基于团队合作博弈的自动协商模型

企业在联盟方式下运营,有利于提高供应链的协同运作能力.为进一步发挥其在与需求方进行协商时的群体协作报价优势,将协商团队、合作博弈和自动协商理论进行融合,以供应链典型生产和物流企业结成产运纵向联盟为背景,提出基于团队合作博弈的纵向联盟成员协同报价自动协商模型,并基于Shapley值对报价方案按照成员报价的贡献对联盟收益进行分配.研究表明,产运纵向联盟集中定价的情况下,团队成员采取完全合作的方式进行报价,可以在团队收益最大化的同时,实现自身收益最佳,且有利于维持激烈市场竞争环境下产运纵向联盟的稳定合作关系.     

基于模糊双合作博弈的收益分配模型

产品服务化供应链中,多个提供商向系统集成商提供产品和服务,获得了产品和服务收益.以提供商的双重收益分配为例,将提供商的服务质量作为参与程度,在双合作博弈的基础上,提出了模糊双合作博弈的收益分配模型,定义了 Aubin 核心和 crisp 核心,证明了凸模糊双合作博弈中,韦伯集与 crisp 核心相等且是 Aubin 核心的子集,并证明了提供商的服务质量提高时,参与的联盟越大,边际收益越大,从而保证最优分配方案的存在性和模糊双联盟结构的稳定性.     

研发联盟中合作溢出量与防范成本的关系研究

为研究研发联盟中合作溢出量与防范成本的关系,利用演化博弈理论建立了以机会主义者比例和发生合作溢出的互惠主义者比例为参数的演化博弈模型,分析了合作溢出量和防范成本之间的约束关系对战略联盟稳定性的影响。结果表明,合作溢出量与防范成本对联盟稳定性有着重要的影响。     

决策者失望规避下供应链协同产品创新微分对策研究

在企业与企业之间竞争日趋激烈的背景下,能否在多个企业紧密联系的供应链中通过收益分配高效赋能上下游企业成为决策者撬动协同优势、凝聚创新合力的关键.基于失望理论,运用微分博弈方法针对供应链协同产品创新动态改进与协调进行策略挖掘,将产品创新研发商誉和失望规避因素纳入决策行为之中,在此基础上提出产品创新补贴契约和收益配比调节机制,最后对计算结果进行仿真分析.研究发现:无论在哪种博弈情形下,失望规避系数越大,供应链成员目标利润越低;在一定条件下,创新研发及推广补贴契约的设计与实施,能有效提升受补贴方研发和推广水平、最优收益和供应链整体收益,而提供补贴方的研发努力水平保持不变;将收益分配比率约束在特定的范围内,可有效提高供应链系统的总体绩效,实现协同合作下帕累托最优.     

loss-averse测度的低碳闭环供应链最优减排研究

以低碳产品销售为不确定市场环境为背景,以Stackelberg博弈理论为方法,以loss-averse为工具,构建了双第三方回收低碳风险再制造闭环供应链模型,研究了制造商低碳减排能力、碳排放征税政策、销售商风险厌恶程度和废旧低碳产品回收市场竞争特性等因素的影响作用机理;揭示非合作博弈和协调运作下,订单数量、交易价格、低碳废旧产品回收价格等重要变量和节点企业收益、期望效用及整个链条收益的变化规律及隐含的经济意义与管理理念。设计正向渠道收益共享、逆向渠道风险共担契约,克服风险规避效应和双重边际效应的不利影响,实现低碳再制造闭环供应链的协调;通过数值仿真研究和灵敏度分析,验证了低碳风险再制造闭环供应链模型的有效性及实用性,为供应链上下游企业开展低碳减排合作提供了理论依据和政策建议。     

基于碳配额政策的两级低碳供应链博弈与优化

通过构建供应商和制造商两级低碳供应链Stackelberg博弈模型,得到博弈均衡解和收益共享契约参数,比较分散决策、集中决策和收益共享契约下实现低碳供应链的经济效应,并通过算例分析了是否合作减排以及合作减排对供应链成员产量、碳减排量、利润的影响和收益共享契约参数对供应链成员利润的影响,为供应链上下游企业开展碳减排合作提供了理论依据和政策建议.     

基于广义解的网络合作博弈收益分配模型

网络合作博弈主要研究如何将联盟收益分配给网络合作联盟的每个参与者.考虑到现实生活中很多联盟倾向于保留一部分合作收益用于再发展的情况,对网络合作博弈模型进行扩展,定义广义分配、广义核心和广义谈判集等解的概念,并证明当满足超可加性时,网络合作博弈的广义核心与其广义谈判集存在等价性质.因广义谈判集非空,进而刻画了网络合作博弈广义核心的非空性.算例分析结果表明了广义分配方案的存在性及合理性.     

