optimal defence scheme. Finally, we analyse and verify effectiveness of the model and method through a simulation experiment.

A Dynamic Distributed Spectrum Allocation Mechanism Based on Game Model in Fog Radio Access Networks

With the explosive growth of highspeed wireless data demand and the number of mobile devices, fog radio access networks(F-RAN) with multi-layer network structure becomes a hot topic in recent research. Meanwhile, due to the rapid growth of mobile communication traffic, high cost and the scarcity of wireless resources, it is especially important to develop an efficient radio resource management mechanism. In this paper, we focus on the shortcomings of resource waste, and we consider the actual situation of base station dynamic coverage and user requirements. We propose a spectrum pricing and allocation scheme based on Stackelberg game model under F-RAN framework, realizing the allocation of resource on demand. This scheme studies the double game between the users and the operators, as well as between the traditional operators and the virtual operators, maximizing the profits of the operators. At the same time, spectrum reuse technology is adopted to improve the utilization of network resource. By analyzing the simulation results, it is verified that our proposed scheme can not only avoid resource waste, but also effectively improve the operator's revenue efficiency and overall network resource utilization.

Exploring the dynamic evolutionary mechanism of game model on the protection of traditional villages

With the rapid improvement of urbanization and industrialization in countries around the world,how to effectively solve the rapid demise of traditional villages is a social dilemma faced by all countries,which is why a series of relevant protection regulations have been promulgated in different historical periods.However,the formulation of relevant policies is still not scientific,universal,and long-term.In this study,we constructed an evolutionary game model of local governments and residents based on the evolutionary game theory(EGT),which is used to explore the evolutionary stability strategy(ESS)and stability conditions of stakeholders under the premise of mutual influence and restriction.Besides,the study also included the analysis about the impacts of different influence factors on the evolution tendency of the game model.At the same time,numerical simulation examples were used to verify the theoretical results and three crucial conclusions have been drawn.Firstly,the strategic evolution of stakeholders is a dynamic process of continuous adjustment and optimization,and its results and speed show consistent interdependence.Secondly,the decision-making of stakeholders mainly depends on the basic cost,and the high cost of investment is not conducive to the protection of traditional villages.Thirdly,the dynamic evolutionary mechanism composed of different influence factors will have an impact on the direction and speed of decision-making of stakeholders,which provides the basis for them to effectively restrict the decision-making of each other.This study eliminates the weaknesses of existing research approaches and provides scientific and novel ideas for the protection of traditional villages,which can contribute to the formulation and improvement of the relevant laws and regulations.

Study on group air to ground attack-defends hierarchical dynamic decision-making

As to oppositional, multi-objective and hierarchical characteristic of air formation to ground attackdefends campaign, and using dynamic space state model of military campaign, this article establishes a principal and subordinate hierarchical interactive decision-making way, the Nash-Stackelberg-Nash model, to solve the problems in military operation, and find out the associated best strategy in hierarchical dynamic decision-making. The simulating result indicate that when applying the model to air formation to ground attack-defends decision-making system, it can solve the problems of two hierarchies, dynamic oppositional decision-making favorably, and reach preferable effect in battle. It proves that the model can provide an effective way for analyzing a battle,

Bioinformatic Game Theory and Its Application to Biological Affinity Networks

The exact evolutionary history of any set of biological taxa is unknown, and all phylogenetic reconstructions are approximations. The problem becomes harder when one must consider a mix of vertical and lateral phylogenetic signals. In this paper we propose a game theoretic approach to constructing biological networks. The key hypothesis is that evolution is driven by distinct mechanisms that seek to maximize two competing objectives, taxonomic conservation and diversity. One branch of the mathematical theory of games is brought to bear. It translates this evolutionary game hypothesis into a mathematical model in two-player zero-sum games, with the zero-sum assumption conforming to one of the fundamental constraints in nature in mass and energy conservation. We demonstrate why and how a mechanistic and localized adaptation to seek out greater information for conservation and diversity may always lead to a global Nash equilibrium in phylogenetic affinity. Our game theoretic method, referred to as bioinformatic game theory, is used to construct network clusters. As an example, we applied this method to clustering of a multidomain protein family. The protein clusters identified were consistent with known protein subfamilies, indicating that this game-theoretic approach provides a new framework in biological sequence analysis, especially in studying gene-genome and domain-protein relationships.

