审计中博弈模型的扩展分析

进一步研究审计中的博弈分析.在更为一般的条件下分别建立了审计机关与被审计部门之间的完全信息静态博弈模型和无限阶段重复博弈模型,得到审计机关最优混合策略及其对弄虚作假罚款系数的公式,探讨了影响局中人行为的若干因素,提出了加强审计工作的几点建议.     

乡村振兴战略下农村人居环境整治PPP模式合作行为演化博弈分析

农村人居环境整治是乡村振兴战略的重要内容,基于有限理性的演化博弈理论,构建了以地方政府、社会资本和农村居民为博弈主体的农村人居环境整治PPP模式合作行为演化博弈模型,运用Matlab软件分析了三方主体的初始意愿和政府规制对三方主体行为策略演化的影响。研究发现:(1)政府规制对农村人居环境整治PPP模式合作博弈系统演化的影响从大到小依次是:政府对社会资本的补贴资助、投机罚金、合作奖励和政府对农村居民的参与奖励。(2)与提高地方政府对社会资本的补贴资助力度和惩罚力度相比,提高地方政府的激励规制初始意愿更能促进农村人居环境整治PPP项目的顺利落地实施。(3)农村居民参与PPP项目的初始意愿的提升关键在于提高农村居民参与收益。最后,为促进PPP模式在农村环境治理领域的应用与发展和推动农村人居环境整治的可持续改善提出相应建议。     

基于不同网络层次的集群企业知识共享演化分析

基于不同网络层次嵌入视角,考虑集群内网络和超集群网络嵌入的不同情况,综合运用Hotelling博弈和演化博弈,建立集群企业与集群外部企业间知识共享决策与演化的动态模型,探讨在无政府补贴和有政府补贴条件下高技术与低技术集群企业间的博弈决策。研究发现,高技术集群企业会挤占低技术集群企业与集群外企业的知识共享,知识共享成本是影响集群内外企业知识共享的关键因素,政府可以引导集群内企业更多地与集群外企业进行知识共享。最后运用数值仿真对研究结论进行验证。     

团队规范下管制员违章行为演化与监管研究

为发现团队规范下管制员违章行为及监管策略的演化博弈规律,运用演化博弈理论,构建安全管理者与管制员演化博弈模型,分析博弈系统均衡点的局部稳定性。在此基础上建立演化博弈的系统动力学模型,仿真模拟不同情形下博弈双方策略选择的动态演化过程,并分析模型参数变化对系统演化结果的影响。结果表明:正的团队规范有助于系统演化至理想模式,负的团队规范会使系统陷入“不良锁定”模式和“震荡”模式;管制员行为收益、行为成本、被监查到的概率、处罚力度、监管成本等因素均对系统演化结果产生影响。鼓励班组成员对违章行为进行否定性评价、加大对管制员的处罚力度等均能够长效促进管制员主动遵章。     

云计算开源生态知识共享及风险治理的演化博弈研究

云计算开源生态正在逐步形成,促进知识共享、规避开源风险成为开源运营及开源治理的重要目标。本文基于演化博弈理论,构建了行业自律和政府监管两种治理机制下多方利益相关者(云计算企业、产业联盟以及政府)之间的演化博弈模型,分析了关键参数对各方策略及系统收益的影响。研究发现,在行业自律机制下,违约金和基金会补贴会促进知识共享合作,而产业联盟对于风险的存在则更为敏感。在政府监管机制下,罚款和政府补贴可以帮助参与者平衡风险并促进知识共享合作。研究结果可为云计算开源生态的可持续发展提供决策参考。     

不同渠道权力结构下的双渠道闭环供应链定价与协调决策

在双渠道环境下,针对不同渠道权力结构对闭环供应链最优决策的影响情况,构建了三种分散化(制造商主导的Stackelberg博弈、零售商主导的Stackelberg博弈以及由制造商和零售商构成的Nash均衡博弈)和一种集中化的闭环供应链决策模型,并得出了相关的均衡策略。在此基础上进一步有针对性的设计了两种契约相结合的契约机制协调供应链,并利用数值算例分析验证所得结论以及契约机制的有效性与可实践性,同时分析模型中的一些重要参数如渠道替代系数、传统渠道所占市场份额、废旧产品回收率、再制造成本节约等对双渠道供应链各项最优决策及渠道利润的影响。     

Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization

Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.     

