随机因素干扰下协同创新系统知识共享策略研究

本文考虑随机因素干扰的情形下,运用HJB方程和动态规划方法分别求解Nash非合作博弈和协同创新博弈模型下大学与企业的知识共享策略。结果表明：(1)两种博弈情形下,知识共享的成本越高,共享的知识量越少,知识共享边际收益越高;(2)协同创新博弈模式下的知识共享量、系统总收益均高于Nash非合作博弈,更易于达到Pareto最优,即推动产学研协同创新有助于提升系统总收益;(3)在合作情形下,大学与企业的决策目标定位于整体收益最大化,使得双方在知识共享努力程度与整体收益情况均优于Nash非合作博弈,在对知识共享行为有效协调下,合作策略是大学与企业构建协同创新系统,促进系统内知识共享的最优选择。     

Mean field stochastic linear quadratic games for continuum-parameterized multi-agent systems

In this paper, we consider a stochastic linear quadratic mean field game for the continuum-parameterized multi-agent systems with multiplicative noise. Based on the Nash certain equivalence principle, we obtain a series of decentralized control laws. Then, Dynkin’s formula and comparison principle are employed to prove the boundedness of the state of the closed loop system in the mean square sense. Finally, we show that the set of decentralized controls has an ?-Nash equilibrium property.     

企业员工隐性知识外部化过程中的博弈分析

现代企业需要构建企业内部知识库的一个基础条件是要将员工的隐性知识外部化,这一过程中存在着公共物品由私人提供的囚徒困境问题。本文就这一问题建立了一个完全信息静态博弈模型,并讨论了风险规避决策参与者的四种纳什均衡解。进一步的,从员工个人角度探讨了纳什均衡下的隐性知识供给,从企业整体利益的角度讨论了帕累托最优的隐性知识供给,并分析比较了这两种状态下的个人贡献量关系。最后,根据上述分析,给出了一系列相应的管理启示。     

Analysis of distributed consensus protocols with multi-equilibria under time-delays

In this paper, we propose a novel method for addressing the multi-equilibria consensus problem for a network of n agents with dynamics evolving in discrete-time. In this method, we introduce, for the first time in the literature, two concepts called primary and secondary layer subgraphs. Then, we present our main results on directed graphs such that multiple consensus equilibria states are achieved, thereby extending the existing single-state consensus convergence results in the literature. Furthermore, we propose an algorithm to determine the number of equilibria for any given directed graph automatically by a computer program. We also analyze the convergence properties of multi-equilibria consensus in directed networks with time-delays under the assumption that all delays are bounded. We show that introducing communication time-delays does not affect the number of equilibria of the given network. Finally, we verify our theoretical results via numerical examples.     

Resilient strategy design for cyber-physical system under active eavesdropping attack

We consider a remote state estimation process under an active eavesdropper for cyber-physical system. A smart sensor transmits its local state estimates to a remote estimator over an unreliable network, which is eavesdropped by an adversary. The intelligent adversary can work in passive eavesdropping mode and active jamming mode. An active jamming mode enables the adversary to interfere the data transmission from sensor to estimator, and meanwhile improve the data reception of itself. To protect the transmission data from being wiretapped, the sensor with two antennas injects noise to the eavesdropping link with different power levels. Aiming at minimizing the estimation error covariance and power cost of themselves while maximizing the estimation error covariance of their opponents, a two-player nonzero-sum game is constructed for sensor and active eavesdropper. For an open-loop case, the mixed Nash equilibrium is obtained by solving an one-stage nonzero-sum game. For a long term consideration, a Markov stochastic game is introduced and a Nash Q-learning method is given to find the Nash equilibrium strategies for two players. Numerical results are provided to show the effectiveness of our theoretical conclusions.     