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.

Agent-Based Simulation for Analyzing Network Formation with Learning Dynamics

In game theoretic context, it is assumed that the decision maker has the extraordinary skills of reasoning and calculation. This assumption is called ＂perfect rationality＂. A player with perfect rationality can solve complex problems without making mistakes. However, recently, many studies have restricted this rationality or the structure of game. These restrictions are called ＂bounded rationality.＂ The authors also focus on bounded rationality, but with learning dynamics and complex networks. A complex network covers a wide area. Currently, a wide range of studies have not only investigated network formation and the characteristics of a formed network, but also analyzed situations where a network is already provided. In addition, in an analysis using game theory, a prisoners＇ dilemma type game was used to investigate how a change in the network structure would affect the players＇ relationships Therefore, our model employs decision makers with learning dynamics and describes the interaction of decision makers as a network. The purpose of this study is to examine the behavior of the decision maker with learning dynamics and the formation of networks by the interaction of decision makers through an agent-based simulation.

动力电池回收商与地方政府间行为策略演化博弈研究

新能源汽车的使用量急剧增长,对于到达使用寿命的电池若不及时回收拆解处理,便会造成环境的污染与公共资源的占用。基于此,文章构建动力电池回收商与地方政府之间的演化博弈模型,结合系统动力学理论,构建SD-演化博弈模型,借助Vensim仿真分析,验证均衡点的稳定性,探讨如何实现废旧动力电池高效回收。研究表明,动力电池回收商与地方政府之间存在长期博弈,只有在地方政府采取具有惩罚措施的动态监管策略时,系统才能达到均衡稳定状态,反之在静态监管策略下系统不稳定。

Dynamically Optimized Sensor Deployment Based on Game Theory

Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors, considering exploration cost, target detection value, exploration ability and other factors. Then, aiming at the unfavorable environment, e.g., obstacles and enemy interference, the authors design a method to maintain the connectivity of sensor network, under the conditions of effective detection of the targets. Simulation result shows that the proposed deployment strategy can achieve the dynamic optimization deployment under complex conditions.

Robust Resource Management Control for CO2 Emission and Reduction of Greenhouse Effect: Stochastic Game Approach

With the increasingly severe global warming, investments in clean technology, reforestation and political action have been studied to reduce CO2 emission. In this study, a nonlinear stochastic model is proposed to describe the dynamics of CO2 emission with control inputs: clean technology, reforestation and carbon tax, under stochastic uncertainties. For the efficient resources management, a robust tracking control is designed to force resources tracking a desired reference output. The worst-case effect of stochastic parametric fluctuations, external disturbances and uncertain initial conditions on the tracking performance is considered and minimized from the dynamic game theory perspective. This stochastic game problem, in which one player (stochastic uncertainty) maximizes the tracking error and another player (control input) minimizes the tracking error, could be equivalent to a robust minimax tracking problem. To avoid solving the HJI, a fuzzy model is proposed to approximate the nonlinear CO2 emission model. Then the nonlinear stochastic game problem could be easily solved by fuzzy stochastic game approach via LMI technique.

电厂和政府行为策略演化博弈与仿真研究——基于农林生物质与煤耦合发电产业发展视角

“双碳”背景下,农林生物质与煤耦合发电成为煤电转型的新路径。政府如何制定有效的补贴和监管政策,以及电厂如何进行正确的策略选择是促进农林生物质与煤耦合发电产业发展的关键。通过构建政府和电厂的混合策略博弈模型,分析双方的博弈关系和行为策略动态演变过程,并运用系统动力学进行建模仿真,进一步探析关键因素对政府和电厂行为策略的影响路径。结果表明:政府和电厂的行为策略在较长时期里无法趋于稳定,而是在相互影响中波动变化。政府制定补贴和监管策略、提高耦合发电收益、降低耦合发电成本,都将促进更多电厂选择农林生物质与煤耦合发电,从而进一步促进产业发展。

