生态学视角下的集装箱海运网络适应度 BA 模型

集装箱海运网络主要反映的是港口与航线的空间组织,其演化是一种复杂的社会、经济、历史的整合过程。运用生态学的隐喻方法对其演化的动力机制进行剖析,充分考虑到现实集装箱海运网络的规模限制与动态发展的演化特征,提出基于“优胜劣汰”的五种机制：节点增加、节点删除、择优连接、反择优删除与重新连接;通过设计港口腹地经济、港口吞吐量、港口区位等适应性指标对港口节点适应度进行度量,构建出一个适应度BA模型;对港口节点度分布的理论推导与数值模拟结果表明：（1）该模型的度分布不仅取决于港口节点的度,更依赖于港口节点的适应度。（2）该模型能够得到一个度分布服从幂律指数为γ＝1＋1／β的无标度网络。（3）若每个港口节点取相同适应度时,该模型呈现出“先到者赢”的特征;若每个港口节点适应度并不完全相同时,该模型呈现出“适者更富”的特征。     

21世纪海上丝绸之路沿线港口体系演化研究——基于Logistics、Lotka-Volterra模型

港口体系演化研究揭示21世纪海上丝绸之路沿线港口的发展规律,为我国相关企业海外港口投资提供决策支持,进而为我国“海上丝绸之路”战略的具体实施提供理论依据。本文利用生态种群演化相关理论,选取东南亚港口为数据样本来研究港口体系演化过程,研究发现港口规模演化趋势符合Logistics演化规律,2008年时增长速度达到峰值,2013年海上丝绸之路沿线港口体系进入衰退期,2030年左右港口体系规模将达到极限值;建立双种群增长的Lotka-Volterra模型和多种群增长模型研究新加坡港、巴生港、柔佛、槟城、丹戎帕拉帕斯的相互关系和演化趋势,提出我国在与这些港口合作时需要依据这种关系和演化趋势去统筹协调,才能达到效益最大化。     

基于节点收益的档案合作涌现模型

针对档案作者演化网络中的合作涌现问题,提出了一个基于节点收益的复杂网络演化模型.网络的边根据收益优先的原则进行连接,并且每次博弈的收益都作为节点的一个属性,从而可以讨论节点的聚类情况.通过数据分析可知,当网络演化到一定程度后,模型可以促进合作策略的涌现.仿真结果表明,通过该方法演化的网络,具有明显的聚类特性.     

考虑CO2排放的中国港口动态网络效率研究

港口是一个多时期多流程组成的动态复杂网络系统。本文以2013~2016年中国10个主要沿海港口为研究对象,综合考虑经济、运营、环境等多种因素,将港口分为生产运营阶段和盈利转化阶段,并运用动态网络SBM模型进行港口效率评价,同时考虑了港口内部结构和跨期活动因素,通过结转指标将不同时期的港口结构连接成一个动态的整体系统,构建了动态的考虑港口相邻阶段间相互活动和相邻时期间跨期活动的非线性目标规划效率模型。不仅反映了港口效率在时间序列中的动态变化,而且揭示了港口内部的薄弱环节,完善了现有港口效率评价中忽略跨期活动和内部结构影响的不足。实证结果表明:中国港口的生产运营阶段效率普遍高于盈利转化阶段效率,动态网络综合效率低下与盈利转化阶段效率低有较大的关系,港口碳排放量仍然有很大的减排空间,动态网络SBM模型比动态SBM模型区分力更强,更适于港口效率评价。     

基于超图的突发灾害事件演化网络研究

突发灾害事件演化是灾害系统各要素相互作用的结果,从系统要素相互作用的视角剖析突发灾害事件网络演化机理,对阻断次生、衍生事件的发生,减少事件损失,提高应急处置效果具有重要意义.在分析了突发灾害事件网络演化机理基础上,基于超图理论构建了突发灾害事件演化网络模型,通过分析超图的拓扑特性对承灾体的关联性、暴露性以及事件的衍生性、危害性和重要性进行了评估,并基于超图生成的线图对灾害事件的关键演化链进行了描述,最后,以某城市燃气泄露爆炸事件为例进行了验证分析.研究表明,所构建模型既能对突发灾害事件演化网络进行结构化描述,又能对事件及承灾体的风险和重要性进行评估,为分析突发灾害事件网络演化提供了有力工具.     