我国实施《劳动合同法》的博弈分析

在理性经济人假设下,建立劳动监察部门、劳工和企业的三方博弈模型,通过求解纳什均衡,结合各参数变量的政策含义,认为政府要进一步完善相关法律制度,加大赔偿额;各级政府要进一步加大劳动保障监察队伍的建设;劳动监察部门要进一步加强对用人单位劳动用工的指导和管理．严格依法办事,要加大处罚力度;劳动监察部门要不断积累监管经验,要采用先进的技术手段,以期降低监察成本;劳工应主动深入学习《劳动合同法》有关知识,加强法律维权意识。     

基于互动博弈的组织不同层级间激励协同机制设计

基于双重委托代理理论,以组织效益最大化为目标,构建了静态纳什均衡模型及动态序贯博弈模型,分析了既定情境下的高低层级群体互动博弈过程及其对策。模型的演绎机制揭示:在提供顶层设计前提下,短期内,组织对高层级群体绩效与奖惩紧密挂钩,将提升组织监管有效性;而组织越过高层级群体对低层级群体加重惩罚是无效的;长期看,无论对高层级群体还是低层级群体,组织必须配有一定程度的负激励,负激励是更为有效和持久的激励。     

政府-企业间质量监管博弈分析

通过对博弈模型中纯战略Nash均衡、占优战略Nash均衡和混合战略Nash均衡的分析,揭示了政府质量监管部门与生产企业建立质量管理体系监管博弈的内部运行机理。为实践中政府如何有效进行质量监管以及生产企业如何有效提高产品质量水平提出了建议,并为该监管博弈模型在实践中的具体应用指明了方向。     

网商生态系统交易主体间信用行为博弈研究

随着电子商务的迅猛发展,信用问题越来越被人们所重视。以混合战略博弈理论为依据,研究网商生态系统中交易主体间的信用行为博弈问题,探究信用问题产生的原因,并根据建立的博弈模型分析得到混合纳什均衡解。通过对混合纳什均衡解分析得到,对采取欺诈经营的网商的惩罚力度越大,网商通过欺诈进行获利的空间越小,选择诚信经营的网商比例就越大,这对于网商生态系统交易环境中的信用问题的改善有着重要的作用。     

节水灌溉设施技术创新激励的博弈分析

用博弈论的方法从双寡头市场的角度探讨了供水企业在进行节水灌溉设施技术创新活动中的合作与不合作行为,分析了在这样的市场结构中节水灌溉设施技术创新的微观博弈机制。通过比较不同假设条件下供水企业进行节水灌溉设施技术创新的预期利润,得出了不同情形下的纳什均衡。     

标准采纳过程中的相关方博弈分析与政策设计

分析在不同的市场结构下,不同属性的标准在被采纳过程中,企业与企业之间以及企业与政府之间的博弈行为,并通过相应的博弈模型试图找到不同状况下博弈的纳什均衡解,为标准被顺利采纳提供针对性政策参考。     

Optimal DoS attack scheduling for multi-sensor remote state estimation over interference channels

In this paper, we investigate the optimal denial-of-service attack scheduling problems in a multi-sensor case over interference channels. Multiple attackers aim to degrade the performance of remote state estimation under attackers’ energy constraints. The attack decision of one attacker may be affected by the others while all attackers find their own optimal strategies to degrade estimation performance. Consequently, the Markov decision process and Markov cooperative game in two different information scenarios are formulated to study the optimal attack strategies for multiple attackers. Because of the complex computations of the high-dimensional Markov decision process (Markov cooperative game) as well as the limited information for attackers, we propose a value iteration adaptive dynamic programming method to approximate the optimal solution. Moreover, the structural properties of the optimal solution are analyzed. In the Markov cooperative game, the optimal joint attack strategy which admits a Nash equilibrium is studied. Several numerical simulations are provided to illustrate the feasibility and effectiveness of the main results.     

Non-centralized control for flow-based distribution networks: A game-theoretical insight

This paper solves a data-driven control problem for a flow-based distribution network with two objectives: a resource allocation and a fair distribution of costs. These objectives represent both cooperation and competition directions. It is proposed a solution that combines either a centralized or distributed cooperative game approach using the Shapley value to determine a proper partitioning of the system and a fair communication cost distribution. On the other hand, a decentralized non-cooperative game approach computing the Nash equilibrium is used to achieve the control objective of the resource allocation under a non-complete information topology. Furthermore, an invariant-set property is presented and the closed-loop system stability is analyzed for the non-cooperative game approach. Another contribution regarding the cooperative game approach is an alternative way to compute the Shapley value for the proposed specific characteristic function. Unlike the classical cooperative-games approach, which has a limited application due to the combinatorial explosion issues, the alternative method allows calculating the Shapley value in polynomial time and hence can be applied to large-scale problems.     

