Mixed coordination mechanisms for scheduling games on hierarchical machines

In this paper, we study scheduling games under mixed coordination mechanisms on hierarchical machines. The two scheduling policies involved are ‐ and ‐, where ‐ (resp., ‐) policy sequences jobs in nondecreasing order of their hierarchies, and jobs of the same hierarchy in nonincreasing (resp., nondecreasing) order of their processing times. We first show the existence of a Nash equilibrium. Then we present the price of anarchy and the price of stability for the games with social costs of minimizing the makespan and maximizing the minimum machine load. All the bounds given in this paper are tight.     

会计信息失真现象的博弈分析

在对现实进行合理假定的基础上建立了会计信息失真现象的有限完美信息动态博弈模型，并利用后退归纳法找到了五个满足不同条件的纳什均衡解。通过对均衡条件的分析，找出了导致会计信息失真的治理结构原因和法律制度因素，并提出了建设性的解决措施。     

A constructive approach to sequential Nash equilibria

We present a Coq-formalized proof that all non-cooperative, sequential games have a Nash equilibrium point. Our proof methodology follows the style advocated by LCF-style theorem provers, i.e., it is based on inductive definitions and is computational in nature. The proof (i) uses simple computational means, only, (ii) basically is by construction, and (iii) reaches a constructively stronger conclusion than informal efforts. We believe the development is a first as far as formalized game theory goes.     

证券市场中庄家与散户间的确定性微分博弈

基于确定性微分博弈理论,建立了一种庄家与散户间的连续时间的博弈模型.首先将所有散户作为一个整体与庄家进行博弈,以博弈双方持股率的动态关系作为动态系统方程,并以此构建了一个确定性微分博弈模型; 然后运用开环纳什均衡和反馈纳什均衡分别求解出满足共态函数的常微分方程组和满足价值函数的Issacs - Bellman偏微分方程,以此得到庄家与散户博弈的开环纳什均衡策略和反馈纳什均衡策略.该结果可为金融监管部门监管证券市场和证券市场投资者买卖股票提供参考.     

基于Q学习的DDoS攻防博弈模型研究

新形势下的DDoS攻防博弈过程和以往不同,因此利用现有的方法无法有效地评估量化攻防双方的收益以及动态调整博弈策略以实现收益最大化。针对这一问题,设计了一种基于Q学习的DDoS攻防博弈模型,并在此基础上提出了模型算法。首先,通过网络熵评估量化方法计算攻防双方收益;其次,利用矩阵博弈研究单个DDoS攻击阶段的攻防博弈过程;最后,将Q学习引入博弈过程,提出了模型算法,用以根据学习效果动态调整攻防策略从而实现收益最大化。实验结果表明,采用模型算法的防御方能够获得更高的收益,从而证明了算法的可用性和有效性。     

基于博弈论的多机器人任务分配算法

为了寻找一种合理有效的多机器人任务分配算法,基于多机器人协作救火任务环境,以博弈论纳什均衡为基础,研究多机器人的任务分配问题。根据任务模型特点和纳什均衡的主要特征提出了一种基于博弈论的任务分配算法。博弈的效用函数同时考虑了距离、火势和燃烧时间等因素,机器人根据此效用函数选择行为策略,促使机器人尽快扑灭惩罚值较大的火灾而获得较大的奖励值。利用任务总收益函数值的大小评价算法的优劣性。收益函数与火势、燃烧时间和机器人扑灭火灾数有关,这切合实际救火模型。实验结果证明了该任务分配算法的有效性。     

基于纳什均衡的网格资源管理模型

提出了一种基于纳什均衡的网格资源分配模型.该模型中,多个资源是合作博弈参与者,将任务在资源上的等待时间转化为一个极大极小问题,通过数学模型求得最优解,并据此提出了任务的分配算法.分析证明该算法可以实现合作博弈唯一的纳什均衡.     

Multi-channel power allocation based on market competitive equilibrium in cognitive radio networks

Cognitive radios such as intelligent phones and Bluetooth devices have been considered essential goods in next-generation communication systems.Such devices will have to share the same frequency band owing to the limited bandwidth resource.To improve spectrum efciency,we formulate multi-channel power allocation as a market competitive equilibrium(CE)problem,and prove that its solution exists and is unique under the condition of weak interference.We then propose two distributed power allocation algorithms achieving CE,namely the fast convergent power allocation algorithm(FCPAA)and the social-fairness-aware FCPAA(SFAF).Theoretical analysis and simulation results demonstrate that the proposed algorithms lead to better system performance in terms of the guaranteed interference temperature constraint using the price mechanism instead of a strategy based on the Nash equilibrium.Moreover,it is shown that the FCPAA maximizes total utility,and converges more quickly than the method addressed in prior research with the help of improved round-robin rules.However,the FCPAA cannot ensure the same social fairness among secondary users as the SFAF scheme in both the non-fading channel and Rayleigh fading channel;the SFAF balances the individual utility by adjusting each user’s budget at the expense of a small quantity of system total throughput.     

基于博弈论的无线传感器网络保密容量优化研究

由于无线传感器网络传输数据时的广播性,很容易受到窃听。从信息论的角度,保证节点信道的保密容量,即可实现数据的安全传输。而在无线传感器网络中,节点与节点之间会产生相互干扰,从而影响信道各自的保密容量。使用非合作博弈求解无线传感器网络在节点发射功率受限、节点之间存在互相干扰的情况下选取各自的功率,达到纳什均衡,以获取最佳的收益。仿真结果表明,节点选取纳什均衡时的功率,可获得较好的保密容量,同时提高节点的可持续性。     

Solving linear generalized Nash equilibrium problems numerically

This paper considers the numerical solution of linear generalized Nash equilibrium problems (LGNEPs). Since many methods for nonlinear problems require the nonsingularity of some second-order derivative, standard convergence conditions are not satisfied in our linear case. We provide new convergence criteria for a potential reduction algorithm (PRA) that allow its application to LGNEPs. Furthermore, we discuss a projected subgradient method (PSM) and a penalty method that exploit some known Nikaido–Isoda function-based constrained and unconstrained optimization reformulations of the LGNEP. Moreover, it is shown that normalized Nash equilibria of an LGNEP can be obtained by solving a single linear program. All proposed algorithms are tested on randomly generated instances of economic market models that are introduced and analysed in this paper and that lead to LGNEPs with shared and with non-shared constraints. It is shown that these problems have some favourable properties that can be exploited to obtain their solutions. With the PRA and in particular with the PSM we are able to compute solutions with satisfying precision even for problems with up 10,000 variables.