二维平面网格上移动成本群体合作行为研究

从移动成本、收益期望与空间博弈的角度,探讨多主体系统的博弈策略演化与系统涌现特征之间的关系。利用空间演化博弈理论,构建了基于个体移动机制的拓扑结构时刻演变的空间演化博弈模型,分析了当主体具有不同的移动成本与收益期望时系统演化的稳定策略,通过分析稳定策略深入探讨系统中合作簇涌现的机理。仿真结果表明,提高移动成本能够最有效地促进系统合作率,同时中等水平的个体收益期望会进一步促进高移动成本的影响效果。     

基于广义解的双合作博弈收益分配模型

双合作博弈主要研究如何将联盟收益全部分配给双合作联盟的每个参与者. 考虑到不将全部的合作收益用于分配, 而是预留一部分合作收益用于投资扩大再生产或再分配的情况, 对双合作博弈模型进行扩展, 定义广义分配、广义核心和广义韦伯集等解的概念, 并证明广义核心总是包含在广义韦伯集中. 当双合作博弈满足超模性时, 广义核心总是等于广义韦伯集. 由于广义韦伯集是一个非空集合, 从而进一步证明了广义核心存在且非空.

     

精敏供应链Stackelberg博弈EOQ决策模型

在非合作和合作两种不同情景下,不考虑缺货损失条件,研究一个主导方供应商和两个跟从方制造商不同订货成本结构的两层精敏供应链上游段Stackelberg博弈的EOQ决策模型。引入供应商价格折扣策略,通过模型分析与求解,得出Stackelberg博弈下的EOQ决策均衡点,改善了精敏供应链上游段的整体运行效率,提高了供应链参与成员的各自收益。通过实例分析,验证了模型的可行性。     

GDISD团队信任的动态演化机理研究

结合异地分布式信息系统开发（GDISD）团队的特点与信任演进的相关研究构建GDISD团队的信任演进理论模型。从演化博弈论的角度,描述GDISD团队任意两成员间信任演进的机理,验证信任演进理论模型成立的前提。研究得出GDISD团队信任演进的理论模型,从基于计算的信任,快速信任,基于了解的信任演化到基于认同的信任。针对GDISD过程,分析了该理论模型信任发展各阶段信任的基础和对象。通过构建动态复制方程,得出系统均衡点,并采用雅可比矩阵验证其稳定性,结果显示当博弈双方采取高信任策略时,项目高效完成的奖金大于由于博弈中一方低信任策略造成的另一方的收益损失时,信任的演化都遵循低信任策略到高信任策略的演化路径。     

基于Matlab的生命游戏仿真设计

使用Matlab为开发工具,利用其提供的图形化用户界面,实现了生命游戏的可视化编程,并探讨了在几个简单初始状况下生命演变过程。研究表明:生命游戏的演变过程及其复杂,包括保持不变型、不断缩小型、移动型、消失型、周期变化型、固定不变型以及没有任何规律可循型等多种形态,同时对称的初始状态演变过程是对称分布的。     

Multi-objective design method based on evolution game and its application for suspension

Through research and bionics of biology survival mode, game players with competition, cooperation and self-adaptation capacity are introduced in the multi-objective design. The dynamic behavior and bounded rationality in game processes for players are considered according to Chinese saying “In success, commit oneself to the welfare of the society; in distress, maintain one’s own integrity”. An evolution rule, Poor-Competition-Rich-Cooperation (short for PCRC), is proposed. Then, the corresponding payoff functions of competition and cooperation behavior are established and a multi-objective design method based on evolution game is proposed. The calculation steps are as follows: 1) Taking the design objectives as different game players, and calculating factors of the design variables to objective and fuzzy clustering. The design variables are divided into multiple strategy subsets owned by each game player. 2) According to the evolution rule, each player determines its behavior and payoff function in this game round. 3) In their own strategy subsets, each game player takes their payoff as mono-objective for optimization. It gives the best strategy upon other players. And so the best strategies of all players conform the group strategy in this round. The final equilibrium solution is obtained through multi-round game based on convergence criterion. The validity and reliability of this method are shown by the results of an example of a tri-objective optimization design of passive suspension parameters.     