无信控交叉口网联车辆动态碰撞风险检测与预警策略

针对无信控交叉口碰撞事故多发的问题,面向网联人工驾驶环境,研究提出了一种基于车车通信的无信控交叉口网联车辆碰撞动态风险检测与预警策略。在构建典型无信控交叉口场景的基础上,设计了一种基于多项式拟合的车辆轨迹模型;融合利用碰撞到达时间和风险暴露时间作为风险检测指标,构建了一种圆形-双圆车辆模型实现车辆碰撞风险检测;综合考虑了驾驶人异质性及其交互行为,提出一种基于博弈论和遗传算法的两级碰撞预警策略;基于SUMO搭建仿真环境对预警策略的有效性及适用性进行测试分析。结果表明,所提策略能够准确识别出所有的碰撞事件并触发预警,预警成功率达到100%;在不同比例驾驶人组成测试工况下,所提预警策略均能显著降低碰撞率和平均碰撞动能。

数字经济下个人信息使用和保护平衡的三方演化博弈分析

构建以政府(G)、个人信息所有者(P)和个人信息获取/使用者(C)三方为主体的动态博弈模型,分析其演化过程。通过对以政府为驱动的三方博弈的演化路径分析,从三方的角度分别提出对策建议。研究结果表明,政府实施严格监管可以提升自身形象和管理效率。政府应主要采用惩罚手段,使个人信息获取/使用者在违规策略下产生较高的成本,从而促使他们采取规范获取和使用的策略。对于个人信息所有者而言,政府及时发现违规行为并严格处理,同时积极宣传个人信息维护,可激励他们采取积极提供个人信息的策略。在这种情况下,三方可实现帕累托最优的策略均衡,并实现在数字经济下个人信息使用和保护的平衡。

基于博弈优化算法的综合电热协同网络中热泵系统的智能化运行动态特性研究

为提升区域能源综合利用和促进低碳发展,针对园区等典型区域构建综合能源系统,包含光伏、风电等新能源发电系统及地源/空气源热泵及冷水机组等园区冷热能源供给系统与储能设备等。利用TRNSYS软件对北京地区6种气候条件下系统的动态特性进行仿真研究,采用博弈方法进行智能化运行并与逻辑控制方法对比。结果表明逻辑控制方法下可满足负荷需求但无法实现热泵机组始终高效运行与储能装置的充放平衡。进行博弈优化后的综合能源系统不仅能通过电热协同实现能量柔性调度分配,还能节约热泵机组整体能耗,并实现区域能源经济效益的目标。为综合电热协同网络中热泵系统的智能化运行技术提供了重要的理论基础。

基于博弈论与强化学习的多智能体路径规划算法

针对平面上多个智能体构成的路径规划求解算法普遍存在的速度慢效率低等问题进行研究,将多智能体路径规划问题归结为非零和随机博弈,使用多智能体强化学习算法赢或快速学习-策略爬山(win or learn fast-policy hill-climbing,WoLF-PHC)得到纳什均衡策略,为各智能体做出无冲突的最优路径决策,提出能够快速自适应的WoLF-PHC(fast adaptive WoLF-PHC,FA-WoLF-PHC)算法,通过构建目标函数,使用梯度下降对学习率进行自适应更新.在猜硬币和自定义收益矩阵2个博弈场景中使用FA-WoLF-PHC,并与策略爬山(policy hill-climbing,PHC)算法和Wolf-PHC算法进行比较.结果表明,FA-WoLF-PHC算法的学习速度较WoLF-PHC算法有所提升,并有效减小了WoLF-PHC算法和PHC算法在学习过程中出现的振荡现象.在多智能体路径规划问题中,FA-WoLF-PHC算法的学习速度比WoLF-PHC算法提高了16.01%.将路径规划问题的环境栅格地图扩大为6×6,智能体数量增加为3个时,FA-WoLF-PHC、WoLF-PSP和多头绒泡菌-人工势场Sarsa(physarum polycephalum-artificial potential state-action-reward-state-action,PP-AP Sarsa)算法在10次实验中学习到最终策略需要的平均时间分别为16.30、20.59和17.72 s.在多智能体路径规划问题中,FA-WoLF-PHC算法能够得到各智能体的纳什均衡策略,学习速度较WoLF-PSP和PP-AP Sarsa算法有显著提高.FA-WoLF-PHC算法在常见的博弈场景中能够快速获得纳什策略,在多智能体路径规划问题中可为多个智能体生成无冲突的最优路径,并且在学习速度等方面较其他算法有显著提高.