基于吸收能力和知识透明性的知识联盟动态模型研究

在对知识联盟中知识转移和知识创造过程分析的基础上，本文构造了一个基于吸收能力和知识透明性的知识联盟动态博弈模型，分析了吸收能力和知识透明程度对参与组织决策和利润的重要作用。最后给出了进一步研究的方向。     

A new chaos optimization algorithm based on symmetrization and levelling approaches for global optimization

Chaos optimization algorithm is a recently developed method for global optimization based on chaos theory. It has many good features such as easy implementation, short execution time and robust mechanisms for escaping from local minima compared with existing stochastic searching algorithms. In the present paper, we propose a new chaos optimization algorithm (COA) approach called SLC (symmetric levelled chaos) based on new strategies including symmetrization and levelling: the proposed SLC method is, to our knowledge, the first chaos approach that can efficiently and successfully operates in higher-dimensional spaces. The proposed method is tested on a number of benchmark functions, and its performance comparisons are provided against previous COAs. The experiment results show that the proposed method has a marked improvement in performance over the classical COA approaches. Moreover, among all COA approaches, SLC is the only one to work efficiently in higher-dimensional spaces.     

Option pricing for large agents

This paper considers arbitrage-free option pricing in the presence of large agents. These large agents have a significant market power, and their trading strategies influence the dynamics of the financial asset prices. First, a simple asset pricing model in the presence of large agents is presented. Then a nonlinear partial differential equation is found for the prices of European options in the model. The unit option price depends on the large agent's asset holdings. Finally, a game model is introduced for the interaction between different market players. In this game, the outstanding number of options, as well as the option price, is found as a Nash equilibrium.     

Effort Games and the Price of Myopia

We consider Effort Games, a game‐theoretic model of cooperation in open environments, which is a variant of the principal‐agent problem from economic theory. In our multiagent domain, a common project depends on various tasks; carrying out certain subsets of the tasks completes the project successfully, while carrying out other subsets does not. The probability of carrying out a task is higher when the agent in charge of it exerts effort, at a certain cost for that agent. A central authority, called the principal, attempts to incentivize agents to exert effort, but can only reward agents based on the success of the entire project. We model this domain as a normal form game, where the payoffs for each strategy profile are defined based on the different probabilities of carrying out each task and on the boolean function that defines which task subsets complete the project, and which do not. We view this boolean function as a simple coalitional game, and call this game the underlying coalitional game. We suggest the Price of Myopia (PoM) as a measure of the influence the model of rationality has on the minimal payments the principal has to make in order to motivate the agents in such a domain to exert effort. We consider the computational complexity of testing whether exerting effort is a dominant strategy for an agent, and of finding a reward strategy for this domain, using either a dominant strategy equilibrium or using iterated elimination of dominated strategies. We show these problems are generally #P‐hard, and that they are at least as computationally hard as calculating the Banzhaf power index in the underlying coalitional game. We also show that in a certain restricted domain, where the underlying coalitional game is a weighted voting game with certain properties, it is possible to solve all of the above problems in polynomial time. We give bounds on PoM in weighted voting effort games, and provide simulation results regarding PoM in another restricted class of effort games, namely effort games played over Series‐Parallel Graphs (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

“合作”还是“寄生”？考虑政府规制的众创空间创业生态系统共生机制研究

共生机制是推进众创空间创业生态系统螺旋式上升发展的关键,基于共生理论构建了以创业企业、利益相关者和政府为主体的众创空间创业生态系统三方共生行为策略演化博弈模型,并通过数值模拟分析探究了各主体不同行为策略的影响因素及演化路径。研究结果表明:创业企业、利益相关者以及政府选择合作的初始意愿对彼此行为的影响程度有所差异;相较于利益相关者,创业企业对政府的补贴政策和惩罚力度更为敏感;制定合理的收益分配机制能够促进系统向互利共生方向演化。     

基于熵值Topsis和博弈动力学的空港联盟决策研究

针对空港在面临联盟选择时的决策问题进行了研究,认为众空港在博弈中的收益为不等值,从区域发展、生产要素、需求状况、支撑产业和环境影响五方面建立空港联盟收益的指标体系,将空港网络抽象成小世界网络,结合熵值Topsis和小世界网络博弈动力学建立空港联盟决策算法,并且运用2010年我国25大空港的实证数据进行研究。结论显示空港联盟只能解决空港发展的一时问题,提升自身能力才是重中之重,研究结论具有实际应用意义。     