悖论对策中的计算机仿真研究

社会悖论是在社会生活中大量存在的多人非合作博弈问题，但用经典对策论难以得到有意义的结果。该文应用计算机试验的方法，对一类多人重复悖论对策过程进行仿真研究，以进化对策的观点，研究在不同环境下不同模型的各种策略法则的性能，以及对策的优化解。这个结果为社会悖论的进化对策研究及其在社会经济方面的应用提供一种有效的研究手段。     

Evolutionary computation and games

Games provide competitive, dynamic environments that make ideal test beds for computational intelligence theories, architectures, and algorithms. Natural evolution can be considered to be a game in which the rewards for an organism that plays a good game of life are the propagation of its genetic material to its successors and its continued survival. In natural evolution, the fitness of an individual is defined with respect to its competitors and collaborators, as well as to the environment. Within the evolutionary computation (EC) literature, this is known as co-evolution and within this paradigm, expert game-playing strategies have been evolved without the need for human expertise.     

一种在多Agent系统中求帕累托效率解的方法

著名的Robert Axelrod实验证明了具备善意的、宽容的、强硬的和简单明了的算法将总会是赢家。基于这种思想,设计了PESCO算法。它可以在合作博弈中,面对合作的对手,寻求帕累托效率解,尽可能地达到双赢的局面。也可以在非合作博弈中,或对手不合作时,保证安全收益。以可合作的供零博弈、Stackelberg博弈和非合作的猜硬币博弈为背景,将PESCO算法与几个算法进行博弈,PESCO算法取得了较好的效果。     

Evolution of iterated prisoner's dilemma game strategies in structured demes under random pairing in game playing

We discuss the evolution of strategies in a spatial iterated prisoner's dilemma (IPD) game in which each player is located in a cell of a two-dimensional grid-world. Following the concept of structured demes, two neighborhood structures are used. One is for the interaction among players through the IPD game. A player in each cell plays against its neighbors defined by this neighborhood structure. The other is for mating strategies by genetic operations. A new strategy for a player is generated by genetic operations from a pair of parent strings, which are selected from its neighboring cells defined by the second neighborhood structure. After examining the effect of the two neighborhood structures on the evolution of cooperative behavior with standard pairing in game-playing, we introduce a random pairing scheme in which each player plays against a different randomly chosen neighbor at every round (i.e., every iteration) of the game. Through computer simulations, we demonstrate that small neighborhood structures facilitate the evolution of cooperative behavior under random pairing in game-playing.     

系统直和博弈模型下的合作演化

演化博弈论是生物进化论与博弈论结合产生的理论,已成为研究合作演化行为的有力工具.本文研究了基于系统直和博弈模型下的合作演化行为.首先,利用复制者方程分析了双人双策略及三策略对称博弈的演化动力学过程.然后,以石头剪刀布模型和雪堆模型为基础,采用矩阵直和构建系统直和博弈模型,并将所构造的直和矩阵转化为含参数的系统总支付矩阵.随后,说明了这种方法可推广到n个博弈的情形.最后,利用MATLAB对系统直和博弈模型进行仿真模拟,从系统整体的角度分析合作演化.仿真结果表明,混合之后的系统直和博弈较单一博弈而言,合作策略的占比明显增加,且整个系统稳定性更好.这种合作演化机制呈现了全局互惠.     

考虑排污权交易的有限理性企业博弈模型研究

在有限理性基础上建立寡头垄断博弈模型,将其引入到排污权市场中,同时考虑了生产成本、污染治理成本、排污权交易价格的影响,使该模型更符合实际。将不同理性层次,不同结构的非线性成本函数,不完全信息等因素引入到博弈模型中,对改进后的模型的演化过程进行分析,找出博弈均衡点,并分析其稳定性。由于具有有限理性的双寡头Cournot博弈模型会产生丰富的动力学行为,因此对其进行数据模拟后并对混沌现象进行分析。在此基础上,运用延迟反馈控制法对寡头垄断博弈模型的混沌控制进行了解析分析,结果表明选择合适的控制因子可使模型稳定在Nash均衡。     

AN EVOLUTIONARY STRATEGY FOR A COMPUTER TEAM GAME

Computer team games have attracted many players in recent years. Most of them are rule‐based systems because they are simple and easy to implement. However, they usually cause a game agent to be inflexible, and it may repeat a failure. Some studies investigated the learning of a single game agent, and its learning capability has been improved. However, each agent in a team is independent and it does not cooperate with others in a multiplayer game. This article explores an evolution strategy for a computer team game based on Quake III Arena. The Particle Swarm Optimization (PSO) algorithm will be applied to evolve a non‐player character (NPC) team in Quake III to be more efficient and intelligent. The evolution of a single NPC, which accommodates to its team and, moreover, the team has learning and cooperating abilities, will be discussed. An efficient team is composed of various members with their own specialties, and the leader is capable of evaluating the performance of a member and assigning it a proper job. Furthermore, the leader of an intelligent team will adapt a strategy appropriate for various circumstances and obtain the team's best performance. Instead of considering the tactic of an individual bot, this article takes the strategy of a team into account.