市场导向、多主体协同与创新扩散:基于复杂网络的动态仿真

市场导向与多主体协同关系密切,是提升创新扩散效率的主要驱动要素。本文首先从协同关系和创新收益两个层面,构建空间结构和有效预期的演化机制,生成复杂网络模型,对多主体协同的创新扩散过程进行动态仿真。其次通过细化市场导向理论在用户需求、竞争驱动和职能协同等不同维度的作用路径,深入分析了市场导向对多主体协同的影响机制。研究表明:(1)市场导向对多主体协同的影响与网络结构动态特征具有高度相关性;(2)少量的用户需求与竞争驱动导向对多主体协同的效益提升最为显著,职能协同导向的影响则呈现周期性“倒U型”波动特征;(3)用户需求导向对多主体协同的创新收益驱动效应最明显,竞争驱动导向的推动效果次之。     

基于社会网络结构的创新扩散动力机制及其仿真研究

随着社会资本的大量涌入,创新扩散逐渐受到社会网络关系的影响。在分析了创新扩散机理的基础上,构建了基于不同拓扑结构的创新扩散演化动力模型。将信息获取、领导者创新能力及机会利益作为创新扩散的动力因子。通过利用复杂网络的演化博弈仿真分析,揭示了小世界、无标度等不同网络拓扑结构下,创新技术的扩散情况。仿真结果表明:在网络结构相同的情况下,信息获取对创新扩散的影响较大;在动力因子设定相同的情况下,网络主体连接越规则,创新扩散越充分。     

实践社群知识扩散模型及仿真研究

实践社群知识扩散的效率和效果影响实践社群的创新能力。通过考虑实践社群成员知识异质性对知识扩散的影响,引入知识生态位的观点,比较实践社群成员知识生态位的重叠程度,进而构建实践社群知识扩散模型,考虑实践社群成员间的相互作用,对知识扩散模型进行求解,得到相应的稳定均衡点并利用相图分析法进行验证,最后利用MATLAB进行数值仿真研究。结果表明:实践社群成员知识生态位的重叠程度对知识扩散效果有重要影响,实践社群成员间影响程度的大小对实践社群知识扩散模型均衡点有显著影响,实践社群成员知识表现为知识互补时,实践社群知识扩散的效率与效果最优。研究结果为促进实践社群知识扩散提供理论依据。     

面向集群创新网络异质企业的知识扩散建模及仿真研究

基于合作的集群创新网络知识扩散已经成为企业实现知识创新的重要手段,而集群创新网络知识扩散的动力学过程强烈依赖于异质企业间知识扩散能力的影响,为此,本文综合考虑了企业间不同接触数量、知识吸收和传播能力、知识淘汰率等异质性因素,建立了基于传染病理论的知识扩散模型,验证了由各异质因素构成的知识扩散再生数对知识扩散均衡和扩散效果的影响,并结合仿真实验进一步得出:在知识扩散前期,集群创新网络应发挥hub节点及异质网络的优势加快知识扩散,在中后期应注意企业关系发展的均衡性及企业接触邻居的规模性;相比过于强调知识交流的广泛性,加强企业传播能力和吸收能力的培养对网络知识扩散效果的提升更具意义;随着时间演化,企业知识淘汰率也会影响网络知识扩散的收敛情况。     

负面口碑、社会互动与创新扩散:基于小世界网络的仿真

为探究创新扩散失败的原因及作用机理,在创新扩散微观模型中引入负面口碑,建立个体在社会互动影响下的决策模型,并进行小世界网络中的多智能体仿真分析,研究网络结构、抵抗领袖比例、意见领袖创新性和社会规范约束力对创新扩散的影响。结果表明,考虑负面口碑的创新扩散曲线呈“S”形变化,但扩散深度受限;高度聚集的社会网络更有利于创新扩散。抵抗领袖比例越高,创新扩散速度和深度越小,且负面口碑作用范围越大;当抵抗领袖比例高于意见领袖比例时则会导致扩散失败。意见领袖创新性的提高可以缓解负面口碑的消极影响并促进创新扩散。社会规范约束力对创新扩散深度的影响随网络结构变化呈现不同态势。研究不但丰富了现有创新扩散理论,而且对开发创新推广策略具有指导意义。     

Approximation of an Analog Diffusion Network with Applications to Image Estimation

This work is concerned with a numerical procedure for approximating an analog diffusion network. The key idea is to take advantage of the separable feature of the noise for the diffusion machine and use a parallel processing method to develop recursive algorithms. The asymptotic properties are studied. The main result of this paper is to establish the convergence of a continuous-time interpolation of the discrete-time algorithm to that of the analog diffusion network via weak convergence methods. The parallel processing feature of the network makes it attractive for solving large-scale optimization problems. Applications to image estimation are considered. Not only is this algorithm useful for the image estimation problems, but it is widely applicable to many related optimization problems.     