基于博弈论的风光氢能源系统容量优化

目前风光氢能源系统中的能源利用率较低,传统的容量优化根据电解槽固定制氢效率进行建模且仅考虑了系统中存在1个运营商的情况,没有考虑到不同运营商之间的竞争与合作、电解槽工作时制氢效率的变化情况和待机启动成本,导致容量优化结果不理想。为解决上述问题,首先将有机朗肯循环与燃料电池相结合,用于回收余热;通过分析电解槽制氢效率的影响因素构建动态制氢效率数学模型,并引入二进制变量,用于描述电解槽的待机启动成本;采用基于博弈论的容量优化方法,以全年收益最大为目标采用麻雀搜索算法求解博弈的纳什均衡。仿真结果表明:添加余热利用后能源系统的总收益增加了4%;考虑动态制氢效率和待机启动成本后的质子交换膜电解槽容量减少了26.1%;氢储发电站的收益减少了55.3%;风光氢三方组成大联盟时联盟最稳定,且收益最大、容量配置最合理。

高速列车转向架动力学设计指标分解冲突博弈协调方法

为解决传统的高速列车动力学性能多目标优化方法缺乏考虑设计指标之间的相互作用且主观性强、可解释性差、效率低等问题,提出一种基于博弈论的高速列车转向架动力学设计指标分解协调方法。首先,基于灵敏度分析结果建立7个动力学性能设计指标与13个转向架设计参数之间的分解映射,分析了动力学设计指标分解中的冲突关系并提取了冲突要素;然后,将互相冲突的动力学设计指标的分解协调转化为一个博弈过程,确定博弈中的决策结构,构建了博弈主体的效用函数并建立冲突性能的主从博弈协调模型;其次,设计一种基于遗传算法和二次规划联合的Nash均衡求解算法,以Nash均衡作为设计指标分解结果;最后,以高速列车稳定性和曲线通过性能的博弈协调为例验证了博弈协调模型与求解方法的可行性和有效性。研究结果表明:该方法不依赖于人工设置即可快速得到与高速列车实际设计参数相近的结果,并且在该结果下临界速度、脱轨系数、轮重减载率等指标均满足要求。引入博弈论进行动力学性能多目标优化不仅可获得直线稳定性和曲线通过安全性相对均衡的动力学性能,还减少了设计迭代次数,提高了设计效率,且所求取的是数学意义上的Nash均衡,极大地降低了主观性。研究成果可为高速列车悬挂系统设计和动力学性能协调优化提供参考。

基于演化博弈理论的体育旅游发展模型研究

为全面推进体育与旅游深度融合发展,扩大体育旅游市场整体供给,促进体育旅游产业结构持续优化,激活体育旅游产业健康消费新动能,本文基于演化博弈理论,构建政府部门、企业与消费者核心利益主体之间的三方动态博弈模型,通过演化博弈混合策略矩阵建立复制动态方程对均衡点进行稳定性分析,利用数值仿真模拟探究内外部变量对模型结果演化路径的影响。研究认为,政府进行扶持监管、体育旅游企业供给高质量服务与产品及消费者参与体验消费有助于体育旅游发展模型向理想状态演化。

A Dynamic Bargaining Game with Externalities

Negotiation and bargaining are the common means of solving economic and political prob-lems.In these problems,players often form coalitions for activities.In the environment with externalities,the worth of a coalition depends not only on itself,but also on the coalition structure of other players.This paper studies dynamic bargaining games with externalities and analyzes the effect of externality on the payoffs of players.Based on the Rubinstein's two-person alternating-offer bargaining game,the authors study a two-person dynamic game with externalities and extend it to the case of n players.The research shows that externality affects the results of this bargaining game,and coalition structures affect the payoffs of players.Players in this bargaining game can always form grand coali- tion if an allocation is effective,but some coalition structures may not be formed under some certain conditions.