C2B平台竞价回收博弈研究

C2B网络回收平台的竞价回收机制决定了众多回收商之间存在着激烈的竞争,每个网络回收商的回收量不仅取决于自己的回收价格,还取决于其他网络回收商的回收价格。期望利润最大化是每个网络回收商的竞争目标。运用最优化理论与非合作博弈理论证明C2B平台竞价回收博弈存在唯一的Nash均衡。在完全竞争与串谋合作的不同情况中发现,完全竞争情况下Nash均衡的网络回收价格高于串谋合作下的网络回收价格,并通过数值仿真进行验证。     

政府奖惩下不同权力结构闭环供应链的决策研究

针对由单个制造商和零售商所组成的闭环供应链,考虑政府奖惩零售商回收废旧品活动的情况,基于博弈论方法,分别构建了集中式、制造商和零售商存在Nash均衡博弈、制造商领导的Stackelberg博弈、零售商领导的Stackelberg博弈等模式下的闭环供应链决策模型,进而探讨了政府设置的奖惩力度和废旧品最低回收率分别对四种模式闭环供应链的决策、系统各成员及总利润的影响问题。研究表明:产品的单位批发价格和废旧品的回收率均会随着政府奖惩力度的增加而增加;产品的单位销售价格会随着政府奖惩力度的增加而减少;制造商的利润会随着政府奖惩力度的增加而增加;零售商的利润和集中式决策闭环供应链的总利润同时受到政府奖惩力度和废旧品最低回收率的影响。因此,政府通过设置合理的奖惩力度和废旧品最低回收率,可使得闭环供应链和系统各成员在获得满意经济效益的同时实现生态效益。     

基于博弈决策模型的科技企业孵化器与风险投资的融合机制研究

科技企业孵化器能够为初创企业的发展提供管理、咨询等服务,风险投资机构凭借资金优势能够满足科技企业的研发资金需求。通过建立完全信息及信息不对称条件下的博弈决策模型,寻找双方博弈的均衡点。模型结果表明：在完全信息条件下,（融合,融合）是双方博弈的唯一纳什均衡,即合作是一种必然趋势。而在信息不对称条件下,通过建立品牌型孵化器能够部分解决信息不对称问题,减少信息搜寻成本,并同时提高合作双方的收益。     

基于秩依效用理论的网络舆情传播博弈模型

针对当前研究的网络舆情传播问题,本文依据现有网络舆情的传播实例,归纳总结出网络舆情传播的新特征。探讨以“网络大V”为代表的舆情传播者和以政府部门为代表的网络舆情管控者之间的博弈模型,运用秩依效用理论,考虑博弈双方带有心理偏好时的博弈状态,依据不同的实际情况分析其Nash均衡。最后用实例对模型进行验证。     

不完全信息下发电企业竞价的贝叶斯博弈模型研究

电力工业改革在发电领域引入竞争,目的在于提高电力生产效率,促进电力工业的发展.发电企业竞价是一个不完全信息下的静态博弈问题,在深入分析不完全信息市场环境下发电企业竞价过程的基础上,运用博弈论中的暗标拍卖原理构建发电企业竞价的暗标拍卖贝叶斯博弈模型,并通过求解贝叶斯纳什均衡得出发电企业的最优竞价模型,从而为发电企业建立有效的竞价策略提供决策参考.     

基于Nash均衡的专利战略分析

运用博弈论的思维方式和分析工具,研究在既定专利制度下的专利战略,通过专利战略的博弈分析,提出专利战略的组织行为策略应基于Nash均衡的理念;现代经济条件下,无论是技术创新、产品升级还是国际贸易,制定专利战略的均衡策略是组织行为的重要一环.     

Optimized control for human-multi-robot collaborative manipulation via multi-player Q-learning

In this paper, optimized interaction control is investigated for human-multi-robot collaboration control problems, which cannot be described by the traditional impedance controller. To realize global optimized interaction performance, the multi-player non-zero sum game theory is employed to obtain the optimized interaction control of each robot agent. Regarding the game strategies, Nash equilibrium strategy is utilized in this paper. In human-multi-robot collaboration problems, the dynamics parameters of the human arm and the manipulated object are usually unknown. To obviate the dependence on these parameters, the multi-player Q-learning method is employed. Moreover, for the human-multi-robot collaboration problem, the optimized solution is difficult to resolve due to the existence of the desired reference position. A multi-player Nash Q-learning algorithm considering the desired reference position is proposed to deal with the problem. The validity of the proposed method is verified through simulation studies.