A Multilevel Event History Model of Social Diffusion: Medical Innovation Revisited

This article presents a multilevel event history model of social diffusion and applies it to Coleman, Katz, and Menzel's (1966 Coleman , J. S. , Katz , E. , &; Menzel , H. ( 1966 ). Medical Innovation: A Diffusion Study . Indianapolis , IN : Bobbs-Merrill . [Google Scholar]) data on the adoption of tetracycline by physicians. The simplest form of a multilevel model allows a random intercept. In the present application of this simple model to the Medical Innovation data, structured for an event history analysis, the physicians are nested in city and time. Random intercepts capture effects of contextual conditions that are shared by event history cases with the same city–time status. The intercepts also reflect any baseline internal contagion effects, that is, the proportion of physicians in the city–time network who have adopted the drug at time t ? 1. Here, I show that Van den Bulte and Lilien's (2001 Van den Bulte , C. &; Lilien , G. L. ( 2001 ). Medical innovation revisited: social contagion versus marketing effort . American Journal of Sociology , 106 , 1409 – 1435 .[Crossref], [Web of Science ®] , [Google Scholar]) finding of an important contextual effect of drug firms' marketing effort is misleading. I also show that the social network in which physicians are situated significantly contributes to their adoptions, controlling for baseline internal contagion effects and individual-level characteristics of physicians, which have been emphasized in investigations of these data.     

具有扩散影响的Hopfield型神经网络的全局渐近稳定性

对具有扩散影响的Hopfield型神经网络平衡点的存在唯一性和全局渐近稳定性进行了研究．在激活函数单调非减、可微且关联矩阵和Liapunov对角稳定矩阵有关时,利用拓扑度理论得到了系统平衡点存在的充分条件．通过构造适当的平均Liapunov函数,分析了系统平衡点的全局渐近稳定性．所得结论表明系统的平衡点(如果存在)是全局渐近稳定的而且也蕴含着系统的平衡点的唯一性．     

网络结构、采纳者偏好与创新扩散:基于采纳者决策过程的创新扩散系统动力学模型仿真分析

在对采纳者决策过程分析的基础上,将网络结构和采纳者偏好作为核心参数,构建基于采纳者决策过程的创新扩散系统动力学模型。对模型进行仿真发现,在采纳者趋同化偏好条件下,网络平均度、网络重连概率与采纳者偏好强度的变动趋势与创新扩散效率的变动趋势相同,而在采纳者差异化偏好条件下则与创新扩散效率变动趋势相反。网络平均路径长度对创新扩散的影响方向与采纳者偏好特征无关,提高网络平均路径长度会始终降低创新扩散的效率。采纳者的趋同化偏好能够放大创新扩散对网络结构变量与采纳者偏好强度变量的敏感程度,采纳者差异化偏好则会缩小创新扩散对网络结构变量与采纳者偏好强度变量的敏感程度。研究结果对于制定创新推广策略具有一定的指导意义。     

二维非均匀信息扩散及其应用

在风险分析和评估过程中,经常会遇到信息不充分、数据不完备的情况,即小样本问题.本文提出了二维的非均匀信息扩散方法,对小样本进行分析处理,并用于实际项目加以印证.其中非均匀信息扩散方程取(N)/(t)=(N 1)·((~2N)/(x~2) (~2N)/(y~2)),用MacCormack方法进行求解,在两个优化准则下建立优化模型,利用MATLAB编程实现,结果与大样本是一致的.     

煤炭码头卸车调度问题多目标优化模型及算法

本文针对煤炭码头卸车调度问题,提出了相应的多约束多目标优化模型,并设计了采用仿真推演策略解码的遗传算法求解。首先,本文考虑列车、煤种、场存、设备、翻堆线和卸车作业过程等约束条件,以卸车效率最大和列车在港时间最短为目标,构建了煤炭码头卸车调度问题多目标数学模型。然后,综合运筹学、遗传算法以及仿真技术,给出了煤炭码头卸车调度问题遗传算法详细设计,包括组合式编码和仿真推演解码方法,染色体生成算法,适应度函数设计,以及采用多种策略的遗传操作及修正等,并列出了算法步骤。实例测试表明,本算法的执行效率高而且优化效果好,结果适用。     

Airline Crew Scheduling: State-of-the-Art

The airline industry is faced with some of the largest scheduling problems of any industry. The crew scheduling problem involves the optimal allocation of crews to flights. Over the last two decades the magnitude and complexity of crew scheduling problems have grown enormously and airlines are relying more on automated mathematical procedures as a practical necessity. In this paper we survey different approaches studied and discuss the state-of-the-art in solution methodology for the airline crew scheduling problem. We conclude with a discussion about promising areas for further work to make it possible to get very good solutions for the crew scheduling problem.     

Multifunction Radar: Task Scheduling

Multifunction radar has to perform several types of tasks. Some of them are systematic: the case of probing and guidance tasks. Others are random: the case when a new object is detected in the sky and should be confirmed and tracked. The tasks that consist of tracking targets are also random, since these targets may follow unexpected trajectories, possibly asking for a search in the neighborhood of the forecasted position of the target. The goal of this paper is to propose a strategy that maximizes the number of useful tasks performed, taking into account their priorities. The more efficient the radar, i.e. the higher the quality of the information provided, the greater the energy consumption, which leads to a reduction of the number of feasible tasks. Thus, the goal is to find a trade-off between energy consumption and the number of tasks to perform by unit of time that maximizes the quantity of useful information.     

面向多检查的门诊患者调度研究

及时的检查对于患者病情诊断和治疗非常重要。然而,患者不同的紧急程度、检查项目的多样性、以及患者的行为因素如失约等,使门诊患者调度问题难以求解。为解决该问题,本文考虑患者对两种检查项目的不同需求,两类不同的紧急程度,以及患者失约和医生加班,建立了有限时域马尔可夫决策过程(MDP)模型,目标是使得患者检查所得的期望收益最大化以及期望加班时间惩罚成本最小化。由于MDP模型复杂,难以用解析方法来分析最优控制策略,因此本文基于MDP模型进行数值实验,观察最优解的结构特征,进一步构造了两种参数化启发式调度策略,并采用遗传算法对调度策略的参数进行优化。数值实验比较了最优控制策略、两种启发式调度策略以及先到先服务规则,实验结果表明本文所提的调度策略性能偏离最优解不超过10%;当工作负荷非常大时,启发式调度策略远远优于先到先服务规则。     

混沌神经网络优化算法用于求解无等待流水调度问题的最小最大完工时间

利用李小平等提出的相邻工件加工结束时间差矩阵,将求解无等待流水调度问题的最小最大完工时间(Makespan)问题映射为TSP问题,构造对应的能量函数,进而得到随机混沌神经网络(SCSA)算法.实验结果证明该混沌神经网络优化算法优于RAJ算法和GANRAJ算法.     

The Maximum Principle for Time-Fractional Diffusion Equations and Its Application

Using an equivalent reformulation of the definition of the Caputo fractional derivative, we present the stability and convergence analysis for a finite difference scheme that we use to solve a time-fractional diffusion equation. The analysis is based on a maximum principle for such equations.     

扩散速度与组元的质量守恒方程*

在混合物流动中,某组元i的质量迁移速度(绝对速度)等于对流速度(牵连速度)与扩散速度(相对速度)之和.扩散速度——以及扩散系数——依对流速度取法之不同而不同.     

水质中放射性核素瞬态三维扩散模型的建立及求解

在一维扩散模型的基础上,建立了放射性核素在水质中的瞬态三维扩散模型,并给出了瞬态排放模式下模型的计算公式,实现了水质中放射性核素扩散浓度的快速估算.在MATLAB程序下,利用该方法对一个实例进行了仿真分析,得出相关结论,对于核泄漏事故下放射性核素扩散浓度预测评估及制定安全措施具有指导意义.     

Like family: Network coevolution and the emergence of altruism

Altruism is difficult to explain in an evolutionary context—the mechanisms of selection appear to favor selfishness. Existing scholarly work addressing this puzzle relies on structural and behavioral assumptions that severely limit generalizability. The model presented in this article circumvents the need for such assumptions by incorporating an evolving network component to natural selection. Through a process of coevolution of individual and relational traits, many of the exogenous assumptions of previous models of the evolution of cooperation are realized endogenously within a simulated population. Such endogeneity allows a more precise examination of conditions under which cooperation arises, but also a clearer understanding of how those conditions themselves arise. The model is specified analytically, and interpretation is carried out on simulated outcomes.