基于Logistic模型的煤炭产业链系统演化过程研究

作为一个开放的、非线性并且远离平衡态的自组织系统,煤炭产业链在促进煤炭深加工、伴生资源的综合利用和实现煤炭产品附加值的提升方面具有重要作用.通过引入Logistic模型,建立产业链间具有差异性影响系数的煤炭产业链系统演化模型,通过分析各自演化方程组的特征矩阵及特征根,得出了完全独立、完全竞争、部分竞争共存以及部分竞争取代等4种不同的产业链系统演化形态.从而从系统演化的角度研究出了煤炭产业链系统的演化过程及相应机理,对于促进不同形态的煤炭产业链的健康发展具有重要的理论和现实意义.     

基于复杂网络上的演化博弈

论述了基于复杂网络的演化博弈.通过介绍相应的演化博弈模型-囚徒困境模型以及一些复杂网络,鉴于复杂网络的优点以及博弈演化的特性,将两者巧妙的结合起来,介绍了研究这类问题的一些方法.     

基于哈肯模型的电子商务系统演化机制研究——以家具电子商务为例的实证分析

电子商务系统是一个具有自组织特性的动态演化系统,通过探究其动态演化机制识别影响其演化的决定因素,分析其动态演化的规律,对把控中国电子商务产业持续发展的方向、促进电子商务产业的健康发展具有重要的研究意义.文章以家具电子商务系统为例,运用哈肯模型构建系统的演化方程,通过实证分析得出家具电子商务系统演化的关键变量,探索家具电子商务系统的演化机制.结果表明,反映子系统协调发展水平的家具制造业与生产性服务业耦合协调度是家具电子商务系统演化的决定因素;互联网发展水平在一定程度上通过提升子系统协调发展水平对家具电子商务系统演化起促进作用,但这种促进作用会随着时间的推移而逐渐减弱.家具电子商务系统演化机制研究为制定我国家具行业电子商务的发展政策提供了理论参考.     

基于混沌理论的创新网络中组织间信任演化研究

以往关于信任的研究是在稳定均衡的假设下进行的,然而信任演化过程中会表现出非线性的混沌状态,具有复杂系统的特征。基于演化博弈理论和混沌理论,建立了创新网络中组织间信任演化模型,分析了创新网络中组织间信任的复杂性、初值敏感性、分岔行为及内随机性等混沌特性,推导出信任演化方程与Logistic映射之间的关系,采用Lyapunov稳定性理论进行混沌性判定,证明创新网络中组织间信任通过倍周期分岔通往混沌,得到了信任从有序进入混沌的一般条件,运用算例进行仿真展示信任演化通往混沌的过程,分析创新网络中信任演化进入混沌区的实际意义,并选择硅谷和筑波科技城两个实例做对比分析,验证了该研究的实用性和有效性。创新网络中组织间信任的混沌演化反映出信任发展的非线性特点,为创新网络中组织间信任的混沌利用和控制提供理论指导。     

基于组织进入、退出和合作的技术创新网络演化模型

技术创新网络是企业、大学、研究机构等相关组织基于共同的技术创新目标而建立起来的一种网络组织形式,像其他生命体一样存在着萌芽、成长、成熟和衰退的生命周期.网络的演化与网络成员的行为密切相关,在分析组织进入网络、增加合作和退出网络机制的基础上构建了技术创新网络演化模型,研究不同演化阶段的特征.理论推导和仿真结果表明:技术创新网络整个生命周期中保持着无标度特性;组织进入网络、增加合作和退出网络的速度决定了技术创新网络的生命周期以及其所处生命周期的阶段.     

基于系统冲击的灰色预测模型及其应用研究

针对系统受到系统外部冲击问题,结合泛函理论和灰色系统理论,建立了含有系统冲击泛函分析因子的灰色泛函预测模型。并运用贝叶斯网络推理技术,建立了系统冲击与系统控制的灰色贝叶斯网络推理预测模型。所建模型可以分析基于系统冲击演化的泛函分析因子的动态推演问题。依据泛函分析因子的变动,可以预测与修正系统发展趋势。案例分析了2013年房地产经济受到新政策的冲击问题。由于房地产经济受到新政策冲击,使经济发展态势发生转变。根据房地产经济的当前时段信息,利用灰色贝叶斯网络推理预测模型对历史趋势进行修正,预测结果与实际数值仅有3.81%的偏离,预测结果较其它现有模型的预测结果精确。灰色贝叶斯网络推理模型强调对近期数据的开发利用,适用于预测系统近期受到外部冲击的发展趋势问题。     

Phase-type distributions in stochastic automata networks

Stochastic automata networks (Sans) are high-level formalisms for modeling very large and complex Markov chains in a compact and structured manner. To date, the exponential distribution has been the only distribution used to model the passage of time in the evolution of the different San components. In this paper we show how phase-type distributions may be incorporated into Sans thereby providing the wherewithal by which arbitrary distributions can be used which in turn leads to an improved ability for more accurately modeling numerous real phenomena.     