情绪影响下选时博弈模型及其最优策略

依据Quiggin的秩依期望效用理论研究经典选时博弈问题。通过引入可以刻画局中人在博弈中情绪状态的非线性决策权重函数,将RDEU有限策略博弈扩展到连续博弈,构建了RDEU选时博弈模型。基于Riccati微分方程的解法,求出博弈模型中局中人的最优策略。最后,通过数值仿真,分析了不同情绪状态对局中人博弈决策行为的影响。研究发现,情绪对混合策略意义下的局中人最优策略有着显著的影响,在乐观情绪状态下,局中人对混合策略极易产生自信和较高的信任倾向,表现出"风险爱好者"行为;在悲观情绪状态下,局中人往往对混合策略缺乏自信和信任,表现出“风险厌恶者”行为。     

乳制品安全监管中的多阶段进化博弈分析

乳制品安全风险存在于生产与组织的多个阶段。在各个阶段中,政府监管部门虽统一监管,但由于采取的监管策略不同,使其与厂商之间的博弈情景存在差异,这将影响风险控制的成果。为探索多阶段监管的有效途径,找寻降低监管风险的方法,本文分析了乳制品安全监管中的多阶段进化博弈。首先阐述多阶段厂商监管的相关组织关系,构建进化博弈模型;其次基于模型进行进化博弈分析,分别得出了不同监管情景下的演化稳定策略,识别了多阶段策略选择的稳定条件;最后归纳了进化博弈分析结果,通过模拟仿真以及案例分析加以验证。     

Dynamics of competing with quality- and advertising-based goodwill

Goodwill formation is a complex process and many factors influence the formation of goodwill of a firm. The implications of advertising enabled goodwill formation are reported in several articles in the research literature. In this paper, we extend this stream of research by including quality in the goodwill formation process. We adopt a dynamic model of competition utilizing a differential game approach and derive expressions for open-loop Markovian Nash equilibrium investments in advertising and quality. The insights gained from the analysis of our model and from the equilibrium solutions are presented in the form of research propositions.     

Coordination and Cooperation in Dyadic Relationships

A Markov model with a coordination parameter isdeveloped to depict coordination and cooperation decisionprocesses in 2 × 2 game relationships. The parameters ofthe model can be used to define and measure various forms ofinfluence. The model is applied to data collected using aprisoner's dilemma payoff structure to represent thesuperior-subordinate relationship. The results demonstrate theusefulness of the model and the measures of influence. Superiorsand subordinates are more likely to coordinate following mutualcooperation, achieving matching behaviors more frequently thanan independent-decisions model would predict. Subordinates showslightly more influence over leaders' behaviors than vice versa.     

下游风险约束下的供应链博弈与合作研究

考虑了由风险中立的单供应商和风险规避的单零售商组成的二级供应链,分别建立了分散决策下和回购契约下供应商占主导地位的Stackelberg博弈模型,求得了均衡解.并研究了零售商下游风险参数值对博弈决策的影响,研究发现当零售商期望目标利润较大时,风险约束下的回购契约机制将无效,且期望目标利润越大则供应商和零售商利润越小,最后用算例对这一结论进行了说明.     

Analysis of decision-making in economic chaos control

In some economic chaotic systems, players are concerned about whether their performance is improved besides taking some methods to control chaos. In the face of chaos occurring in competition, whether one player takes controlling measures or not affects not only their own earning but also other opponents’ income. An output duopoly competing evolution model with bounded rationality is introduced in this paper. Using modern game theory, decision-making analyses about chaos control of the model are taken by taking aggregate profits as players’ payoff. It is found that the speed of players’ response to the market and whether the decisive parameters are in the stable region of the Nash equilibrium or not have a distinct influence on the results of the game. The impact of cost function’ type on results of the game is also found. The mechanism of influences is discovered by using numerical simulation.     

考虑情感的三策略囚徒困境博弈模型与合作演化

基于社会比较情境下强者和弱者之间产生的同情、欺凌、漠然、尊敬、嫉妒和畏惧等6种情感,建立个体情感特征的定量表达方法。考虑情感驱动的三策略囚徒困境博弈模型,设计了6种情感与合作、背叛及孤独3个博弈策略之间的对应关系和定量表达机制。采用二维格子网络、随机网络和无标度网络等三种网络模型进行演化博弈仿真分析。结果显示:较大的背叛成功诱惑促进了采取背叛策略的情感类型发展演化和繁荣稳定,系统的最终演化状态都体现出程度不同的负性情感特征;空间网络结构对情感类型和合作行为的演化存在重要影响,无标度网络结构能够导致具有一定良性特征的情感类型演化稳定,并带来较高的合作水平;从社会比较及由此产生的对比效应和同化效应的角度,对三种网络下不同的情感演化状态进行了相应阐释。