基于灰色预测模型的广州港货运发展研究

新冠肺炎疫情对广州港的货运发展产生了一定程度的影响,但目前仍然缺少相关的定量研究.基于灰色预测模型,利用港口货物吞吐量、外贸货物吞吐量、集装箱吞吐量三个货运指标,在新冠肺炎疫情没发生的这一假设下,推测2020年1-8月广州港的各货运发展指标值.基于预测值与实际值的比较,定量研究广州港的货运发展受新冠肺炎的影响值,反映新...     

THE ROLLING DEVELOPMENT MODEL OF ECOLOGICAL ECONOMY COMBINED WATER CONSERVANCY CONSTRUCTION WITH SCHISTOSOMIASIS CONTROL

Abstract. On the basis of classification of geographical properties a rolling development goal programming model is formulated in correspondence with the ecopattern group in Four-Lake Area. In this model, the control of schistosomiasis and strengthen-Lug of the drainage-storage function are both regarded as ecological measures during the process of multi-year(r0) ecological economic rolling development to make the ecoagrlcultural system in this area into a stably operating, ordered, and good cyclicsystem under the support of some controllable factors.     

Investment optimization on port’s development by fuzzy integer programming

In this research, the main purpose is to formulate a model to determine the optimum investment on port development from national investment prospective; on the other hand, costs and benefits are calculated from consumer and investor’s viewpoint. The formulated model is an integer-programming model. The emphasis is on how to formulate an investment optimization problem where cargo operation, investment costs, cargo-handling capacity, cargo transportation network, and the world maritime fleet constraints are included. Fuzzy numbers are used for cargo forecast study results. The output of the model is the type of design ships and design berths which are needed in each sub period, so that the port planner (the government) will find out the optimum development plan of port in each sub period when there is uncertainty in cargo handling forecast (fuzzy numbers).     

Measuring agility of networked organizational structures via network entropy and mutual information

While the agility of networked organizational structures is important for organizational performance, studies on how to evaluate it remain scant, probably because the difficulty in measuring network evolution. In this conceptual paper, we propose two measures - network entropy and mutual information - to characterize the agility of networked organizational structure. Rooted in graph theory and information theory, these two measures capture network evolution in a comprehensive and parsimonious way. They indicate the uncertainty (or disorder) at the network level as well as the degree distribution at the individual level. We also propose an algorithm for applying them in the scenario of adding links to a network while holding the number of nodes fixed. Both simulated and real networks are used for demonstration. Implications and areas for future research are discussed in the end.     

Stochastic kronecker graphs

A random graph model based on Kronecker products of probability matrices has been recently proposed as a generative model for large‐scale real‐world networks such as the web. This model simultaneously captures several well‐known properties of real‐world networks; in particular, it gives rise to a heavy‐tailed degree distribution, has a low diameter, and obeys the densification power law. Most properties of Kronecker products of graphs (such as connectivity and diameter) are only rigorously analyzed in the deterministic case. In this article, we study the basic properties of stochastic Kronecker products based on an initiator matrix of size two (which is the case that is shown to provide the best fit to many real‐world networks). We will show a phase transition for the emergence of the giant component and another phase transition for connectivity, and prove that such graphs have constant diameters beyond the connectivity threshold, but are not searchable using a decentralized algorithm. © 2011 Wiley Periodicals, Inc. Random Struct. Alg., 38, 453–466, 2011     

Dynamic programming—neural network real-time traffic adaptive signal control algorithm

In this paper, an “intelligent” isolated intersection control system was developed. The developed “intelligent” system makes “real time” decisions as to whether to extend (and how much) current green time. The model developed is based on the combination of the dynamic programming and neural networks. Many tests show that the outcome (the extension of the green time) of the proposed neural network is nearly equal to the best solution. Practically negligible CPU times were achieved, and were thus absolutely acceptable for the “real time” application of the developed algorithm